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Pharmacotherapy in frail elderlyDijk, Karen Nanette van
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Pharmacotherapy in f ra i l e lder ly :
Pharmacy data as a tool for improvement
3
ISBN-nummer: 90-367-1639-x
©2002, K.N. van Dijk
All rights reserved. No part of this book may be reproduced in any manner or by any means
without written permission from the author.
This research was financially supported by the Scientific Institute Dutch Pharmacists
(WINAp). Publication of this thesis was kindly sponsored by the Royal Dutch Association for
the Advancement of Pharmacy (KNMP) and the Groningen Universtity Institute for Drug
Exploration (GUIDE).
Several sections of this thesis are based on published papers, which are reproduced with p
ermission of the co-authors and the publishers. Copyright of these papers remains with the
publishers.
Vormgeving en lay-out: Robbin van Nek, Vos & Libert reclame, Leeuwarden
2
Rijksuniversiteit Groningen
Pharmacotherapy in f ra i l e lder ly :
Pharmacy data as a tool for improvement
Proefschrift
ter verkrijging van het doctoraat in de
Wiskunde en Natuurwetenschappen
aan de Rijksuniversiteit Groningen
op gezag van de
Rector Magnificus, dr. D.F.J. Bosscher,
in het openbaar te verdedigen op
vrijdag 7 juni 2002
om 14.15 uur
door
Karen Nanette van Dijk
geboren op 23 oktober 1966
te ‘s-Gravenhage
5Paranimfen: Petra de Boer-Stoter
Karen Cromheecke-Berghuis4
7Promotores: Prof. dr. L.T.W. de Jong- van den Berg
Prof. dr. J.R.B.J. Brouwers
Referent: Dr. C.S. de Vries
Beoordelingscommissie: Prof. dr. A.C.G. Egberts
Prof. dr. F.M. Haaijer-Ruskamp
Prof. dr. J.P.J. Slaets
6 Rien n’est simple,
tout est possible
Page
Chapter 3 Risk assessment studies in the elderly 115
3.1 Constipation as an adverse effect of drug use in nursing home patients: an overestimated risk 117
3.2 Potential interaction between acenoucoumarol and diclofenac, naproxen and ibuprofen and therole of CYP2C9 genotype 133
Chapter 4 General discussion and perspectives 147
Summary 159
Samenvatting 164
Dankwoord 169
Publications 172
Curriculum vitae 173
9
Contents
Page
Chapter 1 Scope, objective and setting 11
1.1 Scope and objective 13
1.2 Setting 18
Chapter 2 Drug util isation studies in the elderly 23
Part one: nurs ing home res idents
2.1 Background 25
2.2 Use of hospital pharmacy data in pharmaco-epidemiologic research in nursing homes 31
2.3 Drug utilisation in Dutch nursing homes 44
2.4 Occurrence of potential drug-drug interactions in nursing home residents 60
2.5 Prescribing indicators as a tool to evaluate drug use in nursing homes: a pilot study 76
Part two : e lder ly outpat ients
2.6 Concomitant prescribing of benzodiazepines during antidepressant therapy in the elderly 90
2.7 Prescribing of gastroprotective drugs among elderly NSAID users in the Netherlands 103
8
Chapter 1
Scope, ob ject ive and sett ing
1110
1.1 Scope and ob ject ive
Introduct ion
Drug utilisation in the elderly has been subject of many studies [1-4]. In the Netherlands,
people aged 65 and over comprise about 14% of the Dutch population and account for 40% of
the drug prescription costs spent in hospital and community pharmacies [5]. Multiple chronic
conditions and the fact that the possibilities for both preventive and therapeutic medical the-
rapies for many diseases have increased in recent years, contribute to the high frequency of
drug use in the elderly. The prescribing of a drug to counteract adverse effects of another drug
(‘prescribing cascade’) may further increase drug use in the elderly [6].
In view of multiple co-morbidity, changes in drug kinetics and effects and the prescription
of several drugs simultaneously (polypharmacy), elderly people are at an increased risk of
drug-related problems such as drug-drug interactions, drug-disease interactions and adverse
drug reactions (ADRs) [7]. The prevalence of ADRs ranges from 1.5 to 44% in elderly inpatients
and from 2.5 to 50.6% in elderly outpatients [8]. Examples of age-related risks of ADRs are
bleeding from oral anticoagulants and gastropathy associated with non-steroidal anti-inflam-
matory drugs [9]. Apart from the risk of overprescribing in this group [6,10], underprescribing
of effective agents, such as statins, may also be harmful to the elderly [11-13]. Also, underdiag-
nosing of certain diagnoses, such as depression, is reported to be an issue in the elderly [14].
Despite the awareness of the problems associated with drug use in the elderly and the atten-
tion that has been given to rational prescribing, the frequency of drug-related hospital admis-
sions among elderly people aged 65 and over is still considerable [15-17]. The incidence of drug-
related problems is reported to be even higher in nursing home patients, due to the higher
levels of drug use and the complexity of the conditions these patients are cared for [18,19].
Physicians are faced with a complex task when prescribing to elderly people [20-22].
Advanced age leads to increased frailty and altered pharmacokinetics and pharmacodynamics
with large interindividual variability, often leading to unpredictable drug responses [23].
Elderly people are mostly not included in randomised clinical trials, both because of age and
co-morbidity [24], and as a result information on efficacy, optimum drug dosages and toxicity
are frequently lacking in this vulnerable age group [25,26]. Also, in daily clinical practice the
circumstances, under which drugs are used, especially in the elderly, differ from those in ran-
domised clinical trials. In view of the considerations mentioned above, prescribers need a tho-
rough understanding of the risks, benefits and consequences of drug therapy in the elderly,
13
Out l ine
In this introductory chapter, the objective and contents of the thesis are outlined in section
1.1. Section 1.2 gives an overview of the setting in which the studies presented in this thesis
were carried out. A description is given of both the medical and pharmaceutical setting for
ambulatory elderly and nursing home residents aged 65 and over in the Netherlands.
12
outline of the thesis are given. Furthermore, background information is given into the setting
of the studies presented in this thesis. Chapter 2 describes drug use and determinants of drug
use in nursing home patients and elderly outpatients. A description is given of the pharmacy
prescription data that were collected from 6 nursing homes. These data comprised the main
dataset used throughout this thesis. Several drug utilisation studies were performed with this
dataset. Prescribing indicators were used with the aim to study drug use systematically.
Furthermore, using prescription data from the InterAction database, we investigated drug use
in elderly outpatients, focusing on psychotropics and non-steroidal anti-inflammatory drugs.
Chapter 3 describes studies that focus on the outcome of drug use in both nursing home
patients and elderly outpatients. In chapter 4, the main findings of the studies are discussed
and suggestions for clinical practice are given.
15
especially the frail elderly. By studying the uses and effects of drugs in this population in daily
clinical practice such insight into these matters can be obtained. In particular in nursing homes,
where frail elderly people reside with often high levels of drug use, and where adverse drug
effects may lead to serious clinical consequences, it is useful to perform pharmacoepidemiolo-
gic studies. In the United States many such studies have been carried out, addressing three
main issues: the measurement, determinants, and outcomes of drug use. The structure and
organisation of Dutch nursing homes and the type of residents that is cared for, differ conside-
rably from those in other European countries and the United States [27]. As a consequence, the
concern that has been expressed in the United States regarding the excessive and inapprop-
riate use of drugs in nursing homes, especially psychotropics [28], cannot automatically be
applied to Dutch nursing homes.
Relatively few drug utilisation studies in Dutch nursing homes have been carried out.
Several reasons may account for this fact. First, the medical speciality ‘nursing home medicine’
is the youngest of the 34 medical specialities in the Netherlands and has been acknowledged
as an official medical speciality since 1990. In view of this short history, the extent of research
in this speciality is relatively small. Second, the nursing home population in the Netherlands
mainly consists of frail elderly patients. In this group ethical aspects, although this is not typi-
cally a Dutch issue, therefore may play a role. Ethical and practical questions arise when the
value of certain medical interventions is assessed, such as the withdrawal of benzodiazepines.
Furthermore, studies on clinical relevant outcomes are often lacking due to ethical considera-
tions. A third reason why studies on drug use in Dutch nursing homes have not been performed
more extensively, is that until the 1990s medication use on individual patient level was only
registered in the medical chart and not in pharmacy computer systems. Only in the last decade,
medication registration on individual patient level in computerised systems is more common.
Performing pharmacoepidemiologic studies, which often requires large datasets, has therefore
not been feasible until recent years.
Object ive and out l ine of the thes is
The objective of this thesis is to provide insight in drug use, determinants of drug use and
outcomes of drug use in the elderly and in particular in the frail elderly that reside in Dutch nur-
sing homes. The studies that provide this information may lead to a better understanding of the
risks of drug use in the (institutionalised) elderly and may serve as a starting point to improve
prescribing practices. Furthermore, the results of these investigations may provide the tool for
monitoring individual patients at risk for drug-related problems. In chapter 1 the objective and
14
27 Ribbe MW. Care for the elderly: the role of the nursing home in the Dutch health care system. Int Psychogeriatr
1993; 5: 213-22.
28 Beers MH, Ouslander JG, Fingold SF, Morgenstern H, Reuben DB, Rogers W et al. Inappropriate medication pre-
scribing in skilled-nursing facilities. Ann Intern Med 1992; 151: 1825-31.
17
References
1 Heerdink ER. Clustering of drug use in the elderly: population-based studies into prevalence and outcomes
[Thesis]. University of Utrecht, 1995.
2 Lau HS. Drug related problems in the elderly: studies on occurrence and interventions [Thesis]. University of
Utrecht, 1998.
3 Veehof LJG. Polypharmacy in the elderly [Thesis]. University of Groningen, 1999.
4 Avorn J. Gurwitz JH. Drug use in the nursing home. Ann Intern Med 1995; 123: 195-204.
5 Tinke JL. Farmacie in cijfers (SFK): Geneesmiddelgebruik door ouderen. Pharm Weekbl 2001; 136: 589.
6 Rochon PA, Gurwitz JH. Optimising drug treatment for elderly people: the prescribing cascade.
BMJ 1997; 315: 1096-9.
7 Van den Bemt PMLA, Egberts ACG, De Jong-van den Berg LTW, Brouwers JRBJ. Drug related problems in
hospitalised patients: a review. Drug Saf 2000; 22: 321-33.
8 Hanlon JT, Maher RL, Lindblad CI, Ruby CM, Twersky J, Cohen HJ, Schmader KE. Comparison of methods for
detecting potential adverse drug events in frail elderly inpatients and outpatients. Am J Health-Syst Pharm
2001; 58: 1622-6.
9 Beyth RJ, Shorr RI. Epidemiology of adverse drug reactions in the elderly by drug class.
Drugs Aging 1999; 14: 231-9.
10 Gurwitz JH, Avorn J. The ambiguous relation between aging and adverse drug reactions.
Ann Intern Med 1991; 114: 956-66.
11 Rochon PA, Gurwitz JH. Prescribing for seniors. Neither too much, nor too little. JAMA 1999; 282: 113-5.
12 Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical
diseases. N Engl J Med 1998; 338: 1516-20.
13 Avorn J. Improving drug use in elderly patients: getting to the next level. JAMA 2001; 22: 2866-8.
14 NIH Consensus Conference. Diagnosis and treatment of depression in late life. JAMA 1992; 268: 1018-24.
15 Atkin PA, Veitch PC, Veitch EM, Ogle SJ. The epidemiology of serious adverse drug reactions among the elderly.
Drugs Aging 1999; 14: 141-52.
16 Van Kraaij DJW, Haagsma CJ, Go IH, Gribnau FWJ. Drug use and adverse drug reactions in 105 elderly patients
admitted to a general medical ward. Neth J Med 1994; 44: 166-73.
17 Bero LA, Lipton HL, Bird JA. Characterization of geriatric drug-related hospital readmissions.
Med Care 1991; 29: 989-1003.
18 Seppälä M, Sourander L. A practical guide to prescribing in nursing homes. Avoiding the pitfalls. Drugs Aging
1995; 6: 426-35.
19 Monette J, Gurwitz JH, Avorn J. Epidemiology of adverse drug events in the nursing home setting. Drugs Aging
1995; 7: 203-11.
20 Beers MH, Ouslander JG. Risk factors in geriatric drug prescribing. A practical guide to avoid problems.
Drugs 1989; 37: 105-112.
21 Hughes SG. Prescribing for the elderly: why do we need to exercise caution? Br J Clin Pharmacol 1998; 46: 531-3.
22 Denham MJ, Barnett NL. Drug therapy and the older person. Role of the pharmacist. Drug Saf 1998; 19: 243-50.
23 Kinirons MT, Crome P. Clinical pharmacokinetic considerations in the elderly. An update. Clin Pharmacokinet
1997; 33: 302-12.
24 Zhan C, Sangl J, Bierman AS, Miller MR, Friedman B, Wickizer SW, Meyer GS. Potentially inappropriate medica-
tion use in the community-dwelling elderly. JAMA 2001; 22: 2823-9.
25 Turnheim K. Drug dosage in the elderly. Is it rational? Drugs Aging 1998; 13: 357-79
26 Avorn J. Including elderly people in clinical trials. BMJ 1997; 315: 1033-4.
16
made services for elderly people in need of continuous medical care, such as a daily need of
dressing renewals or continuous pain relief. When the need for medical care becomes such that
people cannot live totally in their own home anymore, admission into residential homes or
nursing homes is possible on strict medical indication and depending on the amount of care
needed. Regional pre-selection boards carry out assessment of eligibility for care in nursing
homes and residential homes. However, no measuring instrument has been able to assess the
need for care objectively and in a standardised way that is universally applicable [6].
Inst i tut ional ised e lder ly
In the Netherlands, of the people aged 65 and over 5.5% live in residential homes and
2.7% live in nursing homes [1]. Residential homes offer board, lodging and care to elderly who
are no longer able to cope on their own. These homes do not offer nursing. Medical care is pro-
vided by the residents’ general practitioner and medication is provided by the residents’ com-
munity pharmacy. There are 1425 residential homes in the Netherlands, with a total of 117,500
beds (55 per 1000 inhabitants aged 65 and over) [1]. The Dutch nursing home is a healthcare
institution for chronically ill persons in need of permanent medical and paramedical attention
and complex nursing care and is compared to skilled nursing facilities in the US [1]. The type of
care can be characterised as continuous, long-term, systematic and multidisciplinary [7].
Several features make them different from nursing homes in other countries. First, clear crite-
ria based on medical diagnoses, activities of daily living, behavioural characteristics and men-
tal functioning for nursing home admission exist. On the basis of these criteria, a distinction is
made between eligibility for admission and whether people should be admitted to either a
somatic nursing home or a psychogeriatric nursing home. Residents with predominantly psy-
chogeriatric disorders (mainly dementia) are cared for in psychogeriatric nursing homes, whe-
reas in somatic nursing homes people with predominantly somatic disorders, such as
Parkinson’s disease or diabetes mellitus, reside. Often, psychogeriatric care and somatic care is
provided in the same nursing home, and the division between the two types of care is between
wards. Second, the way care is provided in Dutch nursing homes is markedly different from
other countries. For instance, specially trained nursing home physicians provide medical care
on a continuous basis. This medical speciality requires a two-year postgraduate academic edu-
cation. In other countries, medical care in nursing homes is provided by general practitioners,
mostly on an on-demand basis and not continuously. Furthermore, in the Netherlands, care is
provided by a multidisciplinary team, in which nursing home physicians (1 full-time doctor per
100 residents), nurses, physical therapists, speech therapists, and psychologists closely colla-
19
1.2 Sett ing
Ambulatory e lder ly
The Dutch health care system is geared to facilitate elderly people (aged 65 and older) to
live at home for as long and as independently as possible. Eighty-two percent of the people
aged 65 and over live independently in the community [1]. Most people have healthcare insu-
rance, which covers the costs of primary care and medication as well as the costs of in-hospi-
tal and outpatient treatment. In addition, every Dutch citizen is insured under the Exceptional
Medical Expenses Act (AWBZ). This act provides the general public with insurance for health
risks not covered by normal healthcare insurance, such as admission to nursing homes and
residential homes and the costs of home care (e.g. meals-on-wheels and household help) [1].
General medical care is provided through general practitioners and pharmacies providing pri-
mary care services. In the Netherlands, more than 90% of healthcare problems are dealt with
in primary care. General practitioners can refer patients to 143 hospitals (with almost 60,000
beds). Every academic hospital and each large teaching hospital (approximately 17 in total) has
a specialised geriatric department, together with clinical geriatric teaching and research facili-
ties [1]. In other non-teaching hospitals separate ‘Geriatrische Afdeling Algemeen Ziekenhuis’
(GAAZ)-departments are present, providing medical care for geriatric patients.
In recent years, several initiatives have been issued towards improvements in quality of life
of elderly people. Increasingly, attention has been given to quality of drug use. In Dutch com-
munity pharmacies, pharmaceutical care is gradually implemented in daily community practi-
ce [2]. Community pharmacists have addressed some of the problems of medication use in the
elderly and have given suggestions for improvement [3]. Internationally, several studies have
focused on pharmaceutical care. In a multicenter international study performed in 7 European
countries, it was found that community pharmacy-based provision of pharmaceutical care
improved the well-being of elderly patients [4]. Patients reported better control of their medi-
cal conditions and cost savings associated with pharmaceutical care provision were observed
in most countries. In a 12-month controlled intervention study it was found that community
pharmacy-based interventions improved lung function, health-related quality of life, and self-
management in asthma patients [5].
Several secondary medical care services are provided on an outpatient basis, such as the
home visits of the Outpatient Thrombosis Services throughout the country to monitor oral anti-
coagulant therapy by measuring the prothrombin times. Home care organisations provide tailor-
18
References
1 Hoek JF, Penninx BW, Ligthart GJ, Ribbe MW. Health care for older persons, a country profile: the Netherlands.
J Am Geriatr Soc 2000; 48: 214-7.
2 Van Mil FJW, Tromp DF, McElnay JC, De Jong-van den Berg LTW, Vos R. Development of pharmaceutical care in
The Netherlands: pharmacy’s contemporary focus on the patient. J Am Pharm Assoc (Wash) 1999; 39: 395-401.
3 Van Mil JWF. Results of pharmaceutical care in the elderly, the OMA study. In Van Mil JWF. Pharmaceutical care,
the furure of pharmacy. Theory, research and practice [Thesis]. University of Groningen, 2000.
4 Bernsten C, Bjorkman I, Caramona M, Crealey G, Frokjaer B, Grundberger E, et al. (Pharmaceutical care of the
elderly in Europe Research Group (PEER)). Improving the well-being of elderly patients via community phar-
macy-based provision of pharmaceutical care. Drugs Aging 2001; 18: 63-77.
5 Schulz M, Verheyen F, Muhlig S, Muller JM, Muhlbauer K, Knop-Scheickert E, Petermann F, Bergmann KC.
Pharmaceutical care services for asthma patients: a controlled intervention study.
J Clin Pharmacol 2001; 41: 668-76.
6 Dijkstra GJ. De indicatiestelling voor verzorgingshuizen en verpleeghuizen [Proefschrift]. Rijksuniversiteit
Groningen, 2001.
7 Ribbe MW. Care for the elderly: the role of the nursing home in the Dutch health care system.
Int Psychogeriatr 1993; 5: 213-22.
8 Health Care Inspectorate. Medication distribution in nursing homes (in Dutch). Ministery of Health, Welfare and
Sports, The Hague, the Netherlands, 1997.
9 Dutch Association for Nursing Home Care (Vereniging voor Verpleeghuiszorg (NVVz) in samenwerking met
KNMP, NVZA, NVVA): Guideline Pharmaceutical Care in Nursing Homes (in Dutch). Utrecht, 1998.
10 Gurwitz JH, Soumerai SB, Avorn J. Improving medication prescribing and utilization in the nursing home.
J Am Geriatr Soc 1990; 38: 542-52.
11 Shorr RI, Fought RL, Ray WA. Changes in antipsychotic drug use in nursing homes during implementation of
the OBRA-87 regulations. JAMA 1994; 271: 358-62.
12 Schmidt I, Claesson CB, Westerholm B, Nilsson LG, Svarstad BL. The impact of regular mulidisciplinary team
interventions on psychotropic prescribing in Swedish nursing homes. J Am Geriatr Soc 1998; 46: 77-82.
13 Schmidt I, Claesson CB, Westerholm B, Svarstad BL. Resident characteristics and organizational factors
influencing the quality of drug use in Swedish nursing homes. Soc Sci Med 1998; 47: 961-71.
14 Lunn J, Chan K, Donoghue J, Riley B, Walley T. A study of the appropriateness of prescribing in nursing homes.
Int J Pharm Pract 1997; 5: 6-10.
15 Janzig JGE, Van ‘t Hof MA, Zitman FG. Drug use and cognitive function in residents of homes for the elderly.
Pharm World Sci 1997; 19: 279-82.
16 Wagner C, Van der Wal G, Groenewegen PP, De Bakker DH. The effectiveness of quality systems in nursing
homes: a review. Qual Health Care 2001; 10: 211-7.
21
borate. Medical specialists, such as neurologists and psychiatrists, provide specialised medical
care on consult basis. There are 330 nursing homes in the Netherlands, with a total of 57,000
beds (27 per 1000 inhabitants aged 65 and older) [1]. As mentioned earlier, all nursing home
admissions are financed by the AWBZ. The AWBZ reimburses about 98% of all nursing homes
expenses. In addition to addressing financial stability issues, this act regulates the care stan-
dards for this sector, monitored by regional health inspectors. Another act that relates to quali-
ty issues in nursing homes is the ‘Care Institutions Quality Act’ (Kwaliteitswet Zorginstellingen).
This act aims at adequate quality assurance in health care institutions.
Either community pharmacists or hospital pharmacists can provide the distribution of
medicines in Dutch nursing homes. To investigate the quality of the medication distribution
process and other pharmaceutical activities, in 1997 the Dutch Health Care Inspectorate held a
survey among 33 nursing homes. About half of these nursing homes were served by a hospital
pharmacy, the other half by a community pharmacy. It was concluded that quality aspects
should be more incorporated in the medication distribution processes. The pharmacist should
play a more profound role in the pharmaceutical care to nursing home residents, such as par-
ticipation in drug and therapeutics committees, evaluation of prescribing on patient level, and
regular updates of drug formularies [8]. As a result of this survey, a ‘Guideline Pharmaceutical
Care in Nursing Homes’ was drafted in 1998 [9]. This guideline describes how pharmaceutical
care by the pharmacist in nursing homes is best performed, and serves as a guideline for imple-
menting quality assurance processes. For both economic and therapeutic reasons, a drug for-
mulary is used in almost every Dutch nursing home, constituting of a limitative list of preferred
medications. In several Dutch nursing homes, drug therapy issues are regularly discussed in
Pharmacotherapy Discussion Groups, in which both nursing home physicians and pharmacists
participate. For certification, these meetings should be held at least 6 times a year. Studies
addressing the quality of pharmaceutical care in nursing homes have not been performed
extensively. Mainly this work has been carried out in the United States [10,11], but in recent
years European studies also focus on this issue [12-16].
20
Chapter 2
Drug ut i l i sat ion studiesin the e lder ly
2322
2.1 Background
Introduct ion
Several reviews on drug utilisation and quality of prescribing in elderly outpatients have
been carried out demonstrating high levels of drug use and a considerable number of patients
receiving inappropriate medications [1]. In the Netherlands, drug utilisation studies have
demonstrated that the prevalence of multiple chronic drug use in elderly outpatients is high [2].
Recently, the extent of polypharmacy in Dutch elderly outpatients was investigated and it was
concluded that the problem of polypharmacy was less than in other countries [3]. While drug
use in elderly outpatients has been extensively studied, less is known about drug utilisation
and quality of prescribing in Dutch nursing homes. In the next paragraphs, a review is given of
some major studies on drug use and quality of prescribing in nursing homes.
Drug ut i l i sat ion studies in nurs ing homes
Drug use in nursing homes has been reviewed in several articles [4-7] and a number of drug
utilisation studies have been performed in nursing homes [8-24]. Many of these focus on cer-
tain drugs or drug groups, in particular psychotropics [21-24]. In table 1 a summary of some
major studies on drug use in nursing homes during 1990-2000 is given. The number of nursing
home residents reviewed varies from 60 to 1854, and the average number of drugs prescribed
per resident varies from 2.5 to 8.8. These differences might be due to the different lengths of
time for which drug use was studied. In many of these studies, data on drug use was collected
by medical chart review or by interviews. Three drug utilisation studies have been published
from the Netherlands [12,13,18]. Troost and co-workers used hospital-dispensing data to assess
the defined daily dosages (DDDs) per 100 beddays [12]. Drug use was not analysed on an indi-
vidual patient level. Koopmans and colleagues studied drug use of psychogeriatric patients [13],
and Verkoulen-Wijers [18] reviewed drug use of 60 patients using a cross-sectional design. Two
other Dutch studies were published before 1990 [25,26]. Merkus and co-workers [25] investi-
gated drug use at admission and 3.5 years later in 112 patients aged 75 and over. In people res-
iding in homes providing somatic care, medication increased from 3.8 to 5 prescriptions per
day, and in people residing in nursing homes providing psychogeriatric care, medication incre-
ased from 2.7 to 3.4 prescriptions per day. Van Zuylen [26] showed that 80% of the patients
(n=198) used 2 to 7 drugs (on average 4.4). The drugs used most frequently were psychotropics,
analgesics, cardiovascular drugs, diuretics and laxatives.
25
Out l ine
In this chapter, we present several drug utilisation studies in the elderly. This chapter comp-
rises two parts: in part one (section 2.1 through 2.5) drug utilisation studies in nursing home
residents are described, and in part two (section 2.6 and 2.7) drug use in ambulatory elderly is
described. In section 2.6, both ambulatory elderly and nursing home residents are studied
separately.
In section 2.1 some major international studies on drug use and quality of prescribing in
nursing homes are summarised and the Dutch studies that have been published on this subject
are reviewed. Section 2.2 describes how pharmacy prescription data from nursing homes have
been used to build a nursing home database with the aim to perform drug utilisation and risk
assessment studies. This section also presents a suggestion for the criteria such data should
meet to perform pharmacoepidemiological studies. In section 2.3, a drug utilisation study in 6
nursing homes is presented. In this study, detailed information is given on determinants of drug
use in a cohort of 2,355 residents. The results of this study served as a starting point to inves-
tigate some of the issues on prescribing quality in more detail in the next sections. Section 2.4
describes the occurrence and nature of potential drug-drug interactions in the same cohort of
nursing home patients. Section 2.5 presents a pilot study that used several prescribing indica-
tors, based on the studies in 2.3 and 2.4, to evaluate drug prescribing in Dutch nursing homes.
Section 2.6 presents a study on the concomitant use of benzodiazepines and antidepres-
sants in elderly outpatients and nursing home patients. We assessed whether differences
between co-prescribing with tricyclic antidepressants and selective serotonine re-uptake inhi-
bitors existed. In section 2.7, the concomitant use of non-steroidal anti-inflammatory drugs and
gastroprotective drugs, as an example of a beneficial drug-drug combination was investigated.
24
In conclusion, several studies have investigated the rate of drug use in nursing homes.
Many used cross-sectional designs, and only few studies used longitudinal prescription data to
evaluate effects over time. The number of nursing home residents included in each study varied
considerably. The studies carried out in the Netherlands involved relatively small numbers of
residents. To obtain more insight into drug use in Dutch nursing homes, a study among 2,355
residents was carried out (section 2.3).
Qual i ty of prescr ib ing in nurs ing homes
In particular in the United States, much attention has been given to rational and approp-
riate prescribing in nursing homes. In 1987, the Omnibus Reconciliation Act (OBRA ‘87) was pas-
sed as a result of increasing public concern about the overuse of neuroloptics in nursing homes.
These federal regulations were established to ensure appropriate care in US nursing homes,
and in particular to reduce the unnecessary prescribing of antipsychotic medications [27]. Since
OBRA ‘87 numerous studies have addressed the problem of unnecessary or inappropriate drug
use in nursing homes in the US and other countries and have investigated opportunities for
improvement [28-32]. Several tools have been developed to assess the appropriateness of pre-
scribing [33]. Many of these tools were developed for assessing medication appropriateness in
elderly outpatients, and not nursing homes. Most tools used information on clinical status or
diagnoses. In 1991, Beers and colleagues developed a set of criteria for identifying inapprop-
riate medication use in nursing home residents in the US [34]. These criteria, based on expert
consensus, consisted of a list of 23 medications that should be avoided and 13 medication doses,
frequencies, or duration of prescriptions that generally should not be exceeded. The list of inap-
propriate drugs needs to be updated periodically, as it was based on state of knowledge at that
time (1989). An update, incorporating clinical information, was published in 1997 [35].
Internationally, several studies have used Beers’ criteria to assess medication appropriateness.
In 1992, a prospective cohort study was carried out using Beers criteria among 12 nursing
homes in the US (n=1106) [8]. It was found that 40% of the residents received at least one
inappropriate prescription, and 10% received two or more inappropriate medications concur-
rently. Female residents and residents of large nursing homes were at the highest risk of recei-
ving inappropriate medications. In 1996, Gupta and co-workers [36] investigated the associa-
tion between costs and inappropriate prescribing using a slightly modified version of Beers cri-
teria in a retrospective, cross-sectional study among nursing home residents (Medicaid benefi-
ciaries; n=19,932). It was found that cost of pharmaceutical services for a resident was positi-
vely correlated with the number of different inappropriate drugs prescribed, number of physi-
2726
Table 1: A review of studies on drug use in nursing homes
Ref Country N Study design Average Main findings(year of number ofpublication) prescriptions
8 US 81 Analysis of medication 4.7 (entry) Increase in medication prescribing(1990) prescribed at entry and 6.2 (3 months) due to increase in prn* medications
after 3 months
9 Belgium 198 Cross-sectional chart 4.5 Drug use increased with age but (1992) review in sample of stabilises after 80 yrs
patients aged ≥ 62 yrs
10 US 1106 Cross-sectional study 7.2 Inappropriate prescribing was common(1992) using pharmacy data
of patients ≥ 65 yrs
11 US 120 Effect of drug regimen 7.2-5.3 Pharmacist reduced medication(1992) review on medication use in nursing home by computerised
use drug regimen review
12 Netherlands 147 Analysis of pharmacy - Drug use in general according to(1993) dispensing data drug formulary
(number of DDDs/100 beddays) during 1991-1992
13 Netherlands 390 Retrospective chart review 2.5-2.9 Increase in drug use (mainly laxatives)(1994) among psychogeriatric after admission to nursing home
patients
14 South Africa 85 Intervention study; review 4.8 41% reduction in incidence of(1996) of medical profile charts polypharmacy due to
for drug related problems pharmacist intervention
15 UK 101 Intervention study; review 6.5 53% of residents showed ≥ 1 (1997) of medical records to study inappropriate prescription
appropriateness of prescribing
16 Sweden 1854 Intervention study; review 7.7 No effect of intervention on overall(1998) of medical records use of medication
17 Australia 998 Cross-sectional survey of 6.6 Nursing home culture exerts a major(1998) medication use by chart (prescribed) influence over prescribing and
review 4.8 administration of medications(administered)
18 Netherlands 60 Cross-sectional survey of 4.9 Laxatives, diuretics, psycholeptics and(1999) medication use by chart including prn*; antithrombotics used most frequently
review 4,6 excluding prn
19 Sweden 1001 Cross-sectional survey in 3.4 High prevalence of drug use and(1999) ('87-'89); cohort of subject aged 81 ('87-'89); polypharmacy in very elderly
681 and over 4.6('94-'96) ('94-'96)
20 Sweden 1549 Controlled intervention 8.0 (1995) Intervention homes showed higher(2000) study in 36 nursing homes 8.8 (1998) quality of drug use
# expressed as ‘time pattern of drug exposure’: uninterrupted period of usage of the same drug* prn = pro re nata = as required
formed on the appropriateness or quality of prescribing in nursing homes. In section 2.4 to 2.7
we have made an attempt to identify suboptimal prescribing by using several aspects of pre-
scribing indicators from the international literature, such as the occurrence of drug-drug inter-
actions, the concomitant use of certain drug groups, and selection of formulary drug use.
References
1 Aparasu RR, Mort JR. Inappropriate prescribing for the elderly: Beers criteria-based review. Ann Pharmacother
2000; 34: 338-46.
2 Heerdink ER. Clustering of drug use in the elderly: population-based studies into prevalence and outcomes
[Thesis]. University of Utrecht, 1995.
3 Veehof LJG. Polypharmacy in the elderly [Thesis]. University of Groningen, 1999.
4 Lamy P. Institutionalisation and drug use in older adults in the US. Drugs Aging 1993; 3: 232-7.
5 Avorn J, Gurwitz JH. Drug use in the nursing home. Ann Intern Med 1995; 123: 195-204.
6 Furniss L,Craig SKL, Burns A. Medication use in nursing homes for elderly people. Int J Geriatr Psychiatry 1998;
13: 433-9.
7 Gurwitz JH, Soumarai SB, Avorn J. Improving medication prescribing and utilization in the nursing home.
J Am Geriatr Soc 1990; 38: 542-52.
8 Wayne SJ, Rhyne RL, Stratton M. Longitudinal prescribing patterns in a nursing home population.
J Am Geriatr Soc 1990; 40: 53-6.
9 Vander Stichele RH, Mestdagh J, Van Haecht CH, De Potter B, Bogaert MG. Medication utilization and patient
information in homes for the aged. Eur J Clin Pharmacol 1992; 3: 319-21.
10 Beers MH, Ouslander JG, Fingold SF, Morgenstern H, Reuben DB, Rogers W et al. Inappropriate medication
prescribing in skilled-nursing facilities. Ann Intern Med 1992; 151: 1825-31.
11 Laucka PV, Hoffman NB. Decreasing medication use in a nursing-home patient-care unit. Am J Hosp Pharm
1992; 49: 96-9.
12 Troost SJ, Smit LH, Wentink DH, Neef C. Gebruikscijfers van geneesmiddelen uit het verpleeghuis ‘De Cromhoff’
te Enschede [Drug utilization figures in a nursing home]. Pharm Weekbl 1993; 128: 1511-6.
13 Koopmans RT, de Haan HH, van den Hoogen HJ, Gribnau FW, Hekster YA, van Weel C. Veranderingen in
geneesmiddelgebruik tijdens een verblijf in een psychogeriatrisch verpleeghuis. [Changes in drug use during
stay in a psychogeriatric nursing home]. Ned Tijdschr Geneeskd 1994; 138: 1122-6.
14 Belligan M, Wiseman IC. Pharmacist intervention in an elderly care facility. Int J Pharm Pract 1996; 4: 25-9.
15 Lunn J, Chan K, Donoghue J, Riley B, Walley T. A study of the appropriateness of prescribing in nursing homes.
Int J Pharm Pract 1997; 5: 6-10.
16 Claesson CB, Schmidt IK. Drug use in Swedish nursing homes. Clin Drug Invest 1998; 16: 441-52.
17 Roberts MS, King M, Stokes JA, Lynne TA, Bonner CJ, McCarthy S, Wilson A, Glasziou P, Pugh John W. Medication
prescribing and administration in nursing homes. Age Ageing 1998; 27: 385-92.
18 Verkoulen-Wijers MJ, Koopmans RTCM, Schimmel W. Geneesmiddelengebruik van somatische verpleeghuis-
bewoners en verzorgingshuisbewoners in Zorgcentrum Tilburg-zuid. Tijdschr Verpleeghuisgeneeskd 1999; 1: 4-7.
19 Giron MST, Claesson C, Thorslund M, Oke T, Winblad B, Fastbom J. Drug use patterns in a very elderly
population. Clin Drug Invest 1999; 17: 389-98.
20 Schmidt IK, Fastbom J. Quality of drug use in Swedish nursing homes. A follow-up study. Clin Drug Invest 2000;
20: 433-446.
29
cians and pharmacies used and geographic region. In 1997, Lunn and colleagues [37] developed
a set of 18 explicit criteria, based on expert’s opinions, for identifying inappropriate prescribing
in 101 nursing home residents in the UK. Fifty-three percent of the residents had one or more
inappropriate prescriptions. Medicines most frequently associated with inappropriate prescri-
bing were cardiovascular drugs and drugs for the central nervous system [37]. In Sweden, an
attempt was made to identify inappropriate psychotropic drug prescribing in 33 Swedish nur-
sing homes (1823 residents) by Schmidt and colleagues [38]. They developed a list of 13 criteria,
based on Swedish guidelines for measuring excessive use of psychotropic drug use in the elder-
ly. A wide variability in the appropriateness of drug use among the 33 nursing homes was
found. Drug use for a majority of residents had deviated from one or more of the drug use cri-
teria. Overall, concern was expressed about the quality of drug prescribing practices in Swedish
nursing homes. The drug use criteria developed addressed three main issues: deviation of
documentation on indication of drug (for example: antipsychotic drug prescribed; psychotic
diagnosis absent), deviations of drug choice (for example: prescribing of non recommended
hypnotic drug), and deviations of excess (for example: prescribing of more than 2 psychotropic
drugs concomitantly). Again, these criteria used in part clinical information. In another Swedish
study [20] prescribing indicators were used to assess the effect of an intervention aimed at
improving drug use through improved teamwork among physicians, pharmacists, nurses and
nurses’ assistants. In the intervention homes, a higher quality of drug use was seen after the
intervention, compared with the control homes. The prescribing indicators addressed three
issues: indication of drug (for example: antipsychotic drug prescribed without diagnoses of psy-
chotic symptom); quantity of drug use (more than 2 psychotropic drugs) and potential interac-
tions.
In conclusion, studies on prescribing appropriateness in nursing homes have shown that a
considerable proportion of the nursing home residents received inappropriate prescribing. The
way prescribing appropriateness was assessed differed among the studies as different prescri-
bing or quality indicators are in use. Prescribing indicators used in one health care system are
not automatically applicable to other health care systems due to differences in national phar-
macotherapy guidelines and drug formularies. Furthermore, for many prescribing indicators
information on clinical status, such as laboratory results or diagnoses are needed, which makes
them unsuitable to apply them based solely on pharmacy prescription data. To assess medica-
tion appropriateness in nursing homes, indicators should be used that reflect deviations from
national pharmacotherapy guidelines and drug formularies. As these vary with time and loca-
tion, prescribing indicators are dependent on the country or even the institution where the
research is performed. To our knowledge, in Dutch nursing homes, no studies have been per-
28
2.2 Use of hospi ta l pharmacy data in pharmacoepidemiologic research in nurs ing homes
K.N. van Dijk 1, 2, C.S. de Vries 3, J.R.B.J. Brouwers 1, 2, L.T.W. de Jong-van den Berg 1
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, University
Centre for Pharmacy, Groningen University Institute for Drug Exploration (GUIDE),
Groningen, the Netherlands2 Clinical Pharmacy Department, Medical Centre Leeuwarden, the Netherlands3 Department of Pharmacoepidemiology, Postgraduate Medical School, University of Surrey,
Guildford, United Kingdom
31
21 Nygaard HA, Naik M. Use of psychotropic drugs in homes for the aged in Bergen, Norway: a comperative study.
Norwegian Journal of Epidemiology 1998; 8: 133-8.
22 McGrath AM, Jackson GA. Survey of neuroleptic prescribing in residents of nursing homes in Glasgow.
BMJ 1996; 312: 611-2.
23 Cohen-Mansfield J, Lipson S, Gruber-Baldini AL, Farley J, Woosley R. Longitudinal prescribing pattern of
psychotropic drugs in nursing home residents. Exp Clin Psychopharmacol 1996; 4: 224-33.
24 Koopmans RTCM, van Rossum JM, van den Hoogen HJM, Hekster YA, Willekens-Bogaers MAJH, van Weel C.
Psychotropic drug use in a group of Dutch nursing home patients with dementia: many users, long-term use,
but low doses. Pharm World Sci 1996; 18: 42-7.
25 Merkus JW, Deurenberg HP, Pollmann AM. Het geneesmiddelgebruik in 3 verpleeghuizen voor lichamelijk
zieken [Drug utilization in 3 nursing homes for somatic patients]. Tijdschr Gerontol Geriatr 1985; 16: 87-95.
26 Van Zuylen C, Oostendorp FMGM, van Beusekom BR, Cools HJM, Bolk JH, Ligthart GJ. Toenemend genees-
middelgebruik in het verpleeghuis [Increasing use of medication in nursing homes]. Ned Tijdschr Geneesk
1988; 132: 1692-5.
27 Llorente MD, Olsen EJ, Leyva O, Silverman MA, Lewis JE, Rivero J. Use of antipsychotic drugs in nursing homes:
current compliance with OBRA regulations. J Am Geriatr Soc 1998; 46: 198-201.
28 Semla TP, Palla K, Poddig P, Brauner DJ. Effect of the Omnibus Reconciliation Act 1987 on antipsychotic
prescribing in nursing home residents. J Am Geriatr Soc 1994; 42: 648-52.
29 Garrard J, Makris L, Dunham T, Heston LL, Cooper S, Ratner ER, et al. Evaluation of neuroleptic drug use by
nursing home elderly under proposed Medicare and Medicaid regulations. JAMA 1991; 265: 463-7.
30 Rovner BW, Edelman BA, Cox MP, Shmuely Y. The impact of antipsychotic drug regulations on psychotropic
prescribing practices in nursing homes. Am J Psychiatr 1992; 149: 1390-2.
31 Schorr RI, Fought RL, Ray WA. Changes in antipsychotic drug use in nursing homes during implementation of
the OBRA-87 regulations. JAMA 1994; 271: 358-62.
32 Ray WA, Taylor JA, Meador KG, Lichtenstein MJ, Griffin MR, Fought R, et al. Reducing antipsychotic drug use in
nursing homes: a controlled trial of provider education. Arch Intern Med 1993; 153: 713-21.
33 Shelton PS, Fritsch MA, Scott MA. Assessing the medication appropriateness in the elderly. A review of available
measures. Drugs Aging 2000, 16: 437-50.
34 Beers MH, Ouslander JG, Rollingher I, Reuben DB, Brooks J, Beck J. Explicit criteria for determining
inappropriate medication use in nursing homes. Arch Intern Med 1991; 151: 1825-32.
35 Beers MH. Explicit criteria for determining potentially inappropriate medication use by the elderly.
Arch Intern Med 1997; 157: 1531-6.
36 Gupta S, Rappaport HM, Bennett LT. Inappropriate drug prescribing and related outcomes for elderly Medicaid
benificiaries residing in nursing homes. Clin Therap 1996; 18: 183-96.
37 Lunn J, Chan K, Donoghue J, Riley B, Walley T. A study of the appropriateness of prescribing in nursing homes.
Int J Pharm Pract 1997; 5: 6-10.
38 Schmidt IK, Claesson CB, Westerholm B, Svarstad BL. Resident characteristics and organizational factors
influencing the quality of drug use in Swedish nursing homes. Soc Sci Med 1998; 47: 961-71.
30
Data avai lable
Medicat ion order data
The study was carried out in six nursing homes for long-term care in the Netherlands.
These nursing homes each had a bed capacity that varied between 90 and 225, and each was
served by one of three hospital pharmacies. In the drug dispensing system in the nursing
homes studied, all drugs that were dispensed to residents were registered in the hospital phar-
macy computer system (Apho-data®). Routinely, the nursing home physicians have to write
medication orders for each change in the drug regimen, such as a dosage or frequency change,
the discontinuation of the drug and, obviously, the start of a new drug. In this way, any changes
in medication are updated routinely on a daily basis in the hospital pharmacy computer system
and a complete medication history is kept for each individual resident. Every day, nurses dis-
pense medication to individual nursing home residents on the basis of the information recor-
ded in the computer system (drug, dosage, route and time of administration). As a consequen-
ce, the recording of actual drug use can be considered very accurate.
SIVIS data
In the Netherlands, a national information system on nursing home residents (SIVIS) is
operational. The SIVIS database consists of anonymous administrative, nursing and medical
data collected on individual nursing home residents. More than 70% of the Dutch nursing
homes contribute to the SIVIS database. The SIVIS data are collected quarterly in each nursing
home by the nursing home staff and, subsequently, these data are anonymised and registered
nationally. The SIVIS morbidity classification is derived from the International Classification of
Diseases (ICD-9) and contains 99 diagnoses. For each resident, a maximum of three diagnoses
can be registered each time.
Data col lect ion
Medicat ion order data
We collected medication order data of all nursing home residents for a 2-year period
between 01-10-1993 and 01-10-1995. Patient data for each nursing home resident included date
of birth, gender, date of admission to the nursing home, date of discharge, code of the nursing
home, and code of the attending nursing home physician. For every prescription a start date
and, if known, an end date was registered, enabling us to calculate the actual duration of drug
use. Furthermore, the name of the drug, daily dose, dosage interval and route and time of
33
Introduct ion
In pharmacoepidemiology, computerised medication databases are a useful tool. To study
drug use in primary health care in the Netherlands several computerised medication databa-
ses are available, such as the PHARMO-database [1], the InterAction-database [2], and the
IPCI-database [3]. Both the InterAction-database and the PHARMO-database are based on
community pharmacy records. Because patients generally attend only one pharmacy and com-
munity pharmacies are entirely automated, almost complete individual medication profiles
covering several years are available. Community pharmacy records have been reported to be a
reliable source of drug exposure [4]. The PHARMO database is record-linked with hospital
diagnoses data. The IPCI-database is based on general practitioners’ (GPs) prescribing data and
as such the information recorded is comparable with the GPRD-database in the UK [5], al-
though the IPCI-database is much smaller [3]. A variety of drug utilisation and drug safety stu-
dies have been performed using these Dutch databases [6-9].
In the Netherlands, drug utilisation studies in secondary (hospitals) and tertiary (nursing
homes) health care are performed sparingly, partly because computerised individual hospital
medication records have not been available until the mid 90s. Internationally, in particular in
the United States and Canada, several drug utilisation studies have been carried out in nursing
homes using large administrative databases [10-14]. In the US a Minimum Data Set, a data col-
lection instrument containing more than 300 demographic, clinical and treatment variables, is
used for each patient that is admitted to a certified nursing home [15]. Although not all drug use
is included in these databases, the fact that they contain medical diagnoses makes them very
valuable. In a large drug utilisation study in nursing homes in Sweden [16] medication use was
recorded by research nurses. In the Netherlands, computerised medication databases in nur-
sing homes have not been available until recently. To our knowledge, these databases have not
yet been used in drug utilisation studies [17,18]. The applicability of computerised medication
databases in pharmacoepidemiology in this population is therefore unknown. Ideally, both
detailed information on patients’ drug use and health status should be available for drug
research in nursing homes.
In this study we describe how medication order data from nursing homes have been used
to build a nursing home database with the aim to perform drug utilisation and risk assessment
studies. Furthermore, we make a suggestion for the criteria such data should meet to perform
such studies.
32
Data ver i f i cat ion
Medicat ion order data
Accuracy of the medication database was verified by comparing the medication history
from the original pharmacy computer system with the medication history in the newly built
database for 10 randomly selected patients for each nursing home (total of 60 patients). In view
of the drug distribution system described above, no other way of verifying the medication data-
base was possible (for example, patient interviews, or nurses’ charts). We did not find any dis-
crepancies.
SIVIS data
To verify the accuracy and completeness of the SIVIS diagnoses data we performed a sen-
sitivity and specificity analysis in which we compared medication order records and SIVIS diag-
noses records for diabetes mellitus and Parkinson’s disease. These diseases were chosen in
view of the clearly defined drug groups that are used for these disorders; namely antiglycaemic
drugs (ATC code A10) and anti-Parkinson drugs (ATC code N04). The validation of SIVIS diag-
noses data resulted in three outcomes: registered SIVIS diagnosis and registered proxy drug
use (true positive), no SIVIS diagnosis that could relate to proxy drug dispensed (false negati-
ve), and SIVIS diagnosis registered and no proxy drug prescribed (false positive). The positive
predictive value of the SIVIS diagnosis was calculated as the proportion of patients with the
SIVIS diagnosis and using the proxy drug among all patients who are registered with the SIVIS
diagnosis. Sensitivity of the SIVIS diagnosis was the proportion of patients registered with the
SIVIS diagnosis and using the proxy drug among the total number of patients who were pre-
scribed the proxy drug. Specificity of the SIVIS diagnosis was the proportion of patients pre-
scribed non-proxy drugs for other indications than the SIVIS diagnosis among the total num-
ber of patients prescribed non-proxy drugs. The results of these analyses are given in table 1.
35
administration were recorded. Each medication record was given a drug ID (KNMP-number, a
unique code for every formulation and pack size of a drug that is on the market in the
Netherlands). Each drug ID and hence every medication record was record-linked with the
national reference drug database of the Royal Dutch Association for the Advancement of
Pharmacy (KNMP, The Hague), to collect drug-specific information such as Anatomical
Therapeutic Chemical (ATC) codes and defined daily dosages (DDD).
SIVIS data and record- l inkage
The medication database was record-linked with the SIVIS database, by matching 3 patient
characteristics (date of birth, gender and nursing home code), in order to collect data on mor-
bidity, type of care (somatic or psychogeriatric) and mobility (2 categories) from the SIVIS data-
base. Fourteen percent of the patient records could not be record-linked to the SIVIS database.
In figure 1 the structure of the databases is given.
34
Patient database
Patient IDDate of birth*Gender*Nursing home code*Date of admission Date of discharge (if applicable)
SIVIS database
Date of birth*Gender*Nursing home code*Diagnosis codeType of careMobility code
Reference drug database (Z-index)
Drug IDATC codeDDD value
Medication database
Patient IDDrug nameDrug IDMedication IDStart date Stop dateDaily dosageDosage intervalPrescriber code
Figure 1: Structure of the databases (*: used for record-linkage)
Table 1a: Results of the sensitivity and specificity analysis of the SIVIS diagnoses data for diabetes mellitus
Pharmacy record ATC code A10
+ - Total
SIVIS diagnose + 152 24 176 Positive predictive value:
diabetes mellitus 152 / 176 = 0.86
- 207 1972 2179 Sensitivity:
152 / 359 = 0.42
Total 359 1996 2355 Specificity:
1972 / 1996 = 0.99
37
Study populat ion
The source population consisted of 2,966 patients. Of this population, the admission date
was missing in 264 patients (9%) and the date of discharge was missing in 268 patients (10%).
If the admission date was unknown, it was assumed to be the first medication start date. If the
date of discharge was unknown, it was assumed to be the last end date of the medication used
or as the last day of the study period (01-10-1995), whichever was the earliest. 194 of 2,966
(6.5%) people were excluded because they were younger than 65 years (average age 52.6
years (SD 10.6) versus 82.1 years (SD 7.4) (p<0.05)). Compared with the remaining 2,772 patients,
the excluded population included more men (44.8% versus 28.9% (p<0.05). Of the 2,772
patients left, 394 (14.2%) were excluded because they could not be linked to the SIVIS databa-
se. Compared with the remaining 2,378 residents, they had the same average age (82.5 versus
82.0 years), and there were slightly more women present (74.1% versus 70.6%; p>0.05). These
patients were equally distributed over the six nursing homes. Of the 2,378 patients left, 23 (1%)
were excluded because of missing data (for example, the period of stay could not be calcula-
ted). Compared with the remaining 2,355 patients, this population included more men (47.8%
versus 29.3%; p<0.05). The final study population consisted of 2,355 patients. Figure 2 shows
how the study population was constructed. In table 2 patient characteristics are given for the
study population.
36
Table 1b: Results of the sensitivity and specificity analysis of the SIVIS diagnoses data for M. Parkinson
Pharmacy record ATC code N04
+ - Total
SIVIS diagnose + 115 36 151 Positive predictive value:
M. Parkinson 115 / 151 = 0.76
- 76 2128 2204 Sensitivity:
115 / 191 = 0.60
Total 191 2164 2355 Specificity:
2128 / 2164 = 0.98
Source population: N=2,966
Age: 80.1 yrs (SD 10.6)Gender: 70.0% female# of medication records: 42,648 (14.4)¶
N=2,772
Age: 82.1 (SD 7.4)Gender: 71.1% female# of medication records: 39,793 (14.4)¶
N=2,378
Age: 82.0 (SD 7.3)Gender: 70.6% female# of medication records: 35,186 (14.8)¶
Study population: N=2,355
Age: 82.0 (SD 7.3)Gender: 70.7% female# of medication records: 34,916 (14.8)¶
N=194 (6.5%)
Age: 52.6 (SD 10.6)Gender: 55.2% female# of medication records: 2,855 (14.7)¶
N=394 (14.2%)
Age: 82.5 (SD 8.1)Gender: 74.1% female# of medication records: 4,607 (11.7)¶
N=23 (1%)
Age: 79.4 (SD 8.1)Gender: 52.2% female# of medication records: 270 (11.7)¶
< 65 yrs
no SIVIS-data
missing data
Figure 2: Construction of the study population
Discuss ion
This study describes how hospital pharmacy data and SIVIS data can be used to build a
database to perform pharmacoepidemiological studies of drug utilisation and drug safety in
nursing home residents [19-21]. Recommendations are summarised in table 3. We encountered
several pitfalls during our study, which are described below.
Data availability. Detailed information on individual patients’ drug utilisation profiles has
to be available on a continuous basis. In most pharmacy computer systems, it is now common
use to register this information on a continuous basis for longer periods of time (e.g. 5-10
years). In this study we retrieved data for a two-year period. Ideally, it should be possible to
retrieve data at any time for any period of time. Because information on morbidity and mobili-
ty was needed to perform one study [19], we collected these data from the national nursing
home information database (SIVIS). At the moment, SIVIS data are the only source of automa-
ted diagnoses data available.
Data collection. An important aspect of the data collection is that the data are adequately
anonymised. Ideally, the pharmacy computer system should contain special ‘export files’ by
which drug utilisation data can be anonymously collected on an individual level. Collection of
the SIVIS data was done by record-linkage. Because the patient identifier used in the pharma-
cy records was different from the patient identifier in the SIVIS database, we performed a
record-linkage on three variables: date of birth, sex and nursing home code. Fourteen percent
of the residents could not be linked. This could have been due to the fact that these patients
had not been registered in the SIVIS database yet.
Data completeness. Completeness and accuracy highly depends on the organisation and
structure of the (hospital) pharmacy concerned. For example, adequate quality control proce-
dures should ensure that all necessary information is recorded in the pharmacy computer sys-
tem. The professional organisation of Dutch hospital pharmacies facilitates adequate quality
control by both pharmacy technicians (who generally record the data), and hospital pharma-
cists (who generally check and supervise). We found that in 10% of the patients the date of dis-
charge was missing, and that in 9% of the patients the admission date was missing. These per-
centages could be decreased when it is made impossible not to fill in certain database fields at
the data entry stage (by pharmacy technicians). Use of over-the-counter (OTC) medication has
not been included in our database. We expect this to be relatively low due to practical reasons
such as immobility of the residents and continuous medical attention by both nursing and
medical staff and the possibility to receive drugs that are available OTC via prescription by the
nursing home physician.
39
Medicat ion use
Initially, for the source population, an average number of 14.4 medication records per
patient were registered in the medication database. In figure 2 the number of medication
records for each patient cohort is given. In the final study population, the average number of
(registered) medication records per patient was 14.8. The relatively high number of medication
records recorded per patient can be explained by the fact that each change (dosage or fre-
quency change) in the therapeutic regimen is recorded separately [19].
38
Table 2: Characteristics of the study population (n=2,355)
Variable Number of residents (% of total)
Age 82 (SD 7.3)
Gender
Male 689 (29%)
Female 1666 (71%)
Type of nursing
Psychogeriatric 700 (30%)
Somatic 1609 (68%)
Not known 46 (2%)
Morbidity
Parkinson's disease 151 (6%)
Diabetes mellitus 176 (7%)
Depression 40 (2%)
Dementia 689 (29%)
Mobility
Mobile 1370 (58%)
Immobile 985 (42%)
Number of different medications
0-5 626 (27%)
6-10 969 (41%)
> 10 760 (32%)
Average number of different medications per day per resident 4.9
Average number of different medication (based on ATC-codes; fifth level) per resident during study residence in nursing home 8.9 (SD 4.9)
41
Data verification. In our study we used medication order data, which form the basis for the
dispensing of medication by nurses to individual nursing home residents. One important con-
dition of this dispensing system is that every change in drug therapy is known to the hospital
pharmacy. Unlike for community pharmacy records [4], the accuracy and completeness of the
hospital pharmacy data has not been verified. In the nursing homes studied, nursing home phy-
sicians are obliged to record every change in drug therapy and send these changes (by fax)
immediately to the hospital pharmacy. As a result, hospital pharmacy data are a reliable sour-
ce of drug exposure of nursing home residents. We did not find any discrepancies when we
verified the medication data. From the SIVIS sensitivity and specificity analysis it was found
that the positive predictive value (PPV) of the SIVIS diagnoses diabetes mellitus was 0.86, and
0.76 for M. Parkinson. These data indicate that 14% and 24% of the residents who are diagno-
sed in the SIVIS-database with diabetes mellitus and M. Parkinson respectively, do not use
medication that is commonly used for these disorders. There is a discrepancy between the
medication records and the SIVIS diagnoses data. This suggests that the prevalence of these
disorders may be overestimated when using SIVIS diagnoses data alone. The sensitivity valu-
es of the SIVIS diagnoses are relatively low, indicating that 58% and 40% of the residents
using drugs for diabetes mellitus and M. Parkinson respectively, are not registered as suffering
from these disorders in the SIVIS database. This suggests a large underestimation of the per-
centage of residents with diabetes mellitus and M. Parkinson when SIVIS data alone are used,
when pharmacy data are considered the ‘gold standard’. A reason for this could be the fact that
these diagnoses are not one of the three diagnoses registered, however in view of the disabling
consequences of M. Parkinson this seems unlikely. Further research is needed into the reasons
for these discrepancies. The specificity values of the SIVIS diagnoses diabetes mellitus and M.
Parkinson are high (0.99 and 0.98, respectively), indicating that no SIVIS diagnoses are recor-
ded for residents that do not use medication for these diagnoses. We suggest combining both
pharmacy data and SIVIS-diagnoses data to get a more reliable estimate of the true prevalen-
ce. Recently, Van de Vijver and co-workers showed that antiparkinsonian drugs in pharmacy
records in ambulatory patients aged 55 and older can be used as a reliable marker for M.
Parkinson [22]. Misclassification of drug use could occur when medication that is prescribed
and hence registered in the pharmacy computer system, is not actually consumed by the
patient (either by non-compliance or by errors in the distribution process). However, a pilot
study has shown that medication compliance in Dutch nursing homes is generally high (99%)
[23]. Because we did not have information on the use of OTC-medication, this may lead to an
underestimation of exposure to OTC drugs and hence misclassification.
40
Tab
le 3
: Dis
crep
anci
es b
etw
een
data
requ
ired
for p
harm
acoe
pide
mio
logi
cal r
esea
rch
and
data
ava
ilabl
e in
hos
pita
l pha
rmac
y pr
escr
iptio
n da
taba
ses
Aspe
cts
Requ
irem
ents
for
Data
ava
ilabl
e in
hos
pita
l Re
com
men
datio
nsph
arm
acoe
pide
mio
logi
c re
sear
chph
arm
acy
pres
crip
tion
data
base
us
ed in
cur
rent
stu
dy
Popu
latio
n
Sam
ple
size
N
eeds
to b
e su
ffici
ently
larg
e to
By g
roup
ing
patie
nt d
ata
from
Colle
ct d
ata
from
sev
eral
nur
sing
hom
espe
rfor
m p
harm
acoe
pide
mio
logi
c di
ffere
nt h
ospi
tal p
harm
acie
s an
(and
hen
ce h
ospi
tal p
harm
acie
s).
stud
ies.
Min
imum
requ
ired
size
ad
equa
te s
ampl
e si
ze (n
=2,
355)
was
Hos
pita
l pha
rmac
ies
shou
ld p
refe
rabl
yw
ill v
ary
depe
ndin
g on
the
rese
arch
ob
tain
ed fo
r the
pur
pose
s of
this
stu
dy.
use
the
sam
e ph
arm
acy
com
pute
r sys
tem
ques
tion.
(or p
rovi
de th
e sa
me
expo
rt re
cord
).
Conf
iden
tialit
yD
ata
need
to b
e an
onym
ous.
Patie
nt d
ata
wer
e no
t ano
nym
ousl
y U
se e
ncry
pted
pat
ient
iden
tifie
rsre
cord
ed in
ori
gina
l dat
abas
e, b
ut th
ey
(key
mus
t rem
ain
in o
rigi
nal d
atab
ase)
.w
ere
retr
ieve
d an
onym
ousl
y.
Gen
eral
isab
ility
/Po
pula
tion
has
to b
e re
pres
enta
tive
Popu
latio
n ha
d a
sim
ilar d
istr
ibut
ion
Nee
ds to
be
take
n in
to a
ccou
nt;
com
pare
re
pres
enta
tiven
ess
for D
utch
nur
sing
hom
e po
pula
tion.
of a
ge a
nd s
ex a
nd ty
pe o
f car
e al
thou
ghw
ith n
atio
nal d
ata
avai
labl
e fr
om S
IVIS
.th
ey w
ere
slig
htly
old
er th
an th
e D
utch
nurs
ing
hom
e po
pula
tion.
Drug
use
Accu
racy
, Al
l dru
gs th
at a
re re
gist
ered
in
Dat
a en
try
was
acc
urat
e, a
lthou
gh s
ome
Ensu
re d
ata
com
plet
enes
s an
d ac
cura
teco
mpl
eten
ess
med
icat
ion
data
base
for i
ndiv
idua
l in
accu
rate
dat
a an
d om
issi
ons
have
data
ent
ry b
y tr
aini
ng p
harm
acy
tech
nici
ans.
patie
nts
are
used
by
thos
e pa
tient
s,
been
foun
d (10
%).
and
all d
rugs
that
are
use
d by
in
divi
dual
pat
ient
s ar
e re
gist
ered
.
Det
aile
d in
form
atio
n on
W
hich
dru
g, in
whi
ch d
osag
e, in
whi
ch
Det
aile
d in
form
atio
n av
aila
ble,
dat
aTo
obt
ain
spec
ific
drug
dat
a su
ch a
sdr
ug, d
ose
and
dura
tion
freq
uenc
y is
take
n fo
r how
long
and
on
DD
D-a
nd A
TC-c
odes
wer
e ob
tain
edD
DD
-val
ues
and
ATC-
code
s, li
nkag
e w
ithby
whi
ch ro
ute
of a
dmin
istr
atio
n.th
roug
h re
cord
-lin
kage
with
nat
iona
l na
tiona
l dru
g da
taba
se (Z
-ind
ex) c
ould
dr
ug d
atab
ase.
be
nee
ded.
Cont
inui
tyLo
ngitu
dina
l dat
a ar
e re
quire
d to
Lo
ngitu
dina
l dat
a w
ere
avai
labl
eEn
sure
con
tinui
ty b
y ke
epin
g m
edic
atio
nst
udy
drug
use
ove
r a ti
me
sequ
ence
.fo
r 2-y
ear s
tudy
per
iod.
hist
orie
s fo
r lon
g pe
riod
of t
ime.
Dru
g in
take
In
form
atio
n re
gard
ing
med
icat
ion
Med
icat
ion
com
plia
nce
ensu
red
Non
eco
mpl
ianc
e is
requ
ired.
by n
ursi
ng h
ome
staf
f.
Oth
er s
ourc
es
Use
of O
TC-d
rugs
kno
wn.
OT
C-dr
ug u
se w
as c
onsi
dere
d ne
glig
ible
Non
e
References
1 Herings RMC. PHARMO: a record linkage system for postmarketing surveillance of prescription drugs in the
Netherlands [Thesis]. University of Utrecht, 1993.
2 Tobi H, Van den Berg PB, De Jong-van den Berg LTW The InterAction database: synergy of science and practice
in pharmacy. In: Brause RW, Hanisch E (ed) Medical data analysis: first international symposium;
proceedings/ISMDA. Berlin: Springer, 2000: 206-11
3 Integrated Primary Care Information (IPCI), Rotterdam, The Netherlands.
4 Lau HS, De Boer A, Beuning KS, Porsius A. Validation of pharmacy records in drug exposure assessment.
J Clin Epidemiol 1997; 50: 619-25.
5 Garcia Rodriguez LA, Perez Gutthann S. Use of the UK general practice database for pharmacoepidemiology.
Br J Clin Pharmacol 1998; 45: 419-25.
6 Leufkens HGM. Pharmacy records in pharmacoepidemiology: studies on antiinflammatory and antirheumatic
drugs [Thesis]. University of Utrecht, 1990.
7 De Vries CS. Collaboration in healthcare, the tango to drug safety [Thesis]. University of Groningen, 1998.
8 Heerdink ER. Clustering of drug use in the elderly. Population-based studies into prevalence and outcomes
[Thesis]. University of Utrecht, 1995.
9 Lau HS. Drug related problems in the elderly [Thesis]. University of Utrecht, 1998.
10 Avorn J, Gurwitz JH. Drug use in the nursing home. Ann Intern Med 1995; 123: 195-204.
11 Pies R, Gorman JM, Gorenstein EE, Katz IR, Rovner BW, Schneider L, Avorn J. Use of psychoactive drugs in
nursing homes. New Engl J Med 1992; 327: 1392-3.
12 Llorente MD, Olsen EJ, Leyva O, Silverman MA, Lewis JE, Rivero J. Use of antipsychotic drugs in nursing homes:
current compliance with OBRA regulations. J Am Geriatr Soc 1998; 46: 198-201.
13 Johnson RE, Vollmer WM. Comparing sources of drug data about the elderly. J Am Geriatr Soc 1991; 39: 1079-84.
14 Lamy PP. Institutionalisation and drug use in older aldults in the US. Drugs Aging 1993: 3: 232-7.
15 Gambassi G, Landi F, Peng L, Brostrup-Jensen C, Calore K, Hiris J, et al. Validity of diagnostic and drug data in
standardized nursing home residents assessments. Potential for geriatric pharmacoepidemiology.
Med Care 1998; 36: 167-79.
16 Claesson CB, Schmidt IK. Drug use in Swedish nursing homes. Clin Drug Invest 1998; 16: 441-52.
17 Koopmans RT, Van Rossum JM, Van den Hoogen HJ, Hekster YA, Willekes-Bogaers MA, Van Weel C.
Psychotropic drug use in a group of Dutch nursing home patients with dementia: many users, long-term use,
but low doses. Pharm World Sci 1996; 18: 42-7.
18 Koopmans RT, de Vaan HH, Van den Hoogen HJ, Gribnau FW, Hekster YA, Van Weel C. Changes in drug use
during a stay in a psychogeriatric nursing home (in Dutch). Ned Tijdschr Geneeskd 1994; 138: 1122-6.
19 Van Dijk KN, De Vries CS, Van den Berg PB, Brouwers JRBJ, De Jong-van den Berg LTW. Drug utilisation in
Dutch nursing homes. Eur J Clin Pharmacol 2000; 55: 765-71.
20 Van Dijk KN, De Vries CS, Van den Berg PB, Dijkema AM, Brouwers JRBJ, De Jong- van den Berg LTW.
Constipation as an adverse effect of drug use in nursing home patients: an overestimated risk. Br J Clin
Pharmacol 1998; 46: 255-61.
21 Van Dijk KN, De Vries CS, Van den Berg PB, Brouwers JRBJ, De Jong-van den Berg LTW. Occurrence of potential
drug-drug interactions in nursing home residents. Int J Pharm Pract 2001; 9: 45-52.
22 Van de Vijver DAMC, Stricker BHCh, Breteler MMB, Roos RAC, Porsius AJ, De Boer A. Evaluation of antiparkin-
sonian drugs in pharmacy records as a marker for Parkinson’s disease. Pharm World Sci 2001; 23: 148-52.
23 Boogaard RG, Dercksen H. Therapietrouw in het verpleeghuis. Een onderzoek in het kader van de opleiding tot
verpleeghuisarts. Verpleeghuis Het Zonnehuis, Zwolle, 1997 (report in Dutch).
4342
Tab
le 3
(co
nt.
): D
iscr
epan
cies
bet
wee
n da
ta re
quire
d fo
r pha
rmac
oepi
dem
iolo
gica
l res
earc
h an
d da
ta a
vaila
ble
in h
ospi
tal p
harm
acy
pres
crip
tion
data
base
s
Aspe
cts
Requ
irem
ents
for
Data
ava
ilabl
e in
hos
pita
l Re
com
men
datio
nsph
arm
acoe
pide
mio
logi
c re
sear
chph
arm
acy
pres
crip
tion
data
base
us
ed in
cur
rent
stu
dy
Outc
omes
Clin
ical
sta
tus
Info
rmat
ion
on d
iagn
oses
, bio
chem
ical
N
ot a
vaila
ble
Obt
ain
thes
e da
ta th
roug
h re
cord
-pa
ram
eter
s, la
bora
tory
test
resu
lts,
linka
ge w
ith p
hysi
cian
com
pute
r sys
tem
,dr
ug b
lood
leve
ls, d
iagn
ostic
test
resu
lts.
and
othe
r com
pute
r sys
tem
as
appl
icab
le.
Util
isat
ion
of h
ealth
car
eIn
form
atio
n on
util
isat
ion
of h
ealth
N
ot a
vaila
ble
Obt
ain
data
on
hosp
ital s
tay
by re
cord
-ca
re, s
uch
as h
ospi
tal c
are.
linka
ge.
Qua
lity
of li
feIn
form
atio
n on
qua
lity
of li
fe.
Not
ava
ilabl
eTh
is in
form
atio
n co
uld
be c
olle
cted
on
an
ad h
oc b
asis
.
Conf
ound
ers
Dem
ogra
phic
sAg
e, s
ex, s
ocio
-eco
nom
ic s
tatu
s,
Age
and
sex
Reco
rd d
ata
on li
fe s
tyle
fact
ors
such
as
smok
ing.
life
styl
e fa
ctor
s.
Indi
catio
n fo
r dru
g us
eIn
dica
tion
for d
rug
use,
N
ot a
vaila
ble
Reco
rd d
ata
on in
dica
tion
and
dise
ase
seve
rity
.di
seas
e-se
veri
ty.
Co-m
orbi
dity
Dis
ease
s th
at m
ight
be
asso
ciat
ed
Avai
labl
e th
roug
h re
cord
-lin
kage
Veri
fy p
harm
acy
data
and
SIV
IS d
ata
agai
nst
with
bot
h ex
posu
re a
nd o
utco
me.
with
SIV
IS-d
atab
ase.
each
oth
er. C
arry
out
a v
alid
atio
n st
udy
of e
ach
agai
nst m
edic
al c
hart
s.
Abstract
Objective: To quantify and evaluate drug utilisation in a sample of Dutch nursing homes.
Methods: A retrospective analysis of computerised medication data of 2,355 residents aged 65
and over of six nursing homes in the Netherlands was performed. For each therapeutic drug
group, the number of users was determined. The ten therapeutic groups used most frequently
were investigated further. For these, patient characteristics, use of therapeutic subgroups, the
average daily dosages, and the chronicity of drug use were determined. Chronicity was expres-
sed as the percentage of treatment days divided by the number of residents’ days in the nur-
sing home.
Results: During the study period, 89%, 77% and 56% of the study population used a drug
from ATC main group N (central nervous system), A (alimentary tract and metabolism) and C
(cardiovascular system), respectively. Eight of the ten therapeutic drug groups prescribed most
frequently were used for more than 50% of the time. In particular psycholeptic drugs, diurec-
tics, and laxatives were used chronically (83%, 81%, and 80% of the nursing home stay,
respectively). Except for a few drug groups such as laxatives and diuretics, the prescribed daily
dosages were relatively low. Twenty-eight percent of the residents received loop diuretics;
these were prescribed in relatively high dosages.
Conclusion: Drug utilisation in the nursing home was high and many drugs were used chroni-
cally. In view of the risk of possible side effects and drug-drug interactions, the prescribing and
dosage of psycholeptic drugs, laxatives, loop diuretics and ulcer-healing drugs should be re-
evaluated.
45
2.3 Drug ut i l i sat ion in Dutch nurs inghomes
K.N. van Dijk 1,2, C.S. de Vries 1,3, P.B. van den Berg 1, J.R.B.J. Brouwers 1,2,L.T.W. de Jong-van den Berg 1
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, Groningen
University Institute for Drug Exploration (GUIDE), University Centre for Pharmacy,
Groningen, the Netherlands2 Department of Clinical Pharmacy, Medical Centre Leeuwarden, Leeuwarden, the Netherlands3 Department of Pharmacoepidemiology, Postgraduate Medical School, University of Surrey,
Guildford, United Kingdom
A slightly modified version has been published in
European Journal of Clinical Pharmacology 2000; 55: 765-71
44
Methods
Study populat ion
The study was conducted in residents aged 65 and over from six nursing homes in the nor-
thern part of the Netherlands. Compared to a national register on nursing home residents in
the Netherlands (SIVIS), the study population had a similar distribution of gender and type of
care, although nursing home residents in our study were slightly older (average age 82 in our
study versus 79 in the SIVIS data) than the Dutch nursing home population. Physician care was
provided by nursing home physicians who give medical care on a daily basis. From interview
data it was shown that nursing, physician, and pharmacist care, as well as food and fluid inta-
ke were comparable between the nursing homes [31]. The source population consisted of a
dynamic cohort of 2,772 residents who were present at any time during the two-year study
period from 1 October 1993 to 1 October 1995. The average annual turnover rate (mostly due to
mortality) was 40%.
Data col lect ion
For each resident, pharmacy records and individual resident characteristics were collected.
Pharmacy data included the generic name, strength, dosage, the frequency of use, the route of
administration, and the duration of drug use (in days) of each prescribed drug. This type of data
has been demonstrated to be an adequate source of information of the prescription drugs taken
by elderly people [32,33]. Furthermore, age, gender, date of admission, and date of discharge
were collected from the pharmacy records. Pharmacy records were linked with a national
information system on nursing homes (SIVIS) [34], to collect patient specific data regarding
morbidity (three diagnoses per resident), and the type of care: psychogeriatric or somatic care.
Somatic residents usually are characterised by serious physiological chronic disorders, such as
Parkinson’s disease, diabetes mellitus and rheumatoid arthritis. Psychogeriatric residents are
characterised by serious mental or psychiatric disorders such as dementia. As a consequence,
pharmacotherapy between these groups will differ. Patient specific SIVIS data are collected
four times a year by the nursing home staff, and subsequently these data are registered ano-
nymously on a national basis.
Data handl ing
Residents whose pharmacy records could not be linked to data from the SIVIS system
(14.2%) and subsequently, residents whose period of residence could not be defined (1%) were
excluded. The excluded residents did not differ from the other residents with respect to age,
47
Introduct ion
Many studies have reported drug utilisation in nursing home residents to be disturbingly
high [1-3]. It has been estimated that of prescribed medication, approximately 25-40% is con-
sumed by people over 65 years of age [4,5]. When elderly people are admitted to nursing
homes, their medication use often increases [6,7]. In part this may be due to the reasons for
admission to the nursing home, however other studies suggest that the ‘prescribing cascade’,
i.e. the prescription of a new drug to counteract the side effects of another drug, could also be
a reason [8]. This may lead to an increased frequency of unnecessary polypharmacy and of pos-
sible adverse drug effects [9-11]. Since elderly people often suffer from multiple disorders and
consequently use many drugs, this population receives much emphasis. Also, the nursing home
environment provides an excellent opportunity for comprehensive drug regimen review [12].
Special programs have been developed and implemented to improve drug utilisation in the
community-dwelling elderly [13] and in nursing home residents [14,15]. Furthermore, several
groups have developed or applied prescribing indicators with the aim to create means to sys-
tematically assess medication appropriateness and thus a starting point to improve prescribing
[16-23]. Many of these indicators were developed for assessing medication appropriateness in
elderly outpatients, rather than nursing home residents. Beers’ criteria have been developed
for elderly outpatients in the United States and are not necessarily representative for European
pharmacotherapy standards. For example, according to Beers’ criteria trimethobenzamide,
reserpine, chlorpropamide and dicyclomine use is discouraged [16,23] but in many European
countries these drugs are not even available. Therefore, to measure prescribing of these drugs
in not a good indicator for medication appropriateness in Europe. Typically European issues,
for example, are the prescribing of diuretics, psychotropics and laxatives [24]. To improve pre-
scribing locally, an overview of current prescribing is needed. In Europe, only few studies have
been undertaken to describe and evaluate drug use in the elderly [25] and in nursing homes
[24,26-29] and few settings have the availability of computerised medication data to evaluate
drug utilisation continuously.
The purpose of this study was to evaluate and quantify drug utilisation in six Dutch nursing
homes. In relation to patient characteristics and published guidelines [30], frequency of drug
use, drug groups, daily dosages of relevant drug groups and chronicity of drug use are studied.
This study highlights areas that deserve attention in prescribing practices in nursing homes.
After identifying the pharmacotherapeutic domains that comprise the most serious problem
areas, feedback may be provided to improve pharmacotherapy.
46
gender or type of care. This resulted in a final study population of 2,355 residents.
Drugs were classified according to the Anatomical Therapeutic Chemical (ATC) classifica-
tion system [35]. Dosages were calculated as Defined Daily Dose (DDD) values, defined by the
World Health Organisation (WHO) [36], and Prescribed Daily Dose (PDD) values were deter-
mined for each prescribed drug. The PDD value is the daily dosage of drugs, expressed as the
daily number of DDDs that is actually prescribed; i.e. the daily dosage (in mg) divided by the
DDD (in mg) [37]. The fact that drug volume, or quantities of drug use, are expressed as a com-
parable unit of pharmacological efficacy rather than for example the number of milligrams or
prescriptions dispensed enables us to compare daily dosages between different drugs [38].
When the PDD is greater than 1, the prescribed daily dosage is relatively high relative to WHO
guidelines. A PDD less than 1 indicates a relatively low daily dosage. Drugs that do not have a
DDD value in the ATC-coding system such as injections and dermatological preparations and
drugs that were prescribed ‘as needed’ (5% of all prescriptions), were left out when average
PDD values were calculated.
Drug ut i l i sat ion
Initially, all prescribed main drug groups were analysed on the levels of ATC anatomical
main group (first level) and therapeutic group (second level). The number of users and the
number of users who used these drugs for more than 50% of the nursing home stay were
determined for these drug groups. Of the ten therapeutic categories prescribed most frequent-
ly, further analyses were performed on the level of therapeutic ATC subgroups (third level). For
each therapeutic ATC subgroup the chronicity of pharmacotherapy and average and median
PDD values were studied. To obtain insight in the duration of drug use in relation to the period
of stay in the nursing home, for each resident and for each drug the chronicity of pharmaco-
therapy was expressed as the number of drug utilisation days divided by the number of resi-
dents’ days in the nursing home. Software used was SPSS, version 6.01.
Results
Populat ion character is t i cs
The study population consisted of 2,355 nursing home residents. The mean age of the study
population was 82 years (SD 7.3). Figure 1 represents the duration of stay in the nursing home
for individuals in the study population. The average number of different drugs (based on ATC
codes; fifth level) per person was 8.9 (SD 4.9) during the total residence time; the average num-
ber of different drugs per day was 4.9. Other population characteristics are given in table 1.
48
250
200
150
100
50
0
300
350
400
0-2526-5051-7
576
-100
101-1
25126-15
0151-1
75176
-200201-2
25226-250251-2
75276
-300
301-3
2532
6-350
351-3
75376
-400401-4
25426-450451-4
75476
-500501-5
25526-450551-4
75576
-600601-5
25626-550651-6
75676
-700
701-7
30
Figure 1: Duration of stay in nursing home study population (n=2,355)
Table 1: Characteristics of the study population (n=2,355)
Variable Number of residents
(n) (%)
Age (years)
65-69 127 5
70-74 288 12
75-79 404 17
80-84 608 26
85-89 574 24
90-94 274 12
> 94 80 3
Gender
Male 689 29
Female 1666 71
Type of care
Psychogeriatric 700 30
Somatic 1609 68
Not known 46 2
Morbidity
Parkinson’s disease 151 6
Diabetes mellitus 176 7
Dementia 689 29
Depression 40 2
Duration of stay in the nursing home (months)
< 1 349 15
1-5 990 42
6-11 306 13
12-17 205 9
≥ 18 505 21
49
51
Drug consumption
Figure 2 represents the number of residents on drugs from the ATC anatomical main groups,
and the number of residents who received these drugs for more than 50% of their stay in the
nursing home. Anatomical main groups A (alimentary tract and metabolism), B (blood and
bloodforming organs), C (cardiovascular system), J (general anti-infective agents for systemic
use) and N (central nervous system) were the drug groups from which drugs were prescribed
most frequently and, with the exception of antibiotics, on average these drugs were used for
more than 50% of the stay in the nursing home. Gastrointestinal drugs used most frequently
were laxatives and ulcer-healing drugs: in this population, 56% used laxatives and 24% used
anti-ulcer medication. Drug use from group B consists mainly of anti-thrombotic drugs and
iron, drug use from group C consists mainly of cardiac drugs such as digoxin, anti-arrythmic
agents, diuretics and angiotensin-converting enzyme (ACE) inhibitors. Non-steroidal anti-
inflammatory drugs (NSAIDs) are the only drugs that were used from group M. Drug use from
group N consists mainly of analgesics, such as paracetamol and aspirin, benzodiazepines, and
antipsychotics. For the ten ATC main groups prescribed most frequently, drug consumption data
are represented in descending order of frequency in table 2. From this table, differences in pre-
scribing can be seen by gender (column 4 and 5) and by the type of care that residents receive
(psychogeriatric or somatic, column 6 and 7). With respect to gender, striking differences occur
in the utilisation of antipsychotic agents (ATC code N05A; more frequently used by men) and
pain medication (ATC code M01A and N02B; more frequently used by women). With respect to
the type of care, differences occur in psycholeptic prescribing for psychogeriatric and somatic
residents: psychogeriatric residents receive more antipsychotics (59% versus 23%), whereas
somatic residents receive more benzodiazepines (ATC code N05C). Next, we studied chronicity
and the daily dosage of drugs. The average chronicity of treatment (column 8) and average and
median PDD values (column 9 and 10) are given. From column 8, it can be seen that in these
nursing homes, most drugs are used for more than 50% of the nursing home stay. This inclu-
des diuretics and anti-coagulant drugs, but also laxatives, hypnotics and sedatives: on an aver-
age, 74% of the nursing home population use a psycholeptic drug for an average of 83% of
their stay in the nursing home. Adjusting these results for the duration of stay in the nursing
home had very little, if any, effect on the results. Leaving out the residents who only stayed for
a very short period in the nursing home did not alter the results significantly (both statistical
and clinical). Finally, drugs are given in relatively low dosages (column 9 and 10). Exceptions
are laxative drugs, antibiotics, diuretics, NSAIDs, and proton-pump inhibitors. In general the
mean PDD values are higher than the median PDD values.
50
0
500
1000
1500
2000
2500
A B C G H J L M N P R S V
ATC main group
Num
ber o
f res
iden
ts
Figure 2: Number of residents for each anatomical therapeutic chemical (ATC) main group; Dark bars show the number
of residents who use the drug for more than 50% of their duration of stay. A alimentary tract and
meta–bolism; B blood and bloodforming organs; C cardiovascular system, G genitourinary system; H hormo-
nal preparations; J general anti-infectives; L antineoplastic agents; M musculoskeletal system; N central ner-
vous system; R respiratory system; S sensory organs; V various agents
Discuss ion
In the nursing homes studied there is much long-term use of various medication types.
Generally, prescribed daily dosages are low, which indicates that prescribers are aware of the
pharmacokinetic and pharmacodynamic changes in the elderly. To our knowledge, no studies
have been performed that study chronicity of drug use for all drug groups in the nursing home.
This study shows that -with the exception of antibiotics- once they are on a certain drug, most
nursing home residents use these drugs for more than half of their stay in the nursing home.
Even though the authors are unaware of the complete morbidity of the individual residents and
utilisation is studied on a population basis, this raises the question whether the residents’ need
for a drug is re-evaluated from time to time. Psychogeriatric residents use approximately the
same drugs as residents in somatic care except for antipsychotics, anticoagulants and ulcer-
healing drugs. This indicates that, next to their psychological complaints, they have similar
somatic complaints as the other nursing home residents. The results with regard to the drug
groups studied will be reviewed in the following sections.
Psycholept ic drugs
Psycholeptic drugs are used by 74% of the nursing home residents. Since the nursing
homes offer psychogeriatric care facilities, utilisation of this drug group was expected in this
patient group. However, drug use from this group is more than 70% in both psychogeriatric and
in somatic care residents. A major part of this drug group are the benzodiazepines, especially
the hypnotics and sedatives. In accordance with prescribing guidelines in the Netherlands, the
prescribed daily dosages are about half the advised adult dosage [39]. However, Dutch prescri-
bing guidelines also state that it is suboptimal to prescribe these drugs for more than 30 days,
since the drugs become less effective but the adverse effects remain [39]. This part of the gui-
delines clearly is not followed. In view of previous studies that have demonstrated an increased
risk of falls and fractures in the elderly [40,41] and in view of other adverse effects such as
drowsiness that directly influence the residents’ quality of life, it would pay to re-evaluate the
need for these drugs in those residents. However, we are aware of the fact that withdrawal from
benzodiazepines is often difficult to achieve [42].
Laxat ive drugs
Constipation is a well-known complaint in the elderly. It could result from an impaired
bowel function due to immobility, decreased fluid- and fibre intake, and, sometimes, the use of
drugs with anti-cholinergic properties [31]. In this population, laxatives are used by more than
5352
Tab
le 2
: Num
ber o
f res
iden
ts fo
r the
top-
10 A
TC th
erap
eutic
mai
n gr
oups
in th
e st
udy
popu
latio
n (n
=2,
355)
ATC
code
Num
ber o
fM
ale/
Fem
ale/
Ty
pe o
f car
eCh
roni
city
‡Av
erag
eM
edia
nre
side
nts
1000
*10
00*
PDD
(SD)
PDD
Drug
gro
upn
(%)
PG† /
1000
som
atic
/100
0
Psyc
hole
ptic
s N
0517
33(7
4)73
973
579
170
9#0.
830.
71 (0
.54)
0.51
Psyc
hotr
opic
sN
05A
813
(35)
418
315#
589
231#
0.68
0.38
(0.5
1)0.
20An
xiol
ytic
sN
05B
665
(28)
296
277
363
243#
0.58
0.55
(0.5
9)0.
40H
ypno
tics
and
seda
tives
N05
C12
62
(54)
511
546
497
551
0.77
0.74
(0.2
8)0.
59
Laxa
tives
A06
1308
(56)
543
561
543
558
0.80
1.48
(0.9
5)1.0
9
Anal
gesi
csN
0212
41(5
3)49
254
152
452
50.
520.
51 (0
.27)
0.50
Opi
oids
N02
A33
9(1
4)15
813
813
315
20.
210.
33 (0
.21)
0.30
Oth
er a
nalg
esic
s an
d an
tipyr
etic
sN
02B
1112
(47)
415
496#
466
471
0.54
0.39
(0.2
9)0.
33
Antit
hrom
botic
age
nts
B01
1230
(52)
514
526
340
605#
0.85
0.96
(0.3
9)1.0
0
Antib
acte
rial
s fo
r sys
tem
ic u
seJ0
111
83(5
0)51
749
653
148
40.
121.1
4 (0
.42)
1.00
Diur
etic
sC0
397
3(4
1)38
342
637
443
1#0.
811.3
8 (1
.24)
1.00
Thia
zide
sC0
3A74
(3)
2235
2037
#0.
740.
96 (0
.31)
1.00
Loop
diu
retic
sC0
3C66
8(2
8)28
425
623
330
8#0.
761.4
8 (1
.42)
1.00
Oth
er37
2(16
)12
917
0#15
915
70.
720.
61 (0
.37)
0.50
Antii
nfla
mm
ator
y an
d M
0188
3(3
7)32
139
7#38
136
90.
491.0
3 (0
.49)
1.00
antir
heum
atic
pro
duct
s
Anta
cids
, dru
gs fo
r tre
atm
ent o
f pe
ptic
ulc
er a
nd fl
atul
ence
A02
563
(24)
263
229
173
265#
0.72
0.93
(0.4
8)1.0
0An
taci
dsA0
2A19
6(8
)87
8259
93#
0.54
0.51
(0.2
5)0.
50D
rugs
for t
reat
men
t of
pep
tic u
lcer
A0
2B43
7(19
)20
017
913
320
7#0.
751.0
2 (0
.43)
1.00
H2-
rece
ptor
ant
agon
ists
A02B
A34
1(1
4)15
714
011
315
6#0.
690.
87 (0
.25)
1.00
Prot
on p
ump
inhi
bito
rsA0
2BC
126
(5)
5852
2665
#0.
691.4
6 (0
.52)
1.16
Antia
nem
ic p
repa
ratio
nsB0
352
1(2
2)20
622
722
920
90.
640.
65 (0
.40)
0.67
Psyc
hoan
alep
tics
N06
406
(17)
144
184#
156
178
0.61
0.55
(0.3
8)0.
43Tr
icyc
lic a
ntid
epre
ssan
tsN
06AA
283
(12)
110
124
104
127
0.58
0.42
(0.2
5)0.
33SS
RIs||
N06
AB91
(4)
2843
3342
0.56
0.89
(0.2
6)1.0
0ot
her
68(3
)20
3244
22#
0.52
0.48
(0.2
8)0.
48
Lege
nd to
Tab
le 2
:*
mal
e/10
00: f
or e
ach
drug
cat
egor
y, th
e nu
mbe
r of m
ale
user
s of
this
cat
egor
y pe
r 100
0 m
ale
resi
dent
s; fe
mal
e/10
00: f
or e
ach
drug
cat
egor
y, th
e nu
mbe
r of f
emal
e us
ers
of th
is c
ateg
ory
per 1
000
fem
ale
resi
dent
s†
PG: p
sych
oger
iatr
ic c
are
‡ch
roni
city
was
def
ined
as
the
num
ber o
f tre
atm
ent d
ays
divi
ded
by th
e nu
mbe
r of p
atie
nt d
ays
in th
e nu
rsin
g ho
me
||SS
RIs:
sel
ectiv
e se
roto
nin
reup
take
inhi
bito
rs#
Stat
istic
ally
sig
nific
ant;
Chi-s
quar
e te
st, p
<0,0
5
tics in dosages that extend well beyond the low dose range. Ulcer-healing drugs form an inte-
resting group in this population: the average daily dosage increases with increasing effective-
ness of the prescribed drugs. Antacids are used by 8% of the population with an average PDD
of 0.51. Histamine H2-antagonists are used by 14% of the population with an average PDD of
0.87. Proton-pump inhibitors are used by 5% of the population with an average PDD of 1.46.
The reason why proton-pump inhibitors are prescribed in higher dosages than recommended
is unknown. Again, more in-depth study of individual patient records is needed: do prescribers
use antacids, H2-antagonists and H. pylori eradication optimally before the step towards pro-
ton-pump inhibitors is made? According to Dutch guidelines [30], the latter should be saved as
a final alternative; the prescribing pattern in this population suggests that this alternative may
be used to solve reflux and gastric acid complaints. Although they are used quite frequently,
prescribing of antianaemic drugs does not seem exceptional from the data available.
Antidepressant use, finally, stands out because many residents use tricyclic antidepressants
(TCAs). In the treatment of depression, the use of TCAs is questionable in the elderly in view of
unwanted adverse effects [30,43]. In some countries, the selective serotonin re-uptake inhibi-
tors (SSRIs) and monoamine oxidase inhibitors have superseded the TCAs for use in the elder-
ly [43], although data on their efficacy or adverse effects in frail, institutionalised elderly pers-
ons are inadequate [12]. Furthermore, TCAs are prescribed in relatively low dosages, which may
indicate that they are prescribed mainly for neuropathic pain disorders. All antidepressants are
prescribed for more than 50% of the time, which is considered rational in view of the time nee-
ded to establish an antidepressive effect [30].
L imitat ions
Biases, to which this study may be subject, all boil down to the completeness of the data.
All residents for whom data were suspected to be incomplete because they could not be linked
to the SIVIS database or because they were in the database but it was unclear when they ente-
red or left the nursing home, were excluded from the study. Since these residents did not differ
from the other residents with respect to demographic characteristics, no bias is expected from
exclusion of this subgroup. For the calculation of average PDD values, all medications which
had been prescribed ‘as needed’, were not included in the calculations. This concerned 5% of
the prescriptions; they were mainly hypnotics, analgesics and laxative drugs. It will lead to a
slight underestimation of the average PDD calculated for the drug groups concerned.
Calculations of the number of users and chronicity of drug use will not be affected. Other than
that, the data are complete with respect to prescription drug utilisation: the database origina-
tes from a drug-dispensing database and, therefore, it is continuously updated and kept ade-
55
half of all residents for 80% of the nursing home stay. Like other studies, our data suggest that
laxatives are overused in institutions [43]. Although non-pharmacological approaches such as
adequate fluid- and fibre intake and physical exercise are considered beneficial [44], these
approaches are often difficult to perform in the nursing home population. To decrease laxative
use and improve bowel function in the institutionalised elderly, an individual approach that
considers both pharmacological and non-pharmacological interventions is recommended.
Analges ics
The analgesic prescribed most frequently is paracetamol. Again, low dosages are given. As
expected, opiates are given less frequently and less chronically than non-opioid analgesics,
which seems rational [30]. Opioids may be helpful for relieving moderate to severe pain, espe-
cially nociceptive pain [45]. NSAIDs are used by 37% of the residents, which is 10% less than
use of analgesics such as paracetamol. This seems appropriate [46], especially when the
NSAIDs are used as alternatives when paracetamol is not effective enough (e.g. for rheumatoid
arthritis). The average PDD value is 1.03, which, in view of the risk of hypertension and renal
failure, is quite high for the elderly [47,48]. If prescribers are unaware of this, a reminder could
be given that paracetamol may be given in higher dosages (4 g/day maximum).
Other drugs
As expected, anticoagulant drugs are given chronically. Fifty percent of the nursing home
residents use an antibiotic drug at any time during their stay; they are not used chronically, and
they are given in normal daily dosages (PDD=1) which usually also is desirable in this popula-
tion. Use of diuretic drugs is more surprising: together with beta-blockers, thiazides are the
drugs of first choice for the treatment of secondary hypertension [30]. They are used by 3% of
nursing home residents; 16% use ‘other diuretics’ that include combination preparations of
thiazides and potassium-sparing agents such as triamterene. Many more residents use loop-
diuretics, the use of which in the elderly has been discouraged because of too severe blood
pressure lowering resulting in needless postural hypotension, and, in some cases, stroke [49].
The average PDD value is 1.48, which indicates that the drugs are prescribed in relatively high
dosages. This calls for more in-depth research as to why prescribers choose loop diuretics so
often, why they are prescribed in such high dosages and whether prescribers are aware of the
advice to be cautious with loop diuretics prescribing in the elderly. Furthermore, the average
dose of thiazides is surprising. Considerable evidence supports the efficacy of low doses of
thiazide diuretics in the treatment of hypertension in elderly people [30,50]. The average PDD,
found in the study presented here (0.96), suggests that nursing home residents receive diure-
54
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who received loop diuretics was relatively high (28%) and, again, relatively high dosages were
used. Feedback to prescribers is necessary to evaluate the necessity of this practice. In view of
possible adverse effects, the possibility of parallel prescribing and drug-drug interactions, the
use of these drug groups should be re-evaluated carefully.
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factor? Am J Med 1997; 102: 208-15.
48 Gurwitz JH, Avorn J, Bohn RL, Glynn RJ, Monane M, Mogun H. Initiation of antihypertensive treatment during
58
Abstract
Objective: It has been suggested that elderly people are at an increased risk of drug-related
problems such as drug-induced adverse effects, drug-drug interactions and drug-disease inter-
actions. This is particularly the case for nursing home residents because of the often complica-
ted and multiple co-morbidity that occurs in these people. The aim of this study was to deve-
lop prescribing indicators to assess systematically the occurrence and nature of potential drug-
drug interactions (DDIs) in a cohort of Dutch nursing home residents.
Methods: The study was conducted in residents aged 65 years and over in six nursing homes
(n=2,355, two-year study period). Computerised medication data for the residents were evalu-
ated with respect to co-prescribing of potentially interacting drugs. All DDIs that were classi-
fied as clinically relevant according to the Dutch National Drug Interaction Database were stu-
died. DDIs were classified into three categories according to their pharmacological mechanism:
1- pharmacokinetic interactions at the level of gastrointestinal (GI) absorption, 2- pharmacoki-
netic interactions at the level of metabolism and excretion and 3- pharmacodynamic interac-
tions.
Results: Thirty-two percent (n=748) of all residents were exposed to one or more combinations
of drugs that could lead to clinically adverse outcomes. The numbers of residents who received
drug combinations with a mechanism of interaction from category 1, 2 or 3 were 73 (3%), 164
(7%) and 612 (26%) respectively. The number of medications prescribed was significantly
associated with the occurrence of a potential DDI (p<0.05). Drug groups most frequently invol-
ved were oral anticoagulants, antibiotics and theophylline.
Conclusion: During the two-year study period, about one-third of the residents were exposed
to at least one drug interaction considered clinically relevant. Adequate surveillance systems
are needed to enable better identifications of these interactions with a view to preventing
potential problems. Using the prescribing indicators developed in this study, such surveillance
could focus on detection and clinical aspects of potential DDIs and possible alternative treat-
ments.
61
2.4 Occurrence of potent ia l drug-druginteract ions in nurs ing home res idents
K.N. van Dijk 1,2, C.S. de Vries 1,3, P.B. van den Berg 1, J.R.B.J. Brouwers 1,2,L.T.W. de Jong-van den Berg 1
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, Groningen
University Institute for Drug Exploration (GUIDE), University Centre for Pharmacy,
Groningen, the Netherlands2 Department of Clinical Pharmacy, Medical Centre Leeuwarden, Leeuwarden, the Netherlands3 Department of Pharmacoepidemiology, Postgraduate Medical School, University of Surrey,
Guildford, United Kingdom
International Journal of Pharmacy Practice 2001; 9: 45-52
60
as the number of patients with DDIs per total number of patients [29]. Consequently, the use of
a more uniform way of expressing the incidence or prevalence of DDIs has been recommended
[29]. The duration of concomitant drug use also needs to be taken into account: long-term con-
comitant use may indicate that in daily clinical practice no problems have occurred.
This study aimed to use computerised pharmacy prescription data to develop prescribing
indicators for DDIs and measure their occurrence, nature and duration in a sample of Dutch
nursing home residents. Such prescribing indicators could subsequently be used to audit pre-
scribing practices in the elderly and to monitor potential adverse drug reactions arising from
DDIs.
Methods
Potent ia l drug-drug interact ions
Information on the relevance of potential DDIs was obtained from the Dutch National Drug
Interaction Database [35]. Where available, the DDI information in this database is derived
from international reference books, such as Hansten and Stockley [36,37]. Based on these
books, in the database DDIs are divided into ‘clinically relevant’ DDIs (n=175) and ‘clinically less
relevant’ DDIs (n=74). A ‘clinically relevant’ DDI indicates that immediate action should be
taken, such as adjustment of dosage regimes, or suggesting an alternative drug to the prescri-
ber. For a DDI that is ‘clinically less relevant’ no immediate action is necessary. Only the 175 cli-
nically relevant DDIs were studied. DDIs were classified into three categories according to
pharmacological mechanism: (1) pharmacokinetic DDIs at the level of gastrointestinal absorp-
tion, (2) pharmacokinetic DDIs at the level of metabolism and excretion, and (3) pharmacody-
namic DDIs. The classification is shown in table 2.
Data col lect ion
The study was conducted among residents aged 65 and over in six nursing homes, each
with a 90 to 225-bed capacity. The study population consisted of 2,355 residents present at any
time during the two-year study period (1 October 1993 to 1 October 1995). In this study, phar-
macy records of the nursing home residents were linked with a national information system on
nursing homes (SIVIS), which contains patient-specific information on morbidity and type of
nursing (psychogeriatric or somatic) of virtually every nursing home resident in the
Netherlands. The databases and drug use in this population have been described in detail
[38,39]. The drug dispensing system in the nursing homes results in medication prescribing
being recorded in the pharmacy computer system and updated with medication changes on a
63
Introduct ion
Elderly people are at an increased risk of drug-related problems such as drug-induced
adverse effects, drug-drug interactions and drug-disease interactions [1-5]. This is particularly
the case for nursing home residents because of the often complicated and multiple co-morbi-
dity that occurs in these people [5-9]. To address this problem, several approaches have been
made towards rational and appropriate prescribing of drugs in the institutionalised elderly.
Prescribing indicators have been developed or applied with the aim of systematically assessing
medication appropriateness and thus providing a starting point for improvement of prescribing
[10-23]. One of the prescribing indicators used is the occurrence of potentially inappropriate
drug combinations [15,20-23]. A drug-drug interaction (DDI) is defined as ‘a pharmacological or
clinical response to the administration of a drug combination different from that anticipated
from the known effects of the two agents when given alone’ [24]. DDIs can lead to unintended
responses such as enhanced or reduced drug effects and these effects vary between individu-
als. Drug-drug interactions are commonly divided into pharmacokinetic and pharmacodynamic
DDIs [25,26]. Elderly people may be at a higher risk of the adverse effects of a DDI due to phar-
macokinetic, pharmacodynamic, and disease-related changes that occur with advanced age
[2,3,5]. Risk factors associated with the occurrence of potential DDIs are age, number of medi-
cations prescribed, the number of physicians involved, and the presence of increased frailty [2].
The impact of DDIs in nursing homes is reported to be high: DDIs may account for 22% of all
adverse drug reactions reported, or adversely affect 23% to 53% of all residents [5]. The inci-
dence of hospital admissions caused by DDIs has been reported to range from 0% to 11.5%
[27,28]. These percentages vary due to differences between study populations, use of different
definitions of a DDI, different ways of establishing cause-and-effect relationships, and diffe-
rences in study design.
Several studies have been published on frequencies of potential DDIs in different popula-
tions [28-34]. In a review of 7 studies, the frequency of potential DDIs was found to range from
9.2% to 70% in ambulatory patients [29]. In hospitalised patients, reports of frequencies of
potential DDIs ranged from 2.2% to 60% [28,29]. In nursing home residents, the proportion of
residents exposed to a potential DDIs has been reported to range from 23.4% to 49% [29].
These large variations in reported frequencies are mainly due to methodological considera-
tions, such as the use of different definitions of a clinically significant DDI and use of different
reference lists for DDIs (sometimes no references are given), differences in study design, and
differences in the way of measuring the frequency or incidence of DDIs. For example, the fre-
quency of DDIs can be expressed as the total number of DDIs per total number of patients or
62
ses it was found that the following variables were associated with the occurrence of a DDI:
number of medications prescribed, the type of nursing psychogeriatric or somatic) and
Parkinson’s disease.
In Table 2 detailed information is presented on the potential DDIs. The DDIs to which most
residents were exposed comprised the interaction between loop diuretics and non-steroidal
anti-inflammatory drugs (NSAIDs), and the interaction between oral anticoagulants and
NSAIDs (9.7% and 9.6% respectively). All other DDIs found in this study occurred in less than
5% of the study population. The percentage of index drug users most frequently exposed to a
potential DDI were users of ACE-inhibitors, fluoride, oral anticoagulants, acetazolamide, phe-
nytoin, and loop diuretics.
Table 3 provides an overview of the drugs most commonly involved in prescribing of inter-
acting drugs. The index drugs used most often were oral anticoagulant drugs (involved in ten
DDIs), antibiotics (involved in four DDIs), and theophylline (involved in three DDIs). Interacting
drugs most frequently involved were those with metallic ions (iron salts, antacids; involved in
five DDIs), NSAIDs (involved in three DDIs), diuretics, enzyme inducers (such as carbamazepi-
ne and phenytion), and enzyme inhibitors (such as verapamil and fluoxetine) (all involved in
two DDIs). The number of days that drug combinations were prescribed concomitantly is rela-
tively high. Nineteen out of 32 DDIs were prescribed for an average of 50 days or more per 100
days of index drug use.
daily basis. Nurses dispense medication to individual nursing home residents on the basis of
the information (drug, dosage, route and time of administration) in this computer system. As a
consequence, the recording of actual drug use can be considered very accurate. At the time of
the study there was no automatic signal generation by the system when potential DDIs occur-
red, but nursing home physicians and hospital pharmacists checked medication files on a daily
basis for medication changes.
Data analys is
A retrospective cohort study was performed to estimate the prevalence of potential DDIs
and the possible risk factors associated with the occurrence of potential DDIs. We developed
prescribing indicators based on the frequency, nature and duration of DDIs. For each DDI, an
index drug and an interacting drug were defined. The index drug was defined as a drug of which
the pharmacological or clinical response is altered by the interference of a second drug (the
interacting drug). The outcome of interest was the occurrence of a potential DDI, defined as the
concomitant use of both index drug and interacting drug for at least one day. The number of
residents exposed to each individual DDI was calculated. The prevalence of the DDIs was
expressed as the number of residents exposed to a DDI divided by the number of index drug
users. We also calculated the percentage of all residents (n=2,355) affected. For all patients on
a specific DDI, the number of days that the interacting drugs were prescribed concomitantly per
100 days of index drug use was calculated. A stepwise logistic regression was performed to
determine predictive variables of the prescribing of interacting drugs likely to cause adverse
clinical effects. The statistical software program SPSS 9.0 for Windows (SPSS Inc., Chicago, IL)
was used.
Results
Thirty-two per cent (n=748) of all residents were exposed to one or more combinations of
drugs that could potentially lead to adverse clinical outcomes. Out of 175 clinically relevant
DDIs from the interaction database, 32 drug combinations (18%) were prescribed; the other 143
did not occur in this population. Most DDIs found were based on pharmacodynamic mecha-
nisms. The number and percentage of all residents that received a drug combination from cate-
gory 1 (level of GI-absorption), 2 (level of metabolism and excretion) or 3 (pharmacodynamic
level) was 73 (3%), 164 (7%) and 612 (26%), respectively. Table 1 presents differences between
residents with and without DDIs with regard to age, gender, type of nursing, morbidity, and
number of different medications prescribed. From the multivariable logistic regression analy-
64 65
6766
Table 1: Characteristics of the study population (n=2,355)
Variable Number of residents Number of residents ORcrude for DDI ORadjusted¶
with DDI (%) without DDI (%) (95% CI) (95% CI)(N=748) (N=1607)
Age (years) 82 (sd 7) 82 (sd 7) 0.99 (0.98-1.00) 0.99 (0.98-1.00)Gender
Female 549 (73.4) 1117 (69.5) 1 (reference) 1 (reference)Male 199 (28.9) 490 (30.5) 0.82 (0.68-1.00) 0.81 (0.65-1.00)
Type of nursingPsychogeriatric 142 (19.0) 558 (34.7) 1 (reference) 1 (reference)Somatic 592 (79.1) 1017 (63.2) 2.29 (1.85-2.82) 2.34 (1.87-2.94)Not known 14 (1.9) 32 (2.0)
MorbidityParkinson’s disease 29 (3.9) 122 (7.6) 0.49 (0.32-0.74) 0.29 (0.18-0.46)Diabetes mellitus 74 (9.9) 102 (6.3) 1.62 (1.19-2.22) 1.19 (0.84-1.68)Dementia 144 (19.3) 545 (33.9) 0.47 (0.38-0.57) 0.72 (0.52-1.00)Depression 10 (1.3) 30 (1.9) 0.71 (0.35-1.47) 0.53 (0.24-1.17)
Number of different medications prescribed during study period0-4 33 (4.4) 388 (24.1) 1 (reference) 1 (reference)5-9 264 (35.3) 744 (46.3) 4.17 (2.85-6.11) 4.29 (2.92-6.30)10-14 251 (33.6) 376 (23.4) 7.85 (5.32-11.59) 8.58 (5.78-12.73)15 or more 200 (26.7) 99 (6.2) 23.75 (15.46-36.49) 27.21 (17.48-42.35)
¶ adjusted for type of nursing, Parkinson’s disease, and number of different medications prescribed
Table 2: Characteristics of potential DDIs, listed per category of interaction
Index drug‡ (number of users) Interacting drug† (group) Number of % of study % of days Clinical effect of DDIindex drug population of conco-users with (n=2355) mitant drug DDI (%) use§
Category 1 (reduced GI-absorption)
Doxycycline (349) Metallic ions (iron salts) 27 (8) 1.1 88 Risk of subtherapeutic doxycycline serum concentration.
Fluoroquinolones (121) Metallic ions (antacids and iron salts) 18 (15) 0.8 95 Risk of subtherapeutic fluoroquinolone serum concentration.
Thyroid hormones (81) Metallic ions (iron salts) 18 (22) 0.8 42 Risk of inadequate control of hypothyroidism.
Bisphosphonates (48) Metallic ions (antacids, calcium salts and iron) 7 (15) 0.3 34 Risk of inadequate prevention of osteoporosis.
Fluoride (18) Metallic ions (antacids and calcium salts) 7 (39) 0.3 95 Risk of inadequate prevention of osteoporosis.
Oral anticoagulants (646) Bile acids sequestrants 1 (<1) <0.1 100 Reduced effect of oral anticoagulant.
Cefuroxime (8) Drugs for the treatment of peptic ulcer 1 (13) <0.1 100 Risk of subtherapeutic cefuroxime serum concentration.
Total number of residents with category 1 DDI: 73 (3% of study population)
Category 2 (metabolism and excretion)
Oral anticoagulants (646) Co-trimoxazole 42 (6.5) 1.8 4.4 Increased effect of oral anticoagulant due to enzymatic inhibition and protein displacement. Risk of prolonged bleeding time.
Oral anticoagulants (646) Enzyme inducers£ 32 (5) 1.4 60 Decreased effect of oral anticoagulant due to induction of hepatic metabolism.
Digoxin (306) Verapamil 25 (8) 1.1 62 Verapamil inhibits non-renal and renal excretion of digoxin, leading to digoxin intoxication.
Doxycycline (349) Enzyme inducers£ 24 (7) 1.0 95 Decreased effect of doxycycline due to enzyme induction. Risk of subtherapeutic antimicrobial serum concentration.
Phenytoin (74) Co-trimoxazole 21 (28) 0.9 2 Increased effect of phenytoin. Risk of toxic effects such as nys-tagmus, diplopia and dizziness.
Oral anticoagulants (646) Tamoxifen 8 (1.2) 0.3 100 Increased effect of oral anticoagulant due to inhibition of hepatic metabolism. Risk of prolonged bleeding time.
Carbamazepine (87) Enzyme inhibitors¶ 6 (7) 0.3 51 Increased effect of carbamazepine due to reduced clearance. Risk of toxic effects such as drowsiness, nausea.
Theophylline (70) Fluoroquinolones 7 (10) 0.3 8 Increased risk of theophylline toxicity due to inhibition of hepatic (cytochrome P450 1A2) metabolism.
6968
Tricyclic antidepressants (283) SSRIs# 4 (1) 0.2 73 Increased risk of TCA toxicity due to inhibition of hepatic (cytochrome P450 2D6) metabolism
Lithium (14) NSAIDs¥ 3 (21) 0.1 48 Increased risk of lithium toxicity due to decreased lithium excretion.
Theophylline (70) Erythromycin 2 (3) <0.1 4 Increased risk of theophylline toxicity due to inhibition of hepatic metabolism
Oral anticoagulants (646) Amiodarone 2 (<1) <0.1 100 Increased effect of oral anticoagulant due to inhibition of hepatic metabolism. Risk of prolonged bleeding time.
Oral anticoagulants (646) Cimetidine 1 (<1) <0.1 100 Increased effect of oral anticoagulant due to inhibition of hepatic metabolism. Risk of prolonged bleeding time.
Theophylline (70) Enzyme inhibitors¶ 2 (3) <0.1 18 Increased risk of theophylline toxicity due to inhibition of hepatic metabolism.
Oral anticoagulants (646) Metronidazole 1 (<1) <0.1 4 Increased effect of oral anticoagulant due to inhibition of hepatic metabolism. Risk of prolonged bleeding time.
Total number of residents with category 2 DDI: 164 (7% of total study population)
Category 3 (pharmacodynamic interaction)
Loop diuretics (668) NSAIDs¥ 229 (34) 9.7 41 Decreased effect of diuretics due to reduction in renal perfusionand glomerular filtration.
Oral anticoagulants (646) NSAIDs¥ 225 (35) 9.6 41 Inhibition of platelet aggregation leading to increased bleeding risk and risk of peptic ulcer.
ACE-inhibitors (208) Diuretics 197 (95) 8.4 89 Blocking the activated renine-angiotensione-aldosterone-system leads to increased vasodilatation and strong hypotensive effects.
NSAIDs (883) Corticosteroids 78 (9) 3.3 55 Increased risk of peptic ulcer.
Oral anticoagulants (646) Salicylates (low dose) 34 (5.3) 1.4 65 Irreversible inhibition of platelet aggregation leading to increased bleeding risk and risk of peptic ulcer.
Oral anticoagulants (646) Thyreoid hormones 21 (3.3) 0.9 87 Increased thyroid function leading to increased response to oralanticoagulant.
Beta-blocking agents (167) Verapamil/diltiazem 16 (10) 0.7 94 Risk of diminished atrioventricular conductance and reduced contractility of the heart.
Potassium-sparing diuretics Potassium (salts) 14 (4) 0.6 38 Increased risk of hyperkalaemia.(364)
Hypoglycaemic agents (356) Beta-blocking agents 7 (2) 0.3 50 Reduced awareness of hypoglycaemia.
Acetazolamide (8) Diuretics 3 (38) 0.1 4 Increased risk of hypokalaemia.
Total number of residents with category 3 DDI: 612 (26% of total study population)
Table 3: D
rug groups most com
monly involved in prescribing of interacting drugs
Drug (group)N
umber of
Num
ber of patients %
of days of
DDIs implicated
(n=2355)
concomitant drug use
§
Index drug (group) ‡
Oral anticoagulants
10304
61
Antibacterial drugs4
6493
Theophylline3
1110
Interacting drug group†
Metallic ions
571
53
NSAID
s ¥3
397 41
Diuretics
2199
56
Enzyme inducers £
252
79
Enzyme inhibitors ¶
28
44
Legend to Table 3:§
expressed as the number of days of concom
itant drug use per 100 days of index drug use‡
defined as the drug of which the pharm
acological or clinical response is altered by the interference of a second drug, the interacting drug†
defined as the drug that influences the pharmacological or clinical response of the index drug
¥
non-steroidal anti-inflamm
atory drugs£
carbamazepine, phenobarbitone, phenytoin, rifam
picin¶
fluoxetine, fluvoxamine, verapam
il, diltiazem
Legend to Table 2: ‡
defined as the drug of which the pharm
acological or clinical response is altered by the interference of a seconddrug, the interacting drug
†defined as the drug that influences the pharm
acological or clinical response of the index drug§
expressed as the number of days of concom
itant drug use per 100 days of index drug use £
carbamazepine, phenobarbitone, phenytoin, rifam
picin ¶
fluoxetine, fluvoxamine, verapam
il, diltiazem#
selective serotonin reuptake inhibitors¥
non-steroidal anti-inflamm
atory drugs
For individual DDIs the percentage of residents affected was 1% or less. Interactions in this
category are almost always clinically relevant because complex formation leads to subthera-
peutic concentrations of drugs. Sometimes an alternative drug for the interacting drug can be
chosen (for example an H2-antagonist instead of an antacid), and sometimes the interacting
drug can be discontinued temporarily during index drug administration (for example iron salts
during doxycycline therapy).
Practical implications
The DDIs of this category should be avoided by separating the administration times of the
two interacting drugs by a two- to four-hour interval. In practice, this measure is taken fre-
quently, however exact data on the administration times of the drugs in the study population
were not available. A computerised adjustment of dosage schedules could support appropria-
te timing of administration.
Pharmacokinetic DDIs at the level of metabolism and excretion
The DDIs due to metabolism or excretion (category 2) are often considered clinically rele-
vant, in particular those involving inhibition of cytochrome P450 (CYP450) isoenzymes. Overall,
7% of the residents in our study were exposed to one or more of the DDIs from category 2. For
individual DDIs the percentage of residents affected was 3.4% or less. The highest prevalences
were found for the potentially increased anticoagulant effect of acenocoumarol or phenpro-
coumen by the concomitant use of co-trimoxazole and enzyme-inducers, respectively. For these
DDIs, monitoring of the bleeding time (International Normalised Ratio, INR) is warranted, and
this is common practice in Dutch nursing homes. Among the index drug users, the DDIs most
frequently observed were the interaction between phenytoin and co-trimoxazole (28% of phe-
nytoin users) and the interaction between lithium and NSAIDs (21% of lithium users). The lat-
ter may be clinically important, since renal function deteriorates with age and puts elderly at
an increased risk of lithium toxicity. Alternative drugs could be advised, such as paracetamol
instead of NSAIDs and doxycycline instead of co-trimoxazole. Eight out of 15 DDIs from cate-
gory 2 had 50 days or more of concomitant drug use per 100 days of index drug use. This could
mean that, in practice, these DDIs do not lead to clinical problems. Further investigation is nee-
ded to find out if more intense monitoring of drug therapy by means of therapeutic drug moni-
toring is necessary.
Practical implications
The DDIs of this category can be monitored by measuring serum (index) drug levels, for
example digoxine, theophylline, and lithium levels. For the DDIs in which oral anticoagulants
are involved, measuring bleeding time (INR) is warranted.
71
Discuss ion
In this study we developed prescribing indicators, based on the occurrence, frequency and
duration of drug-drug interactions, to describe DDIs in a cohort of nursing home residents.
Although the prescribing indicators were descriptive in nature, they allowed us to identify the
drug groups most frequently involved and residents who were most at risk for being exposed
to a DDI. Thirty-two percent of the nursing home residents were exposed to at least one DDI
classified as potentially clinically relevant. Of all residents, 26% were exposed to a DDI from
category 3 (pharmacodynamic interaction), which could be relevant especially in this popula-
tion because of reduced homeostatic mechanisms. For each individual DDI classified as clini-
cally relevant, no more than 10% of the residents were affected. This means that although one-
third of the population was exposed to a DDI, there was no specific DDI that could be identified
as potentially harmful to many residents. The more drugs residents were prescribed, the hig-
her became their risk of having a DDI. This is in line with other studies [28,30]. Residents who
were nursed in psychogeriatric wards and residents who were diagnosed with Parkinson’s dis-
ease showed a decreased risk for the occurrence of a DDI. Other risk factors were not identified
in this study.
Other studies have reported the frequency and nature of potential DDIs in nursing home
residents [29-34], although the only study published after 1990 was that of Bergendal [30]. In
their study, potential DDIs were investigated in 5125 mainly elderly patients in nursing homes
and homes for the elderly in Sweden. They found that 31% of the patients had at least one DDI
and that these patients were prescribed significantly more drugs than those without DDIs. The
prescribing indicators we present are descriptive in nature and can be used with prescription
databases. Williams and colleagues [20] developed an index of quality prescribing in general
practice by investigating the incidence of potential DDIs. They determined an odds ratio as a
measure of potential DDIs avoided, comparing the use of cimetidine (which interacts with
many other drugs) with that of non-interacting H2-antagonists in users and non-users of inter-
acting drugs (warfarine, theophylline, phenytoin). Although this is an elegant method of audi-
ting H2-antagonists prescribing, it is not by definition applicable to all other drug groups.
Furthermore, drug formularies in nursing homes usually restrict the choice of drugs from a spe-
cific drug class.
Impl icat ions for c l in ica l pract ice
Pharmacokinetic DDIs at the level of gastrointestinal absorption
Overall, 3% of all residents were exposed to DDIs at the level of GI-absorption (category 1).
70
profile, staff pharmacists detected only 20% of the DDIs. The authors recommended compute-
rised drug interaction profiles to be used by pharmacists to ensure recognition of all potential
DDIs. In another study, the value of electronic prescribing for elderly was highlighted [41]. In
particular, on-line detection of DDIs during prescribing and suggestion of non-interacting drugs
could be useful. Recently, national attention has been given to pharmaceutical activities in
Dutch nursing homes. To assess the quality of the medication distribution process and other
pharmaceutical activities, in 1997 the Dutch Health Care Inspectorate carried out a survey
among 33 Dutch nursing homes [42]. A computerised medication surveillance system was ope-
rational in only 9 out of 33 nursing homes. Together with the results of our study this indicates
that computerised detection of DDIs in nursing homes is warranted. Furthermore, more insight
is needed into the clinical relevance of those DDIs classified as ‘clinically relevant’. The fact that
in this study long-term concomitant use of interacting drugs was found, raises the question of
whether clinically relevant side effects are actually seen in the elderly. The prescribing indica-
tors developed in this study provide the tools to audit DDI occurrence in nursing homes syste-
matically.
Acknowledgements
We wish to thank R. Fijn, pharmacist, for his comments on the manuscript. We express our
gratitude to D.A. Bloemhof, hospital pharmacist, for supplying pharmacy data, SIG Informatics
on Health and Welfare, Utrecht, for supplying morbidity data; and nursing, medical and phar-
macy staff from all participating nursing homes for their co-operation. The Wetenschappelijk
Instituut Nederlandse Apothekers (WINAp) financially supported this study.
73
Pharmacodynamic DDIs
Pharmacodynamic interactions are of particular relevance to the elderly [2]. In this study
26% of all residents were exposed to one or more pharmacodynamic DDIs (category 3). The
highest prevalence was found for the interaction between loop diuretics and NSAIDs. This
interaction may be clinically relevant to the elderly, in view of the reduced effectiveness of the
diuretics. Furthermore, the NSAID-induced reduced renal function may influence other drug
therapies. The next interaction seen most often was that between oral anticoagulants and
NSAIDs, which could lead to an increased risk of bleeding and risk of peptic ulcer. Choosing an
alternative analgesic is an option to avoid this interaction. Among the index drug users, the
DDIs most frequently observed were the combination between ACE-inhibitors and diuretics
(95% of ACE-inhibitor users) and again the combination of oral anticoagulants and NSAIDs
(35% of oral anticoagulant drug users). The combination of ACE-inhibitors with diuretics is
only relevant when ACE-inhibitor use is initiated without a temporary discontinuation of the
diuretic (or the use of a low first dosage of ACE-inhibitor). Feedback on this should be given to
prescribers.
Practical implications.
The clinical relevance of DDIs of this category can be assessed by monitoring the clinical
effects of index drugs, e.g. the decreased effect of loop diurectics that (possibly) results in
symptoms of heart failure. Measuring serum potassium levels is warranted in view of the
hyper- or hypokalaemic effects of some DDIs of this category.
Cl in ica l re levance
Tamai and colleagues [31] found that half of the 24 suspected interactions in a group of 138
nursing home residents had potential clinical relevance. However only two residents were
exposed to a substantial degree of risk. Foxall [34] reported 67 potential DDIs in a group of 106
nursing home patients, of which 25 DDIs were potentially life-threatening. Doucet [28] found
that in 14.5% of hospital patients older than 70 years who were exposed to a potential DDI, a
documented side effect was found. Although data on the clinical relevance of DDIs are scarce,
it is estimated that 10% of potential DDIs result in clinically significant events [2]. This would
imply that 3% of our study population, i.e. 71 residents, would be at risk of a clinically signifi-
cant event.
Several methods can be used to alert prescribers and pharmacists to the occurrence of
potential DDIs. Electronic or computer-aided prescribing can be a useful tool in the recognition
and handling of DDIs. Recently, Weideman and colleagues [40] showed that in an eight-drug
72
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32 Lang LA, Kabat HF. Drug interactions in nursing home patient prescriptions. J Am Pharm Assoc 1970; 10: 647-7.
33 Cooper JW, Wellins I, Fish KH, Loomis ME. Frequency of potential drug-drug interactions.
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34 Foxall MJH. Elderly patients at risk of potential drug interactions in long-term care facilities.
West J Nurs Res 1982; 4: 133-51.
35 WINAp Geneesmiddelinformatie, Royal Dutch Society for the Advancement of Pharmacy, The Hague,
The Netherlands, 1998.
36 Hansten PD, Horn JR. Drug interactions and updates. 7th Ed. Philadelphia: Lea & Febiger, 1990.
37 Stockley IH. Drug Interactions. 2nd Ed. Oxford: Blackwell Scientific Publications, 1991.
38 Van Dijk KN, De Vries CS, Van den Berg PB, Dijkema AM, Brouwers JRBJ, De Jong-van den Berg LTW.
Constipation as an adverse effect of drug use in nursing home patients: an overestimated risk. Br J Clin
Pharmacol 1998; 46: 255-61.
39 Van Dijk KN, De Vries CS, Van den Berg PB, Brouwers JRBJ, De Jong-van den Berg LTW. Drug utilisation in
Dutch nursing homes. Eur J Clin Pharmacol 2000; 55: 765-71.
40 Weideman RA, Bernstein IH, McKinney PW. Pharmacist recognition of potential drug interactions.
Am J Health-Syst Pharm 1999; 56: 1524-9.
41 Venot A. Electronic prescribing for the elderly. Drugs Aging 1999; 2: 77-80.
42 Health Care Inspectorate. Medication distribution in nursing homes (in Dutch). Ministery of Health,
Welfare and Sports, The Hague, The Netherlands, 1997.
75
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9 Beers MH, Fingold SF, Ouslander JG. A computerized system for identifying and informing physicians about
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74
Abstract
Objective: We aimed to evaluate drug use in 2 Dutch nursing homes (254 residents) by develo-
ping and evaluating indicators based on pharmacy prescription data.
Methods: We evaluated the prescribing of benzodiazepines, NSAIDs, ulcer-healing drugs, and
diuretics. Prescribing indicators were used to identify prescribing that was potentially not
according to recommendations in national and regional prescribing guidelines. We used both
descriptive indicators, such as the number and percentage of users, and indicators reflecting
potentially suboptimal prescribing, such as use of drugs outside the regional drug formulary,
use of more than one drug from the same drug class and prescription of drug dosages above
recommended values. When potentially suboptimal prescribing was found, we verified the fin-
dings by means of an interview with one of the prescribers.
Results: The prescribing indicators we assessed were generally in agreement with national and
regional guidelines. However, use of benzodiazepines for more than 30 days and prescribing of
NSAIDs without concomitant prescribing of gastroprotective drugs was found in a relatively
high percentage of patients. After prescriber interview and patient chart review it was found
that some prescribing indicators, such as dosages above recommended values, were not always
indicative for suboptimal prescribing.
Conclusion: We found the majority of prescribing to be in line with recommendations upon
which we based our prescribing indicators. The prescribing indicators could be used to evalu-
ate prescribing practices, however appropriateness of prescribing was more difficult to assess.
For this, clinical information from the prescriber was necessary to be able to fully assess pre-
scribing appropriateness.
77
2.5 Prescr ib ing indicators as a tool toevaluate drug use in nurs ing homes: a p i lot s tudy
K.N. van Dijk 1,2, L.G. Pont 3, C.S. de Vries4, M. Franken1, J.R.B.J. Brouwers1,2, L.T.W. de Jong-van den Berg1
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, Groningen
University Institute for Drug Exploration (GUIDE), University Centre for Pharmacy,
Groningen, the Netherlands2 Department of Clinical Pharmacy, Medical Centre Leeuwarden, Leeuwarden, the Netherlands3 Department of Clinical Pharmacology, Groningen University Institute for Drug Exploration
(GUIDE), Groningen, the Netherlands4 Department of Pharmacoepidemiology, Postgraduate Medical School, University of Surrey,
Guildford, United Kingdom
Submitted
76
teria. Although the remaining criteria could be applied using solely pharmacy data, we percei-
ved them as potentially insensitive measurement instruments, which do not always reflect
inappropriate prescribing. For example, the use of multiple antipsychotic drugs that was inap-
propriate according to these criteria, may be clinically beneficial. Also, the fact that a prescri-
bing indication is not documented, another indication of suboptimal prescribing according to
this study, does not necessarily mean that the drug is being prescribed inappropriately. The fact
that in the Swedish studies more than a quarter of the residents received prescriptions that
were classified as potentially inappropriate suggests these criteria may have been too unspe-
cific. The Medication Appropriateness Index (MAI), developed by Hanlon in 1992 [13], was found
to be the closest to a reliable, standardised and valid instrument for assessing medication
appropriateness in elderly outpatients [1]. To our knowledge, the MAI has not been used to
assess medication appropriateness in nursing homes. In view of the differences in drug use and
living circumstances between elderly outpatients and nursing home residents, criteria for
medication appropriateness are not necessarily the same for both populations. The MAI con-
sists of 10 questions assessing the appropriateness of a prescribed medication. For 4 questions,
information on diagnoses is necessary. The other 6 questions might be suitable for use with
pharmacy prescription data only, such as ‘are there clinically significant drug-drug interactions’
and ‘is there unnecessary duplication with other drug(s)’. However, we considered aspects con-
cerning directions of use, such as patient leaflets, not to be as relevant to the appropriateness
of nursing home prescribing, as nurses ensure adequate administration of the drugs. Recently,
Knight and Avorn [5] published a list of 12 quality indicators, based on literature review and
expert panel consideration. For 5 quality indicators, clinical information such as drug indica-
tion, response to therapy or renal function was needed. One indicator concerned a drug not
available on the Dutch market. Another indicator concerned patient education, an item that
could be relevant in view of monitoring side effects by caregivers. The quality indicators that
could be used with pharmacy prescription data only included the availability of a medication
list, periodic drug regimen review, avoidance of drugs with strong anticholinergic properties
and avoidance of barbiturates, although we consider the latter a less clinically relevant problem
in view of the limited use of barbiturates in the Netherlands. To assess medication appropria-
teness in nursing homes, indicators that reflect deviations from national pharmacotherapy gui-
delines and drug formularies should be used [14]. The development of pharmacotherapy gui-
delines specifically for the elderly is limited so far. In the Netherlands, initiatives for Dutch nur-
sing home patients are currently being developed [15].
Our aim was to evaluate drug use in 2 Dutch nursing homes using different prescribing
indicators based on pharmacy prescription data. In our earlier study among nursing home
79
Introduct ion
Appropriateness of prescribing has gained much attention in studies of the quality of health
care [1-5]. This is particularly true for elderly and nursing home patients. In view of the high
rate of drug use, age-related pharmacokinetic and pharmacodynamic changes and multiple co-
morbidity, elderly patients are at a higher risk of adverse drug effects (ADEs) [1,2,6,7]. Schmader
and colleagues defined appropriate prescribing as the selection of a medication and instruc-
tions for its use that agree with accepted medical standards [2]. These standards are based on
efficacy, adverse drug effects and cost-effectiveness and they are derived from national and
international guidelines, clinical trials and expert opinion [2]. Today, the concept of evidence-
based medicine is included in daily medical practice. Evidence-based medicine is not only
based on external clinical evidence, but also on individual clinical expertise [8]. We sought to
evaluate prescribing practices in Dutch nursing homes by assessing tools that could be used
with pharmacy prescription data only.
Several tools have been developed to assess the appropriateness of prescribing in the
elderly [1,5]. Many of these were developed for assessing medication appropriateness in elder-
ly outpatients, rather than nursing home residents. Prescribing indicators used in one health
care system are not automatically applicable to other health care systems due to differences in
national pharmacotherapy guidelines and drug formularies. Furthermore, for many prescribing
indicators information on clinical status such as laboratory results or diagnoses is necessary,
making them unsuitable for use with pharmacy prescription data only. Appropriateness crite-
ria developed by Beers and colleagues [9] were based on expert consensus. They consisted of
a list of 23 medications that should be avoided and 13 medication doses, frequencies, or pre-
scription duration that generally should not be exceeded. An update, including clinical infor-
mation such as information on the prescribing indication and on potassium level monitoring,
was published in 1997 [10]. As Beers’ criteria list several medications that are not available in
the Netherlands or are not in accordance with Dutch pharmacotherapy standards, some of
these criteria cannot be applied to Dutch nursing homes. In 1997, Lunn and co-workers [7] deve-
loped a set of 18 explicit criteria, based on expert opinion, to identify inappropriate prescribing
in 101 nursing home residents in the UK. For 7 of the 18 criteria, information on clinical status
or diagnoses of the residents was necessary, again making them unsuitable for use with phar-
macy prescription data only although some of them could be incorporated. Two Swedish stu-
dies used criteria that were based on Swedish guidelines for measuring excessive use of psy-
chotropic drug use in the elderly [11,12]. In one study [11] the availability of clinical information
for 4 out of 13 criteria was required, in the other study [12] this was the case for 1 out of 10 cri-
78
group of diuretics, as the combination of a loop diuretic and a thiazide diuretic may be someti-
mes therapeutically useful in heart failure and hypertension. The appropriateness of drug
dosage was assessed by comparing the actual prescribed daily dose (PDD) with the recom-
mended dose for the elderly, expressed as the defined daily dose (DDD [20]). For benzodiaze-
pines, the recommended dosage for elderly people is 0.5 DDD [21]. For the other drug groups
the recommended dosage was set on 1 DDD as no specific recommendation for elderly patients
exists. Prescribing of hypnotic benzodiazepines is recommended not to exceed 30 days [18].
This prescribing indicator was assessed for the benzodiazepines only, as for the other drug
groups there were no guidelines concerning the duration of therapy. Furthermore, two drug
combinations were studied, both concerning NSAIDs. First, the co-prescribing of NSAIDs and
loop-diuretics was studied because NSAIDs may decrease the efficacy of diuretics and induce
congestive heart failure [22]. Second, the co-prescribing of gastroprotective drugs (proton pump
inhibitors) during NSAID therapy was studied. In view of the risks of NSAID therapy in the
elderly [23], not to prescribe a gastroprotective drug concomitantly could be regarded as sub-
optimal prescribing.
81
patients [16], we identified signals that the prescribing and use of benzodiazepines, loop diu-
retics, ulcer-healing drugs and non-steroidal anti-inflammatory drugs (NSAIDs) could poten-
tially be improved. Therefore, we focused on these drug groups. To assess the utility of the pre-
scribing indicators we verified the cases of potentially suboptimal prescribing by means of an
interview with one of the prescribers.
Methods
Sett ing
The study was carried out in two nursing homes, one for somatic care (home A; 134 resi-
dents) and one for psychogeriatric care (home B; 120 residents). Five nursing home physicians
(three in nursing home A and two in nursing home B) provided medical care on a daily basis.
Each ward was visited twice a week, and a nursing home physician was on call 24 hours a day.
Both nursing homes were served by the same hospital pharmacy. All drugs dispensed to the
nursing home residents were registered in the hospital computer system. Any changes in medi-
cation were updated routinely on a daily basis in the hospital pharmacy computer system and
a complete medication history was kept for each individual resident. Medication was adminis-
tered to individual nursing home residents based on information recorded in the computer sys-
tem, such as drug, dosage, route and time of administration. Hospital pharmacists carried out
medication surveillance. At the moment of the study there was no computerised medication
surveillance available.
Evaluat ion of drug prescr ib ing by prescr ib ing indicators
We evaluated the prescribing of benzodiazepines, NSAIDs, ulcer-healing drugs, and diure-
tics and compared them with recommendations in national guidelines and a regional drug for-
mulary [17-19]. The drug formulary was based on the International Classification of Primary
Care. We evaluated drug use against this drug formulary [19]. Table 1 presents the drugs listed
in the regional drug formulary.
The prescribing indicators we used fell into two groups, as is shown in table 2. Indicators in
group (a) were descriptive in nature and as a consequence no optimal value was defined. We
used the number and the percentage of users, related to the total number of residents present
in the nursing home. Group (b) indicators reflected potentially suboptimal prescribing.
Examples of these indicators that were applied to all four drug groups were use of drugs out-
side the formulary and use of more than one drug from the same therapeutic drug class (e.g.
two benzodiazepines or two ulcer-healing drugs). The latter indicator was not applied to the
80
Table 1: Drugs listed in the regional drug formulary
Formulary drugs
NSAIDs
Ibuprofen
Diclofenac
Diclofenac + misoprostol (fixed combination)
Meloxicam
Naproxen
Ulcer-healing drugs
Histamine H2-receptor antagonists
- Cimetidine
- Ranitidine
Proton pump inhibitors
- Omeprazole
Benzodiazepines
Hypnotics
- Temazepam
- Nitrazepam
Anxiolytics
- Oxazepam
- Diazepam
Diuretics
Furosemide
Hydrochlorothiazide
83
Ver i f i cat ion of prescr ib ing indicators
In a sample (n=25) of patients, reflecting the range of patients in whom the indicators sug-
gested potentially suboptimal prescribing, the medical charts were reviewed together with
information from the prescribers to ascertain if prescribing for these patients was indeed sub-
optimal. The information was collected during an open-structured 3-hour interview.
Results
Evaluat ion of drug prescr ib ing by prescr ib ing indicators
The results are summarised in table 2 and are reported below.
Benzodiazepines. Of the residents in nursing home A and B, the percentage of benzodia-
zepine users was 31% and 28% respectively. Of the benzodiazepine users in nursing home A
and B the percentage of hypnotic users was 28% and 16% respectively. The percentage of
anxiolytic users was 5% and 13%. The percentage of users that were prescribed daily dosages
above 0.5 DDD was 27% and 24% respectively. Two patients were prescribed a non-formulary
benzodiazepine. Fifteen and 3% of the benzodiazepine users in nursing home A and B respec-
tively received more than one benzodiazepine at the same time. The number of benzodiazepi-
ne users who were prescribed hypnotic benzodiazepines for more than 30 days was 38 (93%)
and 29 (88%).
NSAIDs. Of the residents in nursing home A and B, the percentage of NSAID users was 10%
and 5%, respectively. The percentage of users that were prescribed dosages above 1 PDD was
50% and 17%, respectively. All NSAIDs prescribed were formulary drugs. There were no
patients with concomitant use of more than 1 NSAID. Twenty-one and 17% of the NSAID users
were prescribed a loop diuretic simultaneously. Seventy-nine (home A) and 100% (home B) of
the NSAID users were not prescribed a gastroprotective drug.
Diuretics. Of the residents in nursing home A and B, the percentage of diuretic users was
31% and 13%, respectively. The percentage of users that were prescribed dosages above 1 PDD
was 17% and 19% respectively. All diuretics prescribed were formulary drugs.
Ulcer-healing drugs. Of the residents in nursing home A and B, the percentage of users of
ulcer-healing drugs was 25% and 13% respectively. The percentage of users that were prescri-
bed dosages above 1 PDD was 24% and 25% respectively. All ulcer-healing drugs prescribed
were formulary drugs. There were no patients prescribed more than 1 ulcer-healing drug.
82
Tab
le 2
: Res
ults
of t
he e
valu
atio
n of
dru
g us
e by
mea
ns o
f pre
scri
bing
indi
cato
rs in
two
Dut
ch n
ursi
ng h
omes
Indi
cato
rBe
nzod
iaze
pine
sN
SAID
sDi
uret
ics
Ulc
er-h
ealin
g dr
ugs
Type
of n
ursi
ng h
ome|
Hom
e A
Hom
e B
H
ome
A H
ome
B
Hom
e A
Hom
e B
Hom
e A
Hom
e B
(num
ber o
f res
iden
ts)
(n=
134)
(n=
120)
(n=
134)
(n=
120)
(n=
134)
(n=
120)
(n
=13
4)(n
=12
0)
a. D
escr
iptiv
e pr
escr
ibin
g in
dica
tors
Num
ber (
% o
f use
rs¥
)41
(30.
6)33
(27.5
)14
(10.
4)6
(5.0
)41
(30.
6)16
(13.
3)34
(25.
3)16
(13.
3)
hypn
otic
s: 3
7 hy
pnot
ics:
19
(27.6
)(15
.8)
anxi
olyt
ics:
7
anxi
olyt
ics:
15
(5.2
)(1
2.5)
b. In
dica
tors
ass
essi
ng p
oten
tial s
ubop
timal
pre
scri
bing
(num
ber a
nd p
erce
ntag
e of
use
rs¶ )
PDD
> 0
.521
11 (2
6.8)
8 (2
4.2)
PDD
> 1
--
7 (5
0.0)
1 (16
.7)7
(17.1
) 3
(18.8
)8
(23.
5)4
(25.
0)
[7] #
[5] #
[5] #
Use
of d
rugs
out
side
1 (
2.4)
2 (6
.1)0
(0)
0 (0
)0
(0)
0 (0
)0
(0)
0 (0
)
form
ular
y
Use
of >
1 dr
ug fr
om
6 (1
4.6)
1 (3.
0)0
(0)
0 (0
)-
-0
(0)
0 (0
)
sam
e dr
ug c
lass
Use
> 3
0 da
ys 18
38 (9
2.7)
29 (8
7.9)
--
--
--
Com
bina
tion
with
--
3 (2
1.4)
1 (16
.7)-
--
-
loop
-diu
retic
18
No
com
bina
tion
with
-
-11
(78.
6)6
(100)
--
--
gast
ropr
otec
tive
drug
18[8
] #
|nu
rsin
g ho
me
A pr
ovid
es m
ainl
y so
mat
ic c
are,
nur
sing
hom
e B
pro
vide
s m
ainl
y ps
ycho
geri
atri
c ca
re.
¥de
fined
as
the
num
ber o
f pat
ient
s di
vide
d by
the
num
ber o
f res
iden
ts in
the
nurs
ing
hom
e*10
0¶
defin
ed a
s th
e nu
mbe
r of p
atie
nts
divi
ded
by th
e nu
mbe
r of u
sers
of t
he d
rug
grou
p*10
0#
[ ]=
num
ber o
f pat
ient
s in
clud
ed in
the
inte
rvie
w fo
r ver
ifyin
g pr
escr
ibin
g in
dica
tors
Discuss ion
In this study prescribing indicators based on pharmacy prescription data were used to iden-
tify prescribing that was not in line with regional or national guidelines. We found that pre-
scribing practices in 2 Dutch nursing homes were generally in agreement with national pre-
scribing guidelines and the regional drug formulary. A discussion of the findings is given below.
Evaluat ion of drug use prescr ib ing by prescr ib ing indicators
Number and percentage of users of drug groups
In the nursing home for somatic care, approximately twice as many hypnotics, NSAIDs,
ulcer-healing drugs and diuretics were prescribed compared with the nursing home for psy-
chogeriatric care. This may reflect the somatic disorders these residents are suffering from. This
descriptive indicator reflects overall prescribing practice, and can be used to monitor changes
in prescribing in-house over time. Comparison of prescribing practices between these homes is
difficult in view of the differences in co-morbidity.
Dosage of drug groups
The percentage of the residents receiving dosages higher than recommended (for benzo-
diazepines 0.5 DDD and for NSAIDs, ulcer-healing drugs and diuretics 1 DDD) varied among the
nursing homes, with a minimum of 17% and a maximum of 50% of the residents affected. From
the interview data it was found that often the high dosages were the result of titration of the
dosage based on the clinical effect. This was the case in particular for NSAIDs, diuretics and
ulcer-healing drugs. This indicator does not necessarily reflect suboptimal prescribing regar-
ding these drug groups. Insight in the indication for which the drug is prescribed is needed to
evaluate whether a dosage is too high. In general, monitoring side effects of dosages above 1
DDD is recommended in view of the increased susceptibility of elderly patients to adverse drug
effects.
Use of non-formulary drugs
Overall, 3 patients (1% of the study population) were prescribed non-formulary drugs for
the drug groups studied. Three patients received non-formulary benzodiazepines (flurazepam
and midazolam). For these drugs alternative formulary drugs were available and recommen-
dations with regard to substitution could be made.
Duplication of drugs
More than one drug from the same drug class was prescribed to 0%-13% of the residents
and it concerned benzodiazepines and diuretics. In case of benzodiazepines, it may be worth-
while to limit prescribing to one benzodiazepine.
85
Ver i f i cat ion of prescr ib ing indicators
The medication of 25 patients (all from nursing home A) with potentially suboptimal pre-
scribing was reviewed using the medical charts and subsequently discussed with one of the
prescribing nursing home physicians. We selected eight patients who were prescribed an
NSAID and a proton pump inhibitor (PPI) concomitantly. We inquired whether the PPI was pre-
scribed to counteract the gastrotoxicity of the NSAID. Indeed, in two patients the PPI was pre-
scribed to treat gastro-intestinal adverse effects of the NSAID. For the other six patients other
reasons for prescribing a PPI existed. Two patients had a hernia diaphragmatica and were pre-
scribed a PPI to prevent erosive damage due to reflux oesophagitis, and one was diagnosed
with an ulcus duodeni. One patient was diagnosed with reflux oesophagitis, and therapy with
an H2-antagonist was insufficiently effective. One patient experienced nausea and vomiting as
a result of anti-Parkinson drug therapy (levodopa/carbidopa), and was subsequently prescri-
bed a PPI. One patient was bedridden due to spinal-cord injury and was prescribed the PPI to
prevent erosive damage due to reflux oesophagitis. Five patients were prescribed an ulcer-
healing drug (proton pump inhibitor) in dosages higher than 1 PDD (equivalent to 40 mg ome-
prazole). According to the nursing home physician, this might have been due to the fact that
some prescribers tend to start with a high dosage to effectively heal the symptoms and taper
the dosage when acute symptoms have diminished. Three patients were diagnosed with ulcus
ventriculi or ulcus duodeni and were therefore prescribed ulcer-healing drugs in these dosa-
ges. One of these patients was first prescribed an H2-receptor antagonist, but experienced cen-
tral adverse effects. Of the other 2 patients, one patient was diagnosed with reflux oesophagi-
tis and hiatus hernia and was prescribed a proton-pump inhibitor in high dosage by a medical
specialist. This therapy was subsequently continued. The other patient was prescribed metho-
trexate and experienced nausea that responded well to proton-pump inhibitor therapy.
According to the prescriber, side effects were not seen with these high dosages of proton-pump
inhibitors. Seven patients were prescribed NSAIDs above the recommended dosage. According
to the nursing home physician, this was the result of careful dose adjustments that ultimately
led to these relatively high dosages. Three of these patients were prescribed paracetamol in
dosages of 2-4 g before the NSAID was started. Severe rheumatoid arthritis and severe pain
were reasons for prescribing NSAIDs in such high dosages. The necessity for these high dosa-
ges was re-evaluated periodically as well as the occurrence of potential gastro-intestinal and
renal side effects. Five patients were prescribed loop diuretics in a dosage higher than recom-
mended. These patients all had a diagnosis of heart failure. The high dosages were the result
of careful dose adjustments that had ultimately led to these relatively high dosages. Metabolic
disorders such a hypokalaemia were frequently monitored by measuring plasma potassium
levels.
84
because we also reviewed medical charts, most information on prescribing and medical diag-
noses could be traced. Another limitation of our study was that we did not verify all prescribing
indicators used in the drug evaluation, such as whether use of more than one drug from the
same drug class was justified. Indicators that are to reflect suboptimal prescribing should be
sensitive and specific. It is often difficult to derive prescribing indicators solely from guidelines
and drug formularies. This is particularly true for elderly patients, in view of the complex co-
morbidity and often tailor-made pharmacotherapy on the basis of clinical parameters. Efforts
should therefore be directed towards the development of indicators that take into account
these issues.
In conc lus ion
This pilot study showed that in two Dutch nursing homes, drugs were prescribed according
to regional and national prescribing guidelines. Prescribing indicators were used to evaluate
drug prescribing and may reflect potentially suboptimal prescribing. However, clinical infor-
mation from the prescriber was necessary to get insight into the appropriateness of prescribing.
The prescribing indicators used were a useful tool to evaluate prescribing practices. Because
we studied only two nursing homes, that were not necessarily representative of the Dutch nur-
sing home population, generalised conclusion can not be made.
Acknowledgements
We express our gratitude to J. Tideman, for his valuable help in collecting clinical data and
his contribution to the interview. A. Van den Brand, is thanked for his co-operation in carrying
out this study. Furthermore, we are grateful to the nursing home staff of the nursing homes and
the pharmacy staff of the hospital pharmacy for their co-operation in collecting prescription
data.
87
Combination of drugs
Two indicators assessed the combination of drugs. One indicator identified prescribing of
an NSAID and a loop diuretic, which was the case in 17% to 21% of the NSAID users were affec-
ted, and prescribing practices may be improved on this point in view of the increased risk of
renal failure due to this drug-drug interaction. The other indicator assessed potential subopti-
mal prescribing when no gastroprotective drug was prescribed together with an NSAID.
Seventy-nine percent to 100% of the NSAID users were not prescribed a gastroprotective drug
concomitantly. These results indicate that prescribing practices can be improved.
Duration of drug use
For benzodiazepine users, one indicator assessed the duration of drug use. Nearly 100% of
the benzodiazepine users used hypnotic benzodiazepines for more than 30 days, which is
against Dutch guidelines [18]. Although this indicator reflects suboptimal prescribing, it is often
difficult to withdraw benzodiazepines from patients [24,25]. However, in view of the disadvan-
tageous risk-benefit ratio of the benzodiazepines, tapering of benzodiazepine use should be
attempted. Furthermore, restricting prescription of benzodiazepines for a maximum duration of
2-4 weeks, and regularly assessing the necessity of benzodiazepine prescribing can improve
prescribing practice [26].
Ver i f i cat ion of prescr ib ing indicators
From the interview and chart review data it was found that the prescribing indicators we
investigated did not always reflect suboptimal prescribing. This has also been found by others
[27,28]. An indicator that performed well was the combination of gastroprotective drugs and
NSAIDs. This indicator may reflect suboptimal prescribing in view of the risks of gastrotoxicity
of NSAIDs in the elderly [23]. Recently, the nursing homes under study have changed their pre-
scribing policies on this point. Currently, guidelines recommend prescribing gastroprotective
medication to all elderly people who chronically use NSAIDs. Indicators that assessed drug
dosages above recommended values for NSAIDs, ulcer-healing drugs and loop diuretics, did not
perform well. Often good reasons for prescribing these high dosages existed, the main reason
being that lower dosages were insufficiently effective. Potential side effects were known to the
prescribers and monitored periodically. Furthermore, drug doses are often dependent on the
indication, and several ‘ideal’ dosages per drug may exist depending on the indication. DDD
values have been developed for other purposes than monitoring prescription appropriateness
and therefore are unsuitable to assess appropriateness of drug dosages of these drug groups.
We interviewed only one nursing home physician, and therefore it was not always possible to
find out the exact reasons for prescribing by colleague-nursing home physicians. However,
86
23 Fries JF. NSAID gastropathy: the second most deadly rheumatic disease? Epidemiology and risk appraisal.
J Rheumatolog 1991; 18 (suppl 28): 6-10.
24 Busto U, Sellers EM, Naranjo CA, Capell H, Sanchez-Craig M, Sykora K. Withdrawal reaction after long-term use
of benzodiazepines. N Engl J Med 1986; 315: 854-9.
25 Van Hulten R. Blue Boy- why not? Studies of benzodiazepine use in a Dutch community [Thesis]. University of
Utrecht, 1998.
26 Eide E, Schjøt J. Assessing the effects of an intervention by a pharmacist on prescribing and administration of
hypnotics in nursing homes. Pharm World Sci 2001; 23: 227-31.
27 Avery AJ. Appropriate prescibing in general practice: development of the indicators. Qual Health Care 1998; 7: 123.
28 Brook RH, McGlynn EA, Cleary PD. Quality of health care. Part 2: Measuring quality of care. New Engl J Med
1996; 335: 966-70. 89
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88
Abstract
Objective: Concomitant prescribing of benzodiazepines may occur more frequently with selec-
tive serotonin reuptake inhibitors (SSRIs) than with tricyclic antidepressants (TCAs), partly due
to the milder sedating effects of SSRIs. However, drug utilisation studies in this area show con-
flicting results.
Methods: To investigate whether prevalence and incidence of benzodiazepine drug prescribing
is comparable between users of SSRIs and users of TCAs, a follow-up study was performed in
two different cohorts: an ambulatory and a nursing home cohort both aged ≥ 65 years. In each
cohort the incidence and prevalence of benzodiazepine use during antidepressant therapy was
estimated. TCAs and SSRIs were subsequently compared.
Results: The ambulatory population consisted of 14,336 people (58% female), mean age 74 (±
7) years. Co-prescribing of benzodiazepines occurred in 53% of the TCA users and 57% of the
SSRI users (prevalence RR 1.1; CI95 0.9-1.2). Concomitant drug therapy was >65 days per 100
days of antidepressant drug use. During SSRI therapy, significantly more people started with
benzodiazepine drug therapy than during TCA therapy (incidence RR 1.7; CI95 1.2-2.4). Analyses
repeated 5 years later yielded similar results (overall incidence RR 1.6 (CI95 1.3-2.0). The nursing
home population consisted of 2,355 residents (73% female), mean age 82 (± 7) years. Co-pre-
scribing of benzodiazepines was slightly higher than in the ambulatory population: 59% in
TCA users and 57% in SSRI users (prevalence RR 1.0; CI95 0.8-1.2). No difference was found in
the incidence of benzodiazepine starts between SSRI and TCA therapy (incidence RR 0.8; CI95
0.4-2.0).
Conclusion: More than 50% of antidepressant users receive a benzodiazepine at the same
time. In the ambulatory population, it was found that SSRI use is associated with a significant-
ly higher chance of initiating benzodiazepine therapy compared with TCA use. In the nursing
home population, no such difference was found.
91
2.6 Concomitant prescr ib ing of benzo-diazepines dur ing ant idepressant therapy in the e lder ly
K.N. van Dijk 1,2, C.S. de Vries 3, K. ter Huurne 1, P.B. van den Berg 1,J.R.B.J. Brouwers1,2, L.T.W. de Jong-van den Berg 1
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, Groningen
University Institute for Drug Exploration (GUIDE), University Centre for Pharmacy, Groningen,
the Netherlands2 Department of Clinical Pharmacy, Medical Centre Leeuwarden, Leeuwarden, the Netherlands3 Department of Pharmacoepidemiology, Postgraduate Medical School, University of Surrey,
Guildford, United Kingdom
A modified version is accepted for publication in the Journal of Clinical Epidemiology
90
study in an ambulatory cohort as well as a nursing home cohort, both aged 65 and over.
Methods
Design
Using computerised pharmacy records a prospective follow-up study was performed to
estimate the prevalence rate ratio (RR) as well as the incidence RR of benzodiazepines pre-
scribed during antidepressant therapy. Differences in benzodiazepine drug use were compared
between SSRI users (index group) and TCA users (reference group).
Study populat ion
Ambulatory cohort. Pharmacy dispensing data from the InterAction database were used for
this study. This database is a collaboration between community pharmacists in the northern
part of the Netherlands and the University of Groningen [20]. The database contains complete
medication profiles of individual patients from a number of pharmacies from 1994 onwards.
Since Dutch patients are generally registered to only one pharmacy and community pharma-
cies in the Netherlands are completely automated, complete individual medication histories
are available in community pharmacies. Community pharmacy records are reported to be a
reliable source of drug exposure [21]. Two 2-year cohorts were defined: one cohort included
those registered in the InterAction database during the years 1994-1995 and one cohort inclu-
ded those registered during the years 1998-1999. The second cohort was selected to investiga-
te whether changes over time led to different outcomes. During 1994-1995, the source popula-
tion consisted of 14,067 people aged ≥ 65 for whom a complete medication history was availa-
ble. During the years 1998-1999, the source population consisted of 17,060 people aged ≥ 65.
The increase in source population was due to an increased number of pharmacies participating
in the database. During 1994-1995 10 pharmacies participated and this increased to 12 pharma-
cies in 1998-1999.
Nursing home cohort. The study was conducted among residents aged 65 years and over in
six nursing homes with a 90 to 225-bed capacity each. The nursing home study population con-
sisted of 2,355 residents residing at any time during the two-year study period (1 October 1993
to 1 October 1995). The databases and drug use in this population have been previously descri-
bed in detail [22,23].
93
Introduct ion
Choosing appropriate antidepressant therapy in the elderly has been subject of debate in
clinical practice [1-4]. Particular attention has been given to the adverse effects of these drugs,
as elderly subjects may be more sensitive to adverse drug effects due to pharmacokinetic, phar-
macodynamic and disease-related changes that occur with advanced age. In elderly patients,
adverse effects associated with the use of tricyclic antidepressants (TCAs) include postural
hypotension, anticholinergic effects, and extrapyramidal symptoms [5]. Adverse effects of
selective serotonin reuptake inhibotors (SSRIs) include syndrome of inappropriate antidiuretic
hormone secretion, gastrointestinal disturbances and insomnia [5]. As a result of the unwan-
ted anticholinergic adverse effects of TCAs in the elderly, the use of SSRIs in the elderly is incre-
asing [3]. However, the preferential use of the more expensive SSRIs in the elderly is still under
debate [6-8]. For example, an increased risk of falls and fractures as a consequence of antide-
pressant drug use has been reported for both TCAs and SSRIs [9,10]. In addition, the need for
concomitant benzodiazepines during antidepressant therapy has been debated [11-15]. These
drugs decrease the non-specific symptoms of depression, such as insomnia, agitation and
anxiety, and are often used during the initiation of antidepressant treatment. After 4-6 weeks
of treatment the benzodiazepine is usually discontinued [16]. From two drug utilisation studies,
it has been suggested that concomitant prescribing of benzodiazepines may occur more fre-
quently in users of SSRIs than in users of TCAs, and this may be partly due to the less sedative
effects of SSRIs [12-13]. However, a more recent study in Sweden, using a cross-sectional design,
demonstrated that among TCA users the frequency of concomitant benzodiazepine prescribing
was higher than among SSRI users [14]. In view of the adverse effects of benzodiazepines in
the elderly, such as drowsiness, excess sedation, memory impairment and risk of falls [17-19], it
is worthwile to know whether use of these drugs differs between TCA and SSRI users in the
elderly. Furthermore, a difference in co-prescribing of benzodiazepines between users of TCAs
and SSRIs could constitute an additional selection criterion for choosing antidepressant thera-
py in the elderly.
A limitation of the cross-sectional study [14] was that no time sequence could be studied
and the benzodiazepines could well have been prescribed before initiation of antidepressant
therapy. To investigate changes in anxiolytic/ hypnotic benzodiazepine drug prescribing during
antidepressant therapy (SSRI or TCA) in more detail, we performed a follow-up study among
elderly subjects. The aim of this study was to investigate whether the prevalence and inciden-
ce of benzodiazepine drug prescribing differed between SSRI and TCA users. To determine
whether differences exist between ambulatory and institutionalised elderly, we performed this
92
son-days both during SSRI time and TCA time at risk. The incidence rate ratio with correspon-
ding 95% confidence intervals was determined as IRSSRI / IRTCA. To investigate whether chan-
ges over time led to different outcomes, the incidence rates of concomitant prescribing during
TCA use, during SSRI use and during use of both drug groups of the 1998-1999 cohort (cohort
II) were compared with the incidence rates of concomitant prescribing of the 1994-1995 cohort
(cohort I). The incidence rate ratio with corresponding 95% confidence intervals was determi-
ned as IRcohort II/IRcohort I. To investigate differences between the ambulatory cohort (1994-1995)
and the nursing home cohort, the incidence rates of both cohorts were compared. The statisti-
cal software program SPSS 9.0 for Windows (SPSS Inc., Chicago, IL) was used for all analyses.
Results
Populat ion character is t i cs
In table 1 characteristics of the study populations are given. More elderly people and more
women were present in the nursing home cohort, compared with both ambulatory cohorts. In
the 1998-1999 cohort, a higher percentage of benzodiazepine users was found, compared with
the 1994-1995 ambulatory cohort.
95
Prevalences
The 2-year prevalence of benzodiazepine (Anatomical Therapeutic Chemical (ATC) [24]
codes N05BA (anxiolytics; benzodiazepine derivates), N05CD (hypnotics; benzodiazepine deri-
vates) and N05CF (benzodiazepine-related hypnotics (zolpidem and zoplicone)) prescribing
among SSRI and TCA users (ATC code N06AB and N06AA respectively) was estimated by divi-
ding the number of SSRI users and TCA users who were prescribed a benzodiazepine for at
least 7 days by the total number of SSRI users and TCA users respectively. Each patient was
included only once in these analyses. The prevalence rate ratio with the corresponding 95%
confidence intervals was calculated by prevalence (SSRI) / prevalence (TCA). Furthermore, the
average duration of concomitant drug use was calculated as the number of days of drug use
divided by the total number of days of antidepressant use (SSRIs and TCAs respectively), for
those residents who used the combination for at least 7 days. To investigate whether changes
over time led to different outcomes, the prevalence of concomitant prescribing during TCA use,
during SSRI use and during use of both drug classes in the 1998-1999 cohort (cohort II) was
compared with the prevalence of concomitant prescribing in the 1994-1995 cohort (cohort I).
The prevalence rate ratio with corresponding 95% confidence intervals was determined as pre-
valence (cohort II) / prevalence (cohort I). To investigate differences between the ambulatory
cohort (1994-1995) and the nursing home cohort, the prevalences in both were compared.
Inc idence rates
Patients were considered to be ‘at risk’ for initiation of a benzodiazepines during the time
period of antidepressant therapy in which no benzodiazepines were used. The start of a ben-
zodiazepine during SSRI or TCA use, and subsequent concomitant use of at least 7 days, was
considered an event. When the start of a benzodiazepine coincided with the start of an antide-
pressant, or with the first day of the study period, the start was not considered an event. In this
way, only incident cases of benzodiazepine drug therapy during antidepressant drug therapy
were included in the analyses. We performed a sensitivity analysis in which we included starts
of benzodiazepines that coincided with the start of an antidepressant, to see whether this led
to different outcomes. For each patient, only the first episode of antidepressant drug use in the
study period was considered. We investigated incidence rates in two 2-year study periods: in
1994-1995 (cohort I) and 1998-1999 (cohort II). We blinded ourselves to drug use prior to the 98-
99 study period for better comparison with the earlier cohort. Thus, incidence rates were cal-
culated in exactly the same way for both cohorts. For each patient, only the first episode of
antidepressant drug use in the study period was considered. We investigated incidence rates
(IRs) by dividing the number of events (start of benzodiazepine) by the total number of per-
94
Table 1: Patient characteristics and use of benzodiazepines and antidepressants in both study populations
Variable Ambulatory cohort Ambulatory cohort Nursing home
’94-’95 ’98-’99 cohort ‘94-’95
(n=14,067) (%) (n=17,060)(%) (n=2,355) (%)
Age (yrs ± SD) 75.2 (±7.1) 76.5 (± 7.1) 82.0 (± 7.3)
Gender
Female 8220 (58.4) 9996 (58.6) 1666 (70.7)
Male 5847 (41.6) 7064 (41.4) 689 (29.3)
Drug use
Anxiolytics/ 4063 (28.9) 6105 (35.8) 1530 (65.0)
Hypnotics¶
TCAs¥ 600 (4.3) 832 (4.9) 283 (12.0)
SSRIs# 298 (2.1) 658 (3.9) 91 (3.9)
¶ ATC-codes [22] N05BA, N05CD and N05CF¥ ATC-code [22] N06AA# ATC-code [22] N06AB
benzodiazepine drug therapy (incidence RR 1.72; CI95 1.23-2.42). In the second ambulatory
cohort, a similar result was found: SSRI users were at a 57% higher risk for the start of a ben-
zodiazepine compared with TCA users. In the nursing home cohort, no association was found:
the incidence RR of benzodiazepine drug therapy during SSRI use compared to TCA use was
0.84 (CI95 0.36-1.96). There were no statistically significant differences between the nursing
home cohort and the first ambulatory cohort in the incidence of concomitant prescribing of
benzodiazepines among among TCA users, among SSRI users, or among both drug classes. No
statistically significant differences were found between the second and the first cohort in the
incidence of concomitant prescribing of benzodiazepines among TCA users, or among SSRI
users. However, when the concomitant use of benzodiazepines during both SSRIs and TCAs in
the 1998-1999 cohort was compared with the 1994-1995 cohort, the overall incidence RRMH was
0.82 (CI95 0.67-1.00), indicating 18% less concomitant drug use in the more recent years. In the
results presented above, when the start of a benzodiazepine coincided with the start of an anti-
depressant, the start was not considered an event. A sensitivity analysis in which we included
these events (‘prophylactic starts’) led to an incidence rate ratio of 1.60 (CI95 1.23-2.09) for the
first cohort (1994-1995) and 1.34 (CI95 1.12-1.61) for the second cohort (1998-1999). In particular in
the second cohort, the point estimate was slightly lower than the IRR without inclusion of pro-
phylactic starts (IRR 1.57 (CI95 1.24-1.98)), however the difference between SSRI users and TCA
users was still statistically significant.
97
Prevalence rat ios
In table 2 the prevalence rate ratios for concomitant use of benzodiazepines during antide-
pressant therapy for both ambulatory cohorts and the nursing home cohort are given.
Furthermore, the average duration of concomitant drug use is given. There was no difference
in prescribing of benzodiazepines for SSRI users and TCA users in the first ambulatory cohort.
In the second ambulatory cohort the concomitant prescribing of benzodiazepines was slightly
higher among SSRI users. In the nursing home cohort, there was no difference in concomitant
prescribing of benzodiazepines between SSRI users and TCA users. There were no statistically
significant differences between the nursing home cohort and the 1994-1995 ambulatory cohort
in concomitant drug use among TCA users, among SSRI users or among both drug classes com-
bined. Table 2 shows that in all cohorts the number of days of concomitant drug use per 100
days of antidepressant drug use was more than 67. There were no statistically significant diffe-
rences between the second and the first ambulatory cohort in concomitant drug use among TCA
users, among SSRI users or among both drug classes.
Inc idence rate rat ios
In table 3, the incidence rate ratios for all cohorts are presented. In the first ambulatory
cohort, use of SSRIs, compared with TCAs, was associated with a significantly increased risk of
96
Table 2: Prevalence rate ratios for concomitant benzodiazepine drug use during TCA and SSRI use
Drug use Number Number of % of days Prevalence CI95
of users concomitant of concomitant RR
(%a) benzodiazepine drug usec
users (%b)
Cohort I TCA 600 (4.3) 319 (53.2) 76.2 1.0
1994-1995
(n=14,067) SSRI 298 (2.1) 169 (56.7) 67.2 1.07 0.94-1.21
Cohort II TCA 832 (4.9) 437 (52.5) 73.2 1.0
1998-1999
(n=17,060) SSRI 658 (3.9) 414 (62.9) 70.3 1.20 1.10-1.31
Nursing TCA 286 (12.1) 170 (59.4) 83.7 1.0
home cohort
1994-1995 SSRI 93 (3.9) 53 (57.0) 82.9 0.96 0.78-1.17
(n=2,355)a % of total study populationb % of TCA and SSRI users, respectivelyc defined as the number of days of concomitant drug use per 100 days of antidepressant drug use
Table 3: Incidence rate ratios for the start of benzodiazepine drug prescribing during antidepressant therapy:
SSRIs compared to TCAs
Drug use Number Number of IR (x 10-3)a Incidence CI95
of events days at risk RR
Cohort I TCA 102 48,237 2.11 1.0
1994-1995
(n=14,067) SSRI 49 13,449 3.64 1.72 1.23-2.42
Cohort II TCA 143 80,187 1.78 1.0
1998-1999
(n=17,060) SSRI 136 48,613 2.80 1.57 1.24-1.98
Overall incidence rate ratio 1.61 1.33-1.96
Nursing TCA 23 10,855 2.12 1.0
home cohort
1994-1995 SSRI 7 3,932 1.78 0.84 0.36-1.96
(n=2,355)
a number of events divided by the number of days at risk
diately. After 4-6 weeks when the antidepressant drug has reached its effect, the benzodiaze-
pine should be tapered and discontinued [16]. The results of our analyses indicate that the
simultaneous use is long-term, and most probably not used as a temporary measure associa-
ted with initiation of antidepressant treatment. The difference of nearly 7% in the percentage
of users of benzodiazepines between the two ambulatory cohorts (table 1) could be the conse-
quence of a higher prevalence of psycho-somatic disorders or sleep disorders in the second
cohort. However, as we do not have information on the indication for which the drugs were pre-
scribed, we cannot prove this assumption. In a Dutch report on the appropriate use of benzo-
diazepines issued in 1998 [25], it was stated that in certain cases it is appropriate to treat elder-
ly people suffering from psychosomatic disorders with benzodiazepines. This may further
explain the higher frequency of benzodiazepine users in the second cohort.
Bingefors and colleagues [14] found a difference in the concomitant prescribing of anxioly-
tic/hypnotic drugs between TCA and SSRI users: concomitant prescribing occurred more fre-
quently in TCA users than in SSRI users (ORadj 1.3 (CI95 1.0-1.6). They concluded that the concern
about increased anxiolytic/hypnotic drug use among SSRI users seems to be unfounded in
Sweden. A limitation of their study was they could only study concomitant prescribing of drugs
that were dispensed on the same day [14]. By using a follow-up design, we were able to esti-
mate the incidence rate ratios for initiating benzodiazepine drug therapy during antidepressant
therapy. In this way, differences between SSRI and TCA users can be measured more accurate-
ly. Several other studies have reported on concomitant prescribing of these two drug groups,
although differences in study design and setting make comparison difficult. Gregor [13] studied
concomitant use of anxiolytics and hypnotics with SSRIs in patients younger than 65 years.
Concomitant anxiolytic and hypnotic use occurred in 9.8% and 2.8% of the patients, respecti-
vely, and was more common among patients treated with paroxetine than among patients tre-
ated with sertraline or fluoxetine. Rascati [12] showed that 35% of patients receiving SSRIs or
clomipramine used anxiolytic/hypnotic drugs concomitantly. Parkes [11] suggested that the use
of newer antidepressants, mainly SSRIs, in a group of veterans resulted in a 48% increase in
prescriptions for benzodiazepines, presumably to manage associated insomnia. The prevalen-
ces in our study are higher than in the above mentioned studies. These differences might be
explained by the differences in study design, such as the setting and age limits chosen. Also, we
studied prevalence during a 2-year period, while most studies used a shorter study period,
giving lower prevalences.
99
Discuss ion
In this study we found that in ambulatory elderly the risk for initiating benzodiazepines
during antidepressant therapy was higher for users of SSRIs than for users of TCAs (overall
incidence RR 1.6; CI95 1.3-2.0). In the second cohort (1998-1999), slightly fewer antidepressant
users started benzodiazepine drugs compared with the first cohort (1994-1995), indicating that
the rate of concomitant prescribing decreased over the years. In both cohorts there was a higher
frequency of concomitant prescribing among SSRI users compared with TCA users. Several
explanations for this finding can be given. The most likely reason for the increased risk among
SSRI users compared with TCA users, also suggested in other studies [12,13], is that the less
sedating effects of SSRIs may contribute to the increased prescribing of benzodiazepines.
Furthermore, elderly people may be more sensitive to the sedative adverse effects of TCAs, the-
refore diminishing the need for benzodiazepine drug prescribing during TCA use. In the nursing
home cohort, no difference in starting a benzodiazepine was found between users of SSRIs
compared with users of TCAs. Partly this may be due to the fact that hypnotic use in this popu-
lation is high already: in a previous study, it was found that 65% of the nursing home popula-
tion used a benzodiazepine during the study period [22]. It can be argued that starting on an
antidepressant together with a benzodiazepine may be due to anticipation of insufficient effi-
cacy of the antidepressant, possibly in view of prior use of the combination (which was un-
known to us because it was outside the study window). We checked for the impact of excluding
these ‘prophylactic starts’ and found that inclusion of these events marginally altered the
results, and it did not change our conclusion.
The prevalence of concomitant prescribing was considerable: in both ambulatory and insti-
tutionalised elderly more than 50% of TCA and SSRI users were prescribed a benzodiazepine
concomitantly. In the ambulatory cohort, concomitant benzodiazepine prescribing among SSRI
users was slightly higher when we studied prevalence (57-63% versus 53% in TCAs). The fact
that we did not find any difference between the nursing home cohort and the ambulatory
cohort suggests that the extent of the problem is the same in ambulatory and institutionalised
elderly. The duration of concomitant drug use was also relatively high: on average, concomitant
drug use lasted for greater than 67 days or more per 100 days of antidepressant drug use. The
high rate of concomitant prescribing seems of concern. In particular, the combination of TCAs
and benzodiazepines may lead to cumulation of adverse effects in the elderly, such as excess
sedation and muscle relaxation leading to an increased risk of falls [18]. In psychiatry, it is com-
mon practice to initiate antidepressant and benzodiazepine drug therapy simultaneously, with
the aim to treat non-specific symptoms of depression such as agitation and insomnia imme-
98
References
1 Avorn J, Gurwitz JH. Drug use in the nursing home. Ann Intern Med 1995; 123: 203-11.
2 Flint AJ. Choosing appropriate antidepressant therapy in the elderly. A risk-benefit assessment of available
agents. Drugs Aging 1998; 13: 269-80.
3 Mamdani MM, Parikh SV, Austin PC, Upshur RE. Use of antidepressants among elderly subjects: trends and
contributing factors. Am J Psychiatry 2000; 157: 360-7.
4 Salzman C. Practical considerations for the treatment of depression in elderly and very elderly long-term care
patients. J Clin Psychiatry 1999; 60 (suppl): 30-3.
5 Pollock BG. Adverse reactions of antidepressants in elderly patients. J Clin Psychiatry 1999; 60 (suppl): 4-8.
6 Mittmann N, Herrmann N, Einarson TR et al. The efficacy, safety and tolerability of antidepressants in late life
depression: a meta-analysis. J Affect Disord 1997; 46: 191-217.
7 Williams JW, Mulrow CD, Chiquette E, Hitchcock Noël P, Aguilar C, Cornell J. A systematic review of newer
pharmacotherapies for depression in adults: evidence report summary. Ann Intern Med 2000; 132: 743-56.
8 Steffens DC, Krishnan KB, Helms MJ. Are SSRIs better than TCAs? Comparison of SSRIs and TCAs:
a meta-analysis. Depress Anxiety 1997; 6: 10-18.
9 Thapa PB, Gideon P, Cost TW, Milam AB, Ray WA. Antidepressants and risk of falls among nursing home
residents. N Engl J Med 1998; 339: 875-82.
10 Liu B, Anderson G, Mittman N, To T, Axcell T, Shear N. Use of selective serotonin-reuptake inhibitors or tricyclic
antidepressants and risk of hip fractures in elderly people. Lancet 1998; 351: 1303-7.
11 Parkes A. Starting SSRI antidepressant therapy: its effect on tricyclic antidepressant and benzodiazepine
prescribing (letter). Med J Aust 1996; 164: 509.
12 Rascati K. Drug utilization review of concomitant use of specific serotonin reuptake inhibitors or clomipramine
with anxiety/sleep medications. Clin Ther 1995; 17: 786-90.
13 Gregor K, Riley J, Downing D. Concomitant use of anxiolytics and hypnotics with selective serotonin reuptake
inhibitors. Clin Ther 1996; 18: 521-7.
14 Bingefors K, Isacson DGL. Concomitant prescribing of tranquilizers and hypnotics among patients receiving
antidepressant prescriptions. Ann Pharmacother 1998; 32: 531-5.
15 Pathiyal A, Hylan TR, Jones JK, Davtian D, Sverdlov L, Keyser M. Prescribing of selective serotonin reuptake
inhibitors, anxiolytics, and sedative-hypnotics by general practitioners in The Netherlands: a multivariate
analysis. Clin Ther 1997; 19: 798-810.
16 Joffe RT, Levitt AJ, Sokolov STH. Augmentation strategies: focus on anxiolytics. J Clin Psychiatry 1996; 57 (suppl):
25-31.
17 Holbrook AM, Crowther R, Lotter A, Cheng C, King D. Meta-analysis of benzodiazepine use in the treatment of
insomnia. CMAJ 2000; 162: 225-33.
18 Ray WA, Thapa PB, Gideon P. Benzodiazepines and the risk of falling in nursing home residents.
J Am Geriatr Soc 2000; 48: 682-5.
19 Hanlon JT, Horner RD, Schmader KE, Fillenbaum GG, Lewis IK, Wall WE, et al. Benzodiazepine use and
cognitive function among community-dwelling elderly. Clin Pharmacol Ther 1998; 64: 684-92.
20 Tobi H, Van den Berg PB, De Jong-van den Berg LTW. The InterAction database: synergy of science and practice
in pharmacy. In: Medical data analysis: first international symposium; proceedings/ISMDA (RW Brause,
E Hanisch, eds). Berlin: Springer, 2000: 206-11.
21 Lau HS, de Boer A, Beuning KS, Porsius A. Validation of pharmacy records in drug exposure assessment.
J Clin Epidemiol 1997; 50: 619-2.
22 Van Dijk KN, De Vries CS, Van den Berg PB, Brouwers JRBJ, De Jong-van den Berg LTW. Drug utilisation in
Dutch nursing homes. Eur J Clin Pharmacol 2000; 55: 765-71.
101
Limitat ions
Information for each subject in the study population was obtained from one of the two
datasets: the InterAction dataset and the nursing home dataset. Both databases are pharmacy
prescription databases and contain complete medication profiles of individual patients. We do
not know whether people actually used the drugs that were dispensed. In the nursing home,
nursing staff ensure everyone takes their medication [22]. However, in the ambulatory cohort,
people who collect their prescriptions at the pharmacy, need not necessarily use these drugs.
An overestimation of drug use due to non-adherence could be a consequence of using phar-
macy prescription data. In our study this may lead to an overestimation of drug use in the
ambulatory cohort, thus leading to a greater difference between the ambulatory and nursing
home cohort. Non-adherence may be greater with TCAs than with SSRIs (due to more severe
adverse effects [26]), leading to greater differences between TCAs and SSRIs, which would not
change our conclusions. Antidepressants may be used for other conditions than depression, for
example panic disorder (mainly SSRIs), obsessive-compulsive disorder (mainly SSRIs), and
neuropathic pain conditions (mainly TCAs). This may influence co-prescribing of other drugs.
Since we did not have information on indications for prescribing of antidepressant drugs, we
can not adjust for this possibly confounding effect. Furthermore, we do not know whether the
choice of antidepressant is influenced by the mental state of the patients. For example, TCAs
may be prescribed more frequently to agitated patients.
In summary, in this follow-up study of the elderly we could not confirm an increased risk
for concomitant prescribing of benzodiazepines among TCA users found in an earlier, cross-
sectional study [14]. In fact, we found a higher incidence of benzodizapine drug prescribing
among SSRI users compared with TCA users. In the nursing home cohort, no difference in ini-
tiating benzodiazepine drug therapy was found between users of SSRIs compared with users
of TCAs. We also found a 2-year prevalence of benzodiazepine drug prescribing during both
SSRI and TCA use of more than 50% and that the duration of concomitant drug use was rela-
tively long-term (>65 days per 100 days of antidepressant drug use) in both ambulatory and
institutionalised elderly. In view of cumulation of adverse sedative effects, and questionable
therapeutic benefits of concomitant prescribing of these two drug groups, physicians may bene-
fit from feedback on their prescribing habits.
Acknowledgement
We express our gratitude to D.A. Bloemhof, hospital pharmacist, for supplying nursing
home pharmacy data.
100
2.7 Prescr ib ing of gastroprotect ive drugsamong elder ly NSAID users in theNether lands
K.N. van Dijk 1,2, K. ter Huurne1, C.S. de Vries 3, P.B. van den Berg1,J.R.B.J. Brouwers1,2, L.T.W. de Jong-van den Berg1
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, Groningen
University Institute for Drug Exploration (GUIDE), University Centre for Pharmacy,
Groningen, the Netherlands2 Department of Clinical Pharmacy, Medical Centre Leeuwarden, Leeuwarden, the Netherlands3 Department of Pharmacoepidemiology, Postgraduate Medical School, University of Surrey,
Guildford, United Kingdom
Pharmacy World and Science (in press)
103
23 Van Dijk KN, De Vries CS, Van den Berg PB, Dijkema AM, Brouwers JRBJ, De Jong-van den Berg LTW.
Constipation as an adverse effect of drug use in nursing home patients: an overestimated risk.
Br J Clin Pharmacol 1998; 46: 255-61.
24 Anonymous. Anatomical therapeutical chemical (ATC) classification index including defined daily dosages
(DDDs) for plain substances. World Health Organisation Collaborating Centre for Drug Statistics Methodology,
Oslo, 2000.
25 Dutch Health Council: Towards a more appropriate use of benzodiazepines. Publication number 1998/20.
The Hague: Health Council; 1998.
26 Anderson IM. Selective serotonin reuptake inhibitors versus tricyclic antidepressants: a meta-analysis of
efficacy and tolerability. J Affect Disord 2000; 58: 19-36.102
Int roduct ion
The use of non-selective non-steroidal anti-inflammatory drugs (NSAIDs) is associated
with a wide range of gastrointestinal (GI) toxicity, including mild dyspepsia (prevalence 20%),
development of (asymptomatic) duodenal or ventricular ulcera (prevalence of 10-20% within 3
months of NSAID-use) and serious complications such as perforation, ulceration, obstruction
and/or bleeding [1-4]. The risk for serious GI complications increases with advanced age:
NSAID users aged over 60 years are at up to 5 times greater risk of developing serious GI com-
plications (e.g. bleeding, perforation) than those not taking NSAIDs [2]. Other risk factors inclu-
de previous NSAID-related GI adverse effects, previous history of gastrointestinal events, con-
comitant corticosteroid use, concomitant use of anticoagulant drugs, chronic use of NSAIDs and
use of high dosages of NSAIDs [3-7].
To prevent the occurrence of NSAID-induced GI-toxicity, several strategies exist. Prescribing
a gastroprotective agent such as an antacid or an H2-receptor antagonist usually can prevent
mild dyspepsia. Asymptomatic ulcers can be prevented by concurrent administration of H2-
receptor antagonists or proton pump inhibitors. To prevent serious GI toxicity, the prostagland-
in analogue misoprostol [8,9], proton pump inhibitors [9,10] and H2-receptor antagonists in
high dosage [10] are reported to be effective, although a beneficial effect on perforation,
obstruction and bleeding has been shown only for misoprostol [8,11]. In a randomised control-
led trial (n=935) it was found that omeprazole 20 mg and 40 mg daily and misoprostol 800 µg
daily produced similar reductions of endoscopically diagnosed ulceration [12]. Misoprostol
generally causes more adverse effects (diarrhoea and abdominal pain) [12]. It was shown that
misoprostol (used in the combined formulation of diclofenac/misoprostol) was more cost-
effective than proton pump inhibitors [13]. Selective cyclo-oxygenase-2 (COX-2) inhibitors, such
as rofecoxib and celecoxib, are reported to result in 50% less perforation, obstruction and blee-
ding than classic NSAIDs (such as diclofenac, naproxen and ibuprofen) [14,15]. These drugs
might be alternatives to classic NSAIDs combined with gastroprotective drugs [11], however to
our knowledge cost-effectiveness studies have not been performed yet.
In most countries guidelines exist to minimise the risk of NSAID-induced GI toxicity, main-
ly recommending concurrent administration of gastroprotective agents [11]. The American
College of Rheumatology recommends that patients with a risk of developing NSAID-induced
gastropathy should receive concomitant therapy with gastroprotective agents, such as miso-
prostol [2]. In the Netherlands, similar recommendations exist [16]. To investigate whether
these recommendations are being followed in daily clinical practice, we studied the prescribing
of H2-receptor antagonists, proton pump inhibitors and misoprostol in a cohort of NSAID users
aged 65 years and over.
105
Abstract
Objective: Use of non-steroidal anti-inflammatory drugs (NSAIDs) is associated with an in-
creased risk of gastrointestinal toxicity, in particular when risk factors are present. We investi-
gated whether recommendations, suggesting concomitant therapy with gastroprotective
agents for patients at risk of developing NSAID-induced gastropathy, are being followed in
daily clinical practice.
Methods: A study was performed to investigate concomitant prescribing of gastroprotective
agents (H2-receptor antagonists, proton pump inhibitors, or misoprostol) in an ambulatory
cohort of NSAID users aged 65 years and over. The prevalence of concomitant prescribing was
studied, as well as the prophylactic prescribing of gastroprotective drugs. A stepwise logistic
regression was performed to determine predictive variables of prophylactic and concomitant
gastroprotective drug prescribing.
Results: Co-prescribing of gastroprotective drugs occurred in 1,522 (23%) (of which 944 con-
cerned prophylactic prescribing) of the NSAID users (n=6,557), with an average duration of 67
days per 100 days of NSAID use. Co-prescribing of gastroprotective drugs varied among indivi-
dual NSAIDs. Concomitant use of oral corticosteroids (ORadj 2.4; CI95 2.0-2.9), coumarins (ORadj
1.6; CI95 1.3-2.0), and low dose aspirin (ORadj 1.6; CI95 1.4-1.9) were significantly associated with
both prophylactic and concomitant prescribing of gastroprotective agents during NSAID thera-
py.
Discussion: Despite current guidelines recommending gastroprotective drug prescribing among
high risk groups, the rate of concomitant prescribing of gastroprotective agents in NSAID users
aged 65 years and over is low. Feedback to prescribers should be given to improve prescribing
practices in this high risk group.
104
tors were the drugs most frequently used among NSAID users, followed by H2-receptor anta-
gonists and misoprostol (p<0.05).
Co-prescribing of gastroprotective drugs occurred in 1,522 (23%) (of which 944 concerned
prophylactic prescribing) of the NSAID users (n=6,557), with an average duration of 67 days per
100 days of NSAID use. In table 2, the results of the analyses on concomitant drug use of gastro-
protective agents during use of NSAIDs are given for each NSAID that was used in the study
population. The NSAIDs in table 2 are given in order of the GI toxicity ranking of Henry and co-
workers [19], with ibuprofen being the NSAID with the lowest toxicity profile, and ketoprofen
the NSAID with the highest toxicity profile. Meloxicam, nabumetone and the fixed combination
of diclofenac plus misoprostol have been on the market since 1996, and therefore were not clas-
sified by Henry [19]. Prophylactic prescribing of gastroprotective drugs occurred most frequent-
ly among users of ketoprofen (26%), compared to other NSAID users. Ibuprofen users were
prescribed the fewest prophylactic GI-protective drugs (9%). In table 2 it is shown that the rela-
tive risk for prescribing gastroprotective drugs prophylactically was 3.6 times higher for keto-
profen users compared to ibuprofen users. Meloxicam users were 2.3 times more likely to recei-
ve a gastroprotective drug prophylactically. The prevalence of concomitant GI-protective drug
prescribing was highest among users of meloxicam (34%), followed by ketoprofen (31%).
Otherwise stated, meloxicam users and ketoprofen users were respectively 2.4 times and 2
times more likely to receive a gastroprotective drug concomitantly (table 2). The lowest preva-
lence of concomitant GI-protective drug prescribing was found among ibuprofen users (18%).
The duration of concomitant drug use ranged between 58 days per 100 days of indomethacin
107
Methods
Study populat ion
The study was performed with pharmacy dispensing data form the InterAction database,
which is part of a collaboration between community pharmacists in the Northern part of the
Netherlands and the University of Groningen. This database contains complete medication
profiles of 135,000 individual patients from a number of pharmacies from 1994 onwards and
has been described in detail elsewhere [17]. The study population included all patients aged 65
and over who were registered within the InterAction database and who were prescribed an
NSAID at any time during the study period of January 1998 until December 1999.
Drug ut i l i sat ion
Concomitant use of NSAIDs (Anatomical Chemical Therapeutic (ATC) [18] code M01A) and
the following gastroprotective agents was studied: misoprostol (ATC code A02BB01), H2-recep-
tor antagonists (ATC code A02BA), and proton pump inhibitors (ATC code A02BC). The 2-year
prevalence of concomitant prescribing of these gastroprotective agents during NSAID therapy
was calculated by dividing the number of NSAID users who were prescribed a gastroprotective
agent concomitantly for at least 1 day by the total number of NSAID users. Each patient was
included in these analyses only once. The average duration of concomitant drug use was cal-
culated as the number of days of concomitant drug use divided by the total number of days of
NSAID use. These analyses were performed for each NSAID individually. NSAIDs that were
prescribed to less than 50 people were excluded from the analyses. We investigated the pro-
phylactic prescribing of gastroprotective drugs separately, defined as the number of patients
who started with an NSAID and a gastroprotective agent, prescribed by the same physician, on
the same day. Relative risks for both prophylactic and concomitant prescribing of gastroprotec-
tive drugs during NSAID therapy were calculated for each NSAID individually. A stepwise logis-
tic regression was performed to determine predictive variables of prophylactic and concomitant
gastroprotective drug prescribing.
Results
17,060 patients aged 65 and over were registered in the InterAction database during 1998
and 1999. Of these, 6,557 (38.4%) used an NSAID at any time during the study period. In table
1, the numbers of users of gastroprotective medications among NSAID users and non-NSAID
users are given. Proton pump inhibitors, H2-receptor antagonists and misoprostol were used
more frequently in NSAID users compared to non-NSAID users (p<0.05). Proton pump inhibi-
106
Table 1: Use of drugs under study in the study populations
Drug use Cohort 1998-1999¶ NSAID users¥ Non-NSAID users
(n=17,060) (%) (n=6,557) (%) (n=10,503) (%)
Proton pump 2530 (14.8) 1262 (19.2) 1268 (12.1)
inhibitors
H2-antagonists 1998 (11.7) 977 (14.9) 1021 (10.3)
Misoprostol 14 (<0.1) 13 (0.2) 1 (<0.1)
¶ Cohort of patients aged > 64 yrs registered in the InterAction database at any time during 1998-1999¥ Defined as person who uses NSAID at any time during study period.
109
drug use and 82 days per 100 days of ketoprofen drugs use. Table 3 shows the results of the m
ul-
tivariate analyses. It was found that use of N
SAIDs for m
ore than 90 days, concomitant use of
oral corticosteroids, concomitant use of low
dose aspirin and concomitant use of oral antico-
agulants (coumarins) all w
ere significantly associated with both prophylactic and concom
itant
prescribing of gastroprotective agents during NSAID
therapy.
108
Table 2: Use of concomitant GI-protective drugs during NSAID use¶
NSAID Number of patients¥ Number of patients RRcrude (95% CI) Number of patients RRcrude (95% CI) Duration of(% of cohort of on prophylactic on concomitant concomitantNSAID users GI-drug use (%) GI-drug use (%) drug use (%)(n=6,557))
Ibuprofen 2556 (38.9) 229 (9) 1 (reference) 461 (18) 1 (reference) 66.3Diclofenac 3579 (54.6) 475 (13.3) 1.56 (1.32-1.84) 807 (22.5) 1.32 (1.16-1.50) 65.3Naproxen 793 (12.1) 103 (13) 1.52 (1.18-1.94) 176 (22.2) 1.30 (1.07-1.58) 68.7Indomethacin 196 (3) 33 (16.8) 2.06 (1.38-3.06) 53 (27.0) 1.68 (1.21-2.35) 57.7Piroxicam 136 (2.1) 18 (13.2) 1.55 (0.93-2.59) ns 33 (24.3) 1.46 (0.97-2.18) ns 72.3Ketoprofen 104 (1.6) 27 (26) 3.56 (2.25-5.64) 32 (30.8) 2.02 (1.32-3.10) 81.8
Diclofenac+Misoprostol 509 (7.8) 50 (9.8) 1.11 (0.80-1.53) ns 121 (23.8) 1.42 (1.13-1.78) 65Meloxicam 340 (5.2) 63 (18.5) 2.31 (1.70-3.14) 116 (34.1) 2.35 (1.84-3.01) 71.3Nabumeton 279 (4.3) 34 (12.1) 1.41 (0.96-2.07) ns 72 (25.8) 1.58 (1.19-2.11) 73.0
¶ NSAIDs are listed in order of comparative toxicity, ranging from low toxicity (ibuprofen) to relatively high toxicity (ketoprofen)(adapted from [11]), except for diclofenac+misoprostol, meloxicam, nabumeton
¥ total number of patients is more than 6,557 because patients may use several NSAIDsns non significant
Discuss ion
We found that 23% of a cohort of NSAID users aged 65 and over were prescribed a gastro-
protective agent concomitantly. Among two-third of these patients the gastroprotective drugs
were prescribed prophylactically. In view of current prescribing guidelines, these findings indi-
cate low concomitant and prophylactic prescribing of gastroprotective agents among NSAID
users aged over 64 year in daily clinical practice.
The frequency of concomitant prescribing varied between individual NSAIDs. According to
the classification of Henry and co-workers [19], ibuprofen is the NSAID with the lowest GI toxi-
city profile. We found that concomitant and prophylactic prescribing was the lowest among ibu-
profen users, which is in line with Henry’s classification. Ketoprofen (a high GI toxicity NSAID
[19]) users were almost 4 times more likely to receive a gastroprotective drug prophylactically,
compared with ibuprofen users. Also, the prevalence of concomitant gastroprotective drug use
among ketoprofen users was higher than for the other NSAIDs, with the exception of meloxi-
cam. However, percentages of concomitant (31%) and prophylactical (26%) prescribing among
ketoprofen users were still very low. The fact that nearly 75% of the ketoprofen users did not
use any gastroprotective agent raises the question whether these patients are optimally tre-
ated or whether the guidelines need adjusting. Prophylactic and concomitant use of gastropro-
tective drugs among meloxicam users was relatively high (19% and 34% respectively). An
explanation for this finding could be that meloxicam, which is reported to have milder GI side
effects due to preferential inhibition of cyclooxygenase 2 [20], is prescribed to high-risk
patients (‘channelling’). This phenomenon has recently been described in a report by Lanes and
co-workers [21].
Strengths and l imitat ions
Because we used dispensed prescribing data from pharmacies, we eliminated primary non-
compliance. Furthermore, the detail and completeness of information present in the
InterAction database enables the accurate estimation of duration of drug use and the specific
drugs dispensed, for each individual patient. A limitation of our study is that we do not know
whether patients who do not use gastroprotective drugs concomitantly suffer from more seve-
re GI toxicity, such as perforation and bleeding, than patients who are prescribed gastroprotec-
tive drugs. Because we performed a retrospective study on prospectively collected data, sever-
al biases could occur. Information bias could occur when drugs are not taken as dispensed.
Selection bias in this study is not very likely to occur, because this study is based on a popula-
tion-based cohort. Confounding by lifestyle-factors such as smoking and alcohol could have
111110
Tab
le 3
: Var
iabl
es a
ssoc
iate
d w
ith p
roph
ylac
tic a
nd c
onco
mita
nt g
astr
opro
tect
ive
drug
pre
scri
bing
Prop
hyla
ctic
gas
trop
rote
ctiv
e dr
ug u
seCo
ncom
itant
gas
trop
rote
ctiv
e dr
ug u
se
Vari
able
ORcr
ude
(CI 95
)¶OR
adju
sted
(CI 95
)¥OR
crud
e(C
I 95)
ORad
just
ed(C
I 95)
Gen
der (
mal
e vs
fem
ale)
0.66
(0.5
7-0.
77)
0.74
(0.6
3-0.
87)
0.77
(0.6
8-0.
88)
0.83
(0.73
-0.9
4)
Age
(+5
year
s)1.1
0 (1
.04-
1.15)
1.0 (0
.95-
1.05)
1.09
(1.0
5-1.1
4)1.0
3 (0
.99-
1.08)
Dur
atio
n of
NSA
ID u
se (+
30 d
ays)
1.12
(1.10
-1.13
)1.1
0 (1
.09-
1.11)
1.08
(1.0
7-1.0
9)1.0
6 (1
.05-
1.07)
Use
of o
ral c
ortic
oste
roid
s2.
93 (2
.42-
3.56
)2.
18 (1
.78-2
.68)
2.97
(2.4
9-3.
54)
2.44
(2.0
3-2.
93)
Use
of l
ow d
ose
aspi
rin
2.09
(1.78
-2.4
5)1.8
1 (1.5
2-2.
15)
1.83
(1.5
9-2.
11)
1.62
(1.4
0-1.8
8)
Use
of c
oum
arin
s2.
39 (1
.92-
2.96
)1.9
1 (1.5
1-2.4
1)1.9
4 (1
.59-
2.36
)1.6
4 (1
.33-
2.01
)
¶od
ds ra
tio w
ith c
orre
spon
ding
95%
con
fiden
ce in
terv
als,
una
djus
ted
for v
aria
bles
list
ed in
col
umn
1¥
odds
ratio
with
cor
resp
ondi
ng 9
5% c
onfid
ence
inte
rval
s, a
djus
ted
for v
aria
bles
list
ed in
col
umn
1
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15 Bombardier C, Laine L, Reicin A, Shapiro D, Burgos-Vargas R, Davis B, et al. Comparison of upper gastro-
intestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis. VIGOR Study Group.
N Engl J Med 2000; 343: 1520-8.
16 Van der Kuy, A (Ed). Farmacotherapeutisch Kompas (in Dutch). Amstelveen, The Netherlands: CMPC
Ziekenfondsraad, 2001: 940.
17 Tobi H, Van den Berg PB, De Jong-van den Berg LTW. The InterAction database: synergy of science and practice
in pharmacy. In: Brause RW, Hanisch E (ed) Medical data analysis: first international symposium;
proceedings/ISMDA. Berlin: Springer, 2000: 206-11.
18 Anonymous. Anatomical therapeutical chemical (ATC) classification index including defined daily dosages
(DDDs) for plain substances. Oslo: World Health Organisation Collaborating Centre for Drug Statistics
Methodology, 2000.
19 Henry D, Lim LLY, Garcia Rodriguez LA, Perez Gutthann S, Carson JL, Griffin M, et al. Variability in risk of
113
occurred in this study; this information is not present in the prescription database and is likely
to affect concomitant prescribing. In this study, we defined misoprostol (combinations), proton
pump inhibitors and H2-receptor antagonists as gastroprotective agents. Of these misoprostol
is the only drug with proven effectiveness on perforation, obstruction and bleeding [8]. H2-
receptor antagonists have been reported to be effective in double dosage (2 times the licensed
daily dosage for ulcer healing). Limiting our analyses to misoprostol would have shown lower
percentages of concomitant and prophylactic gastroprotective drug use during NSAID therapy.
We found that H2-receptor antagonists were prescribed in normal dosages (data not shown).
From this study, in daily clinical practice the prescription of double dosage of H2-receptor anta-
gonists does not seem to take place.
Other studies
In a Canadian cohort of NSAID users > 65 years (n= 61,601), 26% were prescribed an anti-
ulcer medication, compared with 11% of the non-NSAID users (n=168,944) [22]. Schubert and
colleagues [23] found that 8.9%-12.2% of patients treated with NSAIDs (n=460) were prescri-
bed an antacid or a drug for the treatment of peptic ulcer. LeLorier [24] found that in a cohort
of elderly aged over 65 (n=773,654) ibuprofen users were prescribed less systemic antiulcer
agents than other NSAIDs, probably due to a channelling of low risk patients towards this drug.
Pertusi and colleagues [25] held a survey among geriatric practitioners (n=229) to investigate
the extent of gastroprotective prescribing among elderly NSAID users and the influence of risk
factors (age, previous peptic ulcer, previous gastro-intestinal bleeding, and heart disease) on
their choices. It was found that 64% of the respondents would not prescribe gastroprotective
agents to elderly patients who use NSAIDs. This was different for nursing home residents using
NSAIDs: only 32% would not prescribe gastroprotective agents for this population.
Furthermore, age and heart disease were risk factors that physicians did not take into account
when choosing gastroprotective agents. Further research is needed into the reasons for not
prescribing gastroprotective agents for high-risk patients. The results of our study are in line
with these findings and raise the question why gastroprotective agents are not prescribed more
frequently among high-risk patients.
Conclus ion
Our study shows that gastroprotective drugs are prescribed to only a minority (23%) of
NSAID users aged 64 and over. This might suggest that physicians do not consider age as a risk
factor for gastrointestinal adverse effects of NSAIDs. In view of the known gastrointestinal side
effects of non-selective NSAIDs, we think it is necessary to give feedback to physicians with the
aim to increase the rate of concomitant gastroprotective drug use in elderly NSAID users.
112
Chapter 3
Risk assessment studiesin the e lder ly
115
gastrointestinal complications with individual non-steroidal anti-inflammatory drugs: results of a collaborative
meta-analysis. BMJ 1996; 312: 1563-6.
20 Hawkey C, Kahan A, Steinbrück K, Alegre C, Baumelou E, Bégaud B, et al. Gastrointestinal tolerability of
meloxicam compared to diclofenac in osteoarthritis patients. Br J Rheumatol 1998; 37: 937-45.
21 Lanes SF, Garcia Rodriguez LA, Hwang E. Baseline risk of gastrointestinal disorders among new users of
meloxicam, ibuprofen, diclofenac, naproxen and indomethacin. Pharmacoepidemiol Drug Saf 2000; 9: 113-7.
22 Hogan DB, Campbell NRC, Crutcher R, Jennett P, MacLeod N. Prescription of nonsteroidal anti-inflammatory
drugs for elderly in Alberta. CMAJ 1994; 151: 315-22.
23 Schubert I, Ihle P, Köster I, Ferber von L. Markers to analyse the prescribing of non-steroidal anti-inflammatory
drugs in ambulatory care. Eur J Clin Pharmacol 1999; 55: 479-86.
24 LeLorier J. Patterns of prescription of nonsteroidal antiinflammatory drugs and gastroprotective agents.
J Rheumatol 1995; (suppl 43): 22: 26-7.
25 Pertusi RM, Godwin KS, House KJ, Knebl JA, Alexander JH, Rubin BR, et al. Gastropathy induced by nonsteroidal
anti-inflammatory drugs: pescribing patterns among geriatric practitioners. J Am Osteopath Assoc 1999; 99: 305-10.
114
3.1 Const ipat ion as an adverse effect ofdrug use in nurs ing home pat ients : an overest imated r isk
K.N. van Dijk 1,2, C.S. de Vries3, P.B. van den Berg1, A.M. Dijkema1,J.R.B.J. Brouwers1,2, L.T.W. de Jong-van den Berg1
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, Groningen
University Institute for Drug Exploration (GUIDE), University Centre for Pharmacy,
Groningen, the Netherlands2 Department of Clinical Pharmacy, Medical Centre Leeuwarden, Leeuwarden, the Netherlands3 Department of Pharmacoepidemiology, Postgraduate Medical School, University of Surrey,
Guildford, United Kingdom
British Journal of Clinical Pharmacology 1998; 46: 255-61
117
Out l ine
This chapter focuses on the clinical implications and potential risks associated with drug
use, with reference to two particular issues identified in chapter 2. Section 3.1 presents a study
among a cohort of nursing home residents, in which the association between drug use and con-
stipation was investigated. As was shown in section 2.3, laxative use was high in nursing home
patients and we investigated whether this might be the consequence of use of drugs that have
been associated with an increased frequency of constipation, such as anticholinergic drugs. In
section 3.2, a study is presented in which the clinical effect of a drug-drug interaction was
investigated. The drug-drug interaction studied was between NSAIDs and acenocoumarol, an
interaction that was frequently encountered in nursing home patients as was shown in section
2.4. This study was performed in a cohort of elderly outpatients, using data from the Groningen
Outpatient Thrombosis Service. Genotyping of cytochrome P450 2C9 was performed to deter-
mine whether genotype was a predictive variable for the occurrence of this drug interaction.
116
Int roduct ion
Many studies have reported laxative use in the elderly to be disturbingly high and it has
been suggested that improved pharmacotherapy might reduce the prevalence of constipation
[1]. The prevalence of constipation in ambulatory elderly over age 65 years varies from 16% to
41% [2,3]. Chronic constipation may lead to complications such as faecal impaction, stercoral
ulceration, bowel obstruction, sigmoid volvulus and even syncope [2]. The prevalence of con-
stipation among institutionalised elderly has been reported to be even higher [4,5]. In a popu-
lation of 784 nursing home patients in the Netherlands, 53% were prescribed one or more
laxatives daily [5]. Long-term use of stimulant laxatives may lead to abdominal cramps, fluid
and electrolyte disturbances, malabsorption and cathartic colon [6]. In view of these unwanted
effects and to improve the quality of life of the elderly it is worthwhile to study whether laxa-
tive use can be reduced in this population. Polypharmacy is an important risk factor for consti-
pation, especially in nursing homes where levels of medication use are high [7]. Drugs that are
commonly associated with constipation are opioids, iron salts, calcium channel blockers and
drugs with anticholinergic/antimuscarinic effects [5]. The last group is also responsible for
other potentially dangerous adverse effects in the elderly such as urinary retention, memory
problems, delirium and acute glaucoma [8,9]. In pharmacoepidemiological studies, laxative
administration is used as a proxy for constipation because laxative use has been shown to cor-
relate well with constipation [1]. The association between laxative use and other drug use has
been assessed in several studies [1–3,10,11]. In most of these studies, only some subgroups of
drugs were considered and the majority of these studies used cross-sectional study designs. To
investigate whether the suggested causal association between laxative use and co-medication
could be confirmed in a cohort of nursing home residents in the Netherlands we carried out a
prospective study using prescription sequence analysis. If any such causal association exists,
recommendations can subsequently be given for alternative pharmacotherapy in order to redu-
ce laxative use in the elderly.
Methods
Design
A prospective cohort study was performed to estimate the incidence relative risk of consti-
pation as an adverse effect of drug use. The study was conducted with prescription sequence
analysis of computerised pharmacy records. Prescription sequence analysis is a method to
determine side effects of drugs through individual medication histories. It is based on the
119
Abstract
Objective: To investigate whether results from case control and cross sectional studies, which
suggest an association between laxative use and other drug use, could be confirmed in a cohort
study of nursing home patients.
Methods: A prospective cohort study of 2,355 nursing home patients aged 65 years and over
was performed to estimate the incidence relative risk of constipation associated with drug use.
The study was conducted with prescription sequence analysis of each resident’s detailed phar-
macy records and data on morbidity and mobility.
Results: Use of drugs, which according to the summaries of product characteristics and the lite-
rature on adverse drug effects have moderately to strongly constipating properties, was asso-
ciated with a relative risk of 1.59 (CI95 1.24–2.04) for the occurrence of constipation during
exposure time. Use of drugs with mildly to moderately constipating effects was not associated
with laxative use (RR 1.13; CI95 0.93–1.38). Stratification on the level of age, gender, type of nur-
sing (psychogeriatric or somatic), morbidity, number of medications taken and mobility showed
no confounding effects of these variables on outcome measurements. These variables all acted
as effect modifiers. Effect of age and number of medications taken on the relative risk was non-
linear.
Conclusions: Although an association between drugs that exhibit moderately to strongly con-
stipating effects and occurrence of constipation was found, the risk was not as high as seen in
previous studies. The high prevalence of constipation in nursing home patients is only partly
due to adverse drug effects.
118
Exposure def in i t ion
Drugs were classified into three categories: category 2: drugs that exhibit moderately to
strongly constipating effects (see Appendix A), category 1: drugs that exhibit mildly to modera-
tely constipating effects (see Appendix B) and a reference category which contained all other
drugs. For each drug the summary of product characteristics (SPC), edited and approved by the
Dutch Medicines Evaluation Board [15], provided the main source for the classification of the
constipating effects of the drugs used by the study population, together with specific informa-
tion on adverse drug effects from the literature [16,17]. Each resident’s exposure time was defi-
ned as the duration of drug use from category 1 or 2, respectively. To control for residual effects
we performed both a study in which we defined exposure time as the duration of drug use plus
the first 14 days after every exposure period and a study in which we excluded the first 14 days
after every exposure period from both exposure time and nonexposure time. To investigate if
any differences in constipating properties exist between certain of the subgroups of drugs from
category 2, we performed subgroup analyses on pharmacological subgroups (see table 4). Non-
exposure time was defined as the remainder of the period of stay during the study period.
Exposure days to category 2 drugs, to category 1 drugs and nonexposure days were aggregated
over the study population.
Case def in i t ion
The occurrence of constipation was identified by the start of a laxative, which is considered
a proxy drug. When the start of a laxative coincided with the start of a drug from category 2 or
1, the start was considered as a prophylactic start; these starts were not considered as cases.
When the start of a laxative coincided with the date of admission to the nursing home or with
the first day of the study period, the start was not considered as a case either. Patients were
considered to be ‘at risk’ for constipation during the period of stay in which they did not use a
laxative.
Analys is
Incidence rates during exposure and nonexposure time, respectively, were calculated by
dividing the number of starts of laxative use by the total number of person-days at risk both
during exposure time and during nonexposure time at risk. The incidence relative risk is deter-
mined as Iexp /Inonexp. Mantel-Haenszel relative risks were calculated to control for potential
confounding effects of age, gender, morbidity (Parkinson’s disease, diabetes mellitus, depres-
sion and dementia), type of nursing (psychogeriatric or somatic), number of medications taken
and mobility. All statistical analyses were performed in SPSS for Windows [18]. Incidence rela-
tive risks were calculated with corresponding 95% confidence intervals (CI95).
121
observation that a side effect of drug A is followed by the prescription of drug B (a ‘proxy’ drug)
if drug B is used to counteract the side effect caused by drug A [12]. In this study, laxative drugs
(all drugs in the Anatomical Therapeutic Chemical (ATC) classification A06 and A02AA02 [13])
were considered proxy drugs for constipation.
Sett ing
The study was undertaken in six nursing homes for long-term care with a 1030-bed capaci-
ty in the northern part of the Netherlands. In these nursing homes medical care is provided by
nursing home physicians, who give medical care on a daily basis. Specialists’ medical input is
obtained on demand. A distinction is made between care for psychogeriatric residents and care
for somatic residents. Nursing-, physician- and pharmacist care is comparable between the
nursing homes. In each nursing home nursing staff defined constipation as not having defeca-
ted for more than 3–5 days. Fluid- and fibre intake was comparable between the nursing
homes.
Data col lect ion
For each resident, pharmacy records of a two-year period and individual morbidity and
mobility data were collected. Pharmacy data included the generic name, strength, dosage, the
frequency of use and the route of administration of the drugs, the prescription length (in days)
and the following patient characteristics: age, gender, date of admission and date of discharge.
Drugs were classified according to the ATC classification system [13]. Dermatological prepara-
tions were excluded from the analyses. Pharmacy records were linked with a national infor-
mation system on nursing homes (SIVIS) [14], to collect the following data: type of nursing
(psychogeriatric or somatic), morbidity and mobility.
Study populat ion
All nursing home residents from six nursing homes were initially included in the cohort.
The study population consisted of 2,772 residents aged 65 years and over who were present at
any time during the two-year study period from 1 October 1993–1 October 1995. We excluded
patients who could not be linked to data from the SIVIS-system (14.2%), and subsequently
patients whose period of residence could not be defined as a result of missing data (1%). This
resulted in a final study population of 2,355 patients. Of these patients 65% were newly admit-
ted during the study period.
120
Laxat ive use
Fifty-six percent of the study population used a laxative at some time during stay at the
nursing home. An overview of the laxatives used by the study population is given in table 2.
Seventy-four percent of the residents with Parkinson’s disease used a laxative. At the entry of
the study period, 416 (18%) of the patients used a laxative. After the study entry date 1109
(47%) patients started a laxative for one or more periods. Of these patients 233 (21%) used a
laxative for a period of less than 30 days, and 876 (79%) used a laxative for a period of 30 days
or more. The average duration of laxative use was 154 days (SD 192). On average, people who
were on a laxative drug used it for more than 77% of their nursing home stay. Relatively high
dosages of laxatives were used.
Inc idence rate rat ios
The results from the cohort study are presented in table 3. Use of drugs from category 2
(moderately to strongly constipating drugs) was associated with a relative risk of 1.59 (CI95
1.24–2.04) for the occurrence of constipation and the incidence relative risk of exposure to cate-
gory 1 drugs (mildly to moderately constipating drugs) was 1.13 (CI95 0.93–1.38) compared with
nonexposure. To control for residual effects we performed both a study in which we defined
exposure time as the duration of drug a use plus the first 14 days after every exposure period
and astudy in which we excluded the first 14 days from both exposure time and nonexposure
time. When exposure time was defined as the duration of category 2 drug use plus the first 14
days after this period, the incidence relative risk was slightly higher (RR 1.69; CI95 1.33–2.15),
indicating a carry-over effect from category 2 drug exposure in the nonexposure period. To
exclude this carry-over effect, we deleted the first 14 days after exposure time from both expos-
123
Results
Populat ion character is t i cs
The mean age of the study population was 82 years (SD 7.3). The average residence time
during the study period was 257 days (SD 260). The average number of different medicines
(based on ATC-codes) per person was 8.9 during residence in the nursing home (SD 4.9; der-
matological preparations excluded). The average number of different medicines per patient per
day was 4.9. Most drugs were used for more than 50% of the duration of stay in the nursing
home. In table 1, characteristics of the study population are given. Of the mobile residents 35%
were diagnosed with dementia, while 22% of the immobile residents were diagnosed with
dementia. Forty-six percent of the study population used a drug from category 2; 57% of the
study population used a drug from category 1.
122
Table 1: Characteristics of the study population (n=2355)
Variable Number of residents (percentage of total)
Age (years)
65-74 415 (18%)
75-84 1012 (43%)
≥ 85 928 (39%)
Gender
Male 689 (29%)
Female 1666 (71%)
Type of nursing
Psychogeriatric 700 (30%)
Somatic 1609 (68%)
Not known 46 (2%)
Morbidity
Parkinson's disease 151 (6%)
Diabetes mellitus 176 (7%)
Depression 40 (2%)
Dementia 689 (29%)
Number of different medicines
0-5 626 (27%)
6-10 969 (41%)
> 10 760 (32%)
Mobility
Mobile 1370 (58%)
Immobile 985 (42%)
Table 2: Laxatives used by the study population (n=2355)
Laxative Number of of patients¶ (%)
Lactitol 871 (37%)
Lactulose 346 (15%)
Bisacodyl 336 (14%)
Magnesium oxide 140 (6%)
Docusate sodium 91 (4%)
Triticum 90 (4%)
Ispaghula (psylla seeds) 70 (3%)
¶ Note: patients may use more than one laxative
125
ure and nonexposure time, which resulted in an incidence relative risk of 1.60 (CI95 1.25–2.06).
Results of the subgroup analyses are given in table 4. Point estimates varied from 1.01 (opiates)
to 1.92 (verapamil) but the differences were not all statistically significant. Ninety-six percent
of the people who received opiates received a laxative drug prior to the initiation of opiate use.
Statistical analysis of possible confounding effects of the variables given in table 1 showed no
confounding effects from these variables as shown in table 5. Gender, morbidity and mobility
acted as effect modifiers. There was a non-linear association with age and with the number of
medications taken. Residents with depression and residents with diabetes mellitus were more
at risk for the occurrence of constipation as an adverse drug effect while residents with
Parkinson’s disease showed a markedly lower risk. Residents who were relatively mobile
showed a higher risk for the occurrence of drug-induced constipation.
124
Table 3: Relative risks for the occurrence of constipation with different drug categories
Drug category Events* Time at risk (days) Relative risk (RR) CI95
2: moderately to strongly constipating 84 30931 1.59 1.24-2.04
1: mildly to moderately constipating 179 92339 1.13 0.93-1.38
Reference drug category 236 137835 1.00
* Events: number of starts of a laxative. This was considered a marker for constipation
Table 4: Subgroup analyses of drug groups from category 2
Drugs under study Events* Time at risk (days) Relative risk (RR) CI95
Opiates 5 2880 1.01 0.42-2.46
Morphine, nicomorphine, pethidine,
dextropropoxyphene
Calcium channel blockers 10 3042 1.92 1.02-3.62
Verapamil
Calcium salts and ferrous salts 54 18947 1.67 1.24-2.24
Anticholinergic agents
Atropine, biperiden, orphenadrine, 10 5621 1.04 0.55-1.96
oxybutynine, oxyphencyclimine,
thiazinamium, trihexyphenidyl
Drugs with anticholinergic side effects 58 22244 1.52 1.14-2.03
Amitryptyline, disopyramide,
chlorpromazine, chlorprotixene,
clozapine, clomipramine,
doxepine, flavoxate, imipramine,
maprotiline, nortriptyline, thioridazine
Reference drug category 236 137835 1.00
* Events: number of starts of a laxative. This was considered a marker for constipation
Table 5: Relative risks for the occurrence of constipation associated with exposure to category 2 drugs, stratified for
age, gender, type of nursing, morbidity, number of medications and mobility
Diseased (n) Time at risk (days) Relative risk RR Mantel-Haenzsel
Variable exposed unexposed exposed unexposed (CI95) (CI95)
Age (years)
65-74 17 46 5089 23300 1.69 (0.97-2.95)
75-84 38 95 13820 49055 1.42 (0.98-2.07)
≥ 85 29 95 12022 65480 1.66 (1.10-2.52)
overall 84 236 30931 137835 1.59 (1.24-2.04) 1.55 (1.21-1.99)
Gender
Male 31 69 7428 37380 2.26 (1.48-3.45)
Female 53 167 23503 100455 1.36 (1.00-1.85)
overall 84 236 30931 137835 1.59 (1.24-2.04) 1.60 (1.25-2.05)
Type of nursing
Psychogeriatric 17 66 8614 60166 1.79 (1.05-3.06)
Somatic 64 163 20438 69275 1.33 (1.00-1.78)
Not known 3 7 1852 8394 1.94 (0.50-7.51)
overall 84 236 30931 137835 1.59 (1.24-2.04) 1.47 (1.15-1.89)
Morbidity
Parkinson's disease 5 20 2486 5976 0.60 (0.23-1.60)
Diabetes mellitus 7 12 2276 10790 2.77 (1.09-7.02)
Depression 3 5 355 3077 5.20 (1.24-21.76)
Dementia 17 66 11410 61387 1.39 (0.81-2.36)
overall 32 103 16527 81240 1.53 (1.03-2.27) 1.40 (0.95-2.07)
Number of medications
0-5 9 54 4992 48008 1.60 (0.79-3.25)
6-10 41 101 12721 51 1 31 1.63 (1.14-2.35)
>10 34 81 13218 38696 1.23 (0.82-1.83)
overall 84 236 30931 137835 1.59 (1.24-2.04) 1.45 (1.13-1.86)
Mobility
Mobile 53 137 18272 89477 1.89 (1.38-2.60)
Immobile 31 99 12659 48358 1.20 (0.80-1.79)
overall 84 236 30931 137835 1.59 (1.24-2.04) 1.57 (1.22-2.00)
remained unclear. In a cross-sectional study Monane [1] found a strong association between
laxative use and the use of highly anticholinergic antidepressants (OR 3.12; CI95 1.21–8.03) in
nursing home patients. Also in a cross-sectional study Harari [11] demonstrated that the use of
iron supplements and calcium channel blockers was significantly associated with laxative use
(OR 2.2 and OR 1.9, respectively) in elderly people residing in a long-term care setting. To our
knowledge, our study is the first that uses a cohort design to determine the association
between medication use and laxative administration in a nursing home population. In a cohort
design, laxative use as a result of medication use (sequential use) can be properly assessed
with prescription sequence analysis. With cross-sectional methods, temporal sequences of pre-
scribing are more difficult to assess [12].
Poss ib le b ias
Selection bias. We excluded all nursing home residents for whom data were incomplete or
invalid. Since they represented a small proportion of the population, this is not likely to have
influenced the results. Information for each resident was obtained from the same data set.
Therefore it is unlikely that selection bias played a role in this study.
Information bias. Information bias might occur when a laxative is prescribed for an indica-
tion other than constipation. This could lead to a bias away from the null in both the exposed
and nonexposed group. As the only other indication for the prescription of lactulose is hepatic
(pre) coma, a very rare disease, it is not likely that it leads to differential misclassification. Also
a laxative could be withheld from a patient suffering from constipation. This could lead to a bias
towards the null. This kind of bias is not likely to occur often because residents are frequently
monitored by nurses or carers, so constipation will be noticed at an early stage. However, this
kind of bias could be relevant when the problem of constipation is considered (more constipa-
ted residents), but probably is not relevant when drug-induced constipation is considered (bias
will occur in both exposed and nonexposed groups). Medication use of the individual nursing
home resident is recorded centrally in one of the three computerised hospital pharmacies
involved in our study. Dispensing of drugs takes place when the registration of medication is
complete. Therefore information bias is not likely to occur.
Confounding bias. We tried to control for possible confounding patient characteristics such
as age, gender, type of nursing, number of medications taken, morbidity and mobility. None of
these variables was considered a confounder although some of the variables were considered
effect modifiers (see below). No marked differences were seen in overall fluid and fibre intake
among the different nursing homes. Because we could not collect data on fluid and fibre inta-
ke at individual patient level, this may still confound our results. The influence of fluid and fibre
127
Discuss ion
Our study confirms earlier findings of a risk of constipation as a consequence of drug use.
However, from this cohort study the relative risk appears to be lower than has been suggested
in previous case control and cross-sectional studies. In 2,355 nursing home patients, the use of
drugs that exhibit moderately to strongly constipating effects was slightly but significantly
associated with the start of a laxative (RR 1.59; CI95 1.24–2.04). When the first 14 days after
every exposure period were added to the exposure time, the relative risk was slightly higher
(RR 1.69; CI95 1.33–2.15), which indicates residual effects of these drugs depending on the eli-
mination half-life. The results show that drugs, which according to the summaries of product
characteristics and to the literature on adverse drug effects exhibit a moderately to strongly
constipating effect, in practice are only marginally associated with the occurrence of constipa-
tion. However, the fact that drugs from category 2 are used by nearly half of the study popula-
tion at least once during the study period suggests that this side effect could be clinically rele-
vant in daily practice because it concerns many residents. Drugs that have been reported to
have mild to moderate constipating effects were not associated significantly with constipation
(RR 1.13; CI95 0.93–1.38). This means that although constipation is mentioned as a possible side
effect in the summaries of product characteristics and in the literature, the high prevalence of
constipation is probably not due to use of drugs from this category. Subgroup analyses demon-
strated that the use of the calcium channel blocker verapamil and the use of calcium- and fer-
rous salts, especially, were associated with a high risk for the occurrence of constipation. The
fact that laxatives are given prophylactically with this drug category explains why use of opia-
tes was not associated with a higher risk. Several reports support our results. Mikus and colle-
agues recently showed that the constipating effect of codeine is only seen in extensive meta-
bolizers (CYP2D6 phenotype) [20]. In a literature search (1965–94) concerning adverse events
associated with antidepressant drugs, constipation did not belong to one of the 27 most fre-
quently reported adverse events [21]. In a community based study no significant association
was found between antidepressant drug use and use of laxatives [22].
Previous studies
In the study of Stewart [2], a positive correlation was demonstrated between self-reported
constipation and the total number of drugs used in an ambulatory elderly population, but no
specific drug groups were correlated with constipation. Talley [3] demonstrated that use of non-
steroidal anti-inflammatory drugs was a significant risk factor in elderly subjects with both
functional constipation and outlet delay. However, whether this was a causal association
126
use of newer antidepressants (such as selective serotonin reuptake inhibitors) and antipsy-
chotics with minor or no anticholinergic activity could be considered as alternatives to antide-
pressants and antipsychotics with anticholinergic side effects.
In conclusion, this study demonstrates that medication, which according to their SPCs and
the literature on adverse drug effects exhibit moderately to strongly constipating effects, is
associated with the occurrence of constipation in a cohort of nursing home patients. However,
the risk is not as high as previous studies suggest. Drugs that were classified as mildly to mode-
rately constipating showed no increased risk for the occurrence of constipation. The high pre-
valence of laxative use in nursing home patients is only partly due to adverse drug effects. To
minimize the risk for constipation, alternative pharmacotherapy could be considered for certain
subgroups of drugs.
Acknowledgements
We express our gratitude to D.A. Bloemhof, hospital pharmacist, for supplying pharmacy
data; SIG Informatics on Health and Welfare, Utrecht, for supplying patient morbidity and
mobility data; and nursing, medical and pharmacy staff from all participating nursing homes
for their co-operation.
129
intake on constipation has been assessed in only few studies. Although dietary fibre is often
diminished in the elderly, no clear association has been made with true clinical constipation [7].
There are no data on dehydration as a risk factor for constipation in the elderly although the
beneficial effect of fluid intake has been proven in young male volunteers [7]. A community
based cohort study by Towers showed that constipation was related to caloric intake rather
than fibre consumption or fluid intake [19]. In our study a resident may develop constipation as
a consequence of low fluid and fibre intake. When this patient is using drugs from category 1
or 2, this patient would be wrongly considered as a ‘case’. This might lead to a overestimation
of the relative risk but it does not change our conclusion. Other possible confounders such as
age and co-morbidity did not play a role in our study. Obviously, we can never rule out confou-
ding effects of factors that we are not aware.
Ef fect modif iers
Several other factors have been reported to be associated with constipation in the elderly.
Bowel frequency is decreased with certain neurological and endocrine disorders such as
Parkinson’s disease and diabetes mellitus. Older people and women are reported to be more at
risk for constipation. The type of nursing, psychogeriatric or somatic, can influence outcome. In
several studies, inactivity and immobility have been identified as risk factors for constipation.
Although data in the elderly are scarce, Towers [19] showed that constipated elderly tend to
report less regular activity and exercise. Therefore we stratified for the variables age, gender,
type of nursing, morbidity, number of medications taken and mobility to control for possible
confounding effects. Gender, morbidity and mobility acted as effect modifiers. The non-linear
association with age and number of medications taken suggests a combined effect of effect
modification and confounding by these variables. Men were at a higher risk for the occurrence
of constipation during category 2 drug exposure in comparison with women. Residents suffe-
ring from Parkinson’s disease showed a markedly lower relative risk, probably because these
residents are already constipated. Patients with depression and diabetes mellitus showed a
higher risk for the occurrence of constipation as an adverse drug effect. Finally, residents who
were relatively mobile were more at risk for the occurrence of constipation during category 2
exposure. When pharmacotherapy is needed, the possible constipating effects of category 2
drugs should be taken into account with special reference to the patients’ gender, co-morbidi-
ty, number of medications taken and mobility. In these risk groups in particular the use of cate-
gory 2 drugs is associated with the occurrence of constipation. Furthermore, alternative phar-
macotherapy could be considered with certain subgroups of category 2 drugs. For example, the
necessity of verapamil, calcium salts and ferrous salts should be (re-)evaluated carefully. The
128
131
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9 Riedel WJ, Van Praag HM. Avoiding and managing anticholinergic effects of antidepressants.
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13 Anonymous. Anatomical Therapeutic Chemical (ATC) classification index. Oslo: WHO collaborating centre for
drug statistics methodology, 1994.
14 SIG. Informatics in Health and Welfare, Utrecht, the Netherlands.
15 Dutch Medicines Evaluation Board, Rijswijk, the Netherlands.
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17 Dukes MNG (ed). Meyler’s side effects of drugs, twelfth edition, Amsterdam: Elsevier Science Publishers 1992.
18 Norusis MJ SPSS. 6.1. Guide to data analysis. New Jersey: Prentice-Hall Inc. Englewood Cliffs.
19 Towers AL, Burgio KL, Locher JL, Merkel IS, Safaeian M, Wald A. Constipation in the elderly: influence of dietary,
psychological, and physiological factors. J Am Geriatr Soc 1994; 42: 701–6.
20 Mikus G, Trausch B, Rodewald C, Hofmann U, Richter K, Gramatte T, et al. Effect of codeine on gastrointestinal
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130
Appendix A: Drugs classified as moderately to strongly constipating [15–17]
ATC-code [13] Generic name
A02BX02 Sucralfate
A03AA01 Oxyphencyclimine
A03BA01 Atropine
A07DA03 Loperamide
A12AA03 Calcium gluconate
A12AA04 Calcium carbonate
A12AA05 Calcium lactate
A12AA20 Calcium, combinations
B03AA02 Ferrous fumarate
B03AA07 Ferrous sulphate
B04AD01 Colestyramine
C01BA03 Disopyramide
C08DA01 Verapamil
G04BD02 Flavoxate
G04BD04 Oxybutynin
N02AA01 Morphine
N02AA04 Nicomorphine
N02AB02 Pethidine
N02AC04 Dextropropoxyphene
N04AA01 Trihexyphenidyl
N04AA02 Biperiden
N04AB02 Orphenadrine
N05AA01 Chlorpromazine
N05AC02 Thioridazine
N05AF03 Chlorprothixene
N05AH02 Clozapine
N06AA02 Imipramine
N06AA04 Clomipramine
N06AA09 Amitriptyline
N06AA10 Nortriptyline
N06AA12 Doxepin
N06AA21 Maprotiline
NO6CA01 Amitriptyline plus neuroleptic
R06AD06 Thiazinamium
3.2 Potent ia l interact ion between acenocoumarol and dic lofenac , naproxen and ibuprofen and the ro le of CYP2C9 genotype
K.N. van Dijk1,2, A.W. Plat1, A.A.C. van Dijk1, G. Piersma-Wichers3,A.M.B. de Vries-Bots4, J. Slomp5, L.T.W. de Jong-van den Berg1, J.R.B.J. Brouwers1,4
1 Department of Social Pharmacy, Pharmacoepidemiology and Pharmacotherapy, Groningen
University Institute for Drug Exploration (GUIDE), University Centre for Pharmacy,
Groningen, the Netherlands2 Department of Clinical Pharmacy, Medical Centre Leeuwarden, Leeuwarden, the Netherlands3 Groningen Outpatient Thrombosis Service, Groningen, the Netherlands4 Community Pharmacy ‘t Hooge Zand, Hoogezand, the Netherlands5 Department of Clinical Chemistry, Stichting Klinisch Chemisch Laboratorium, Leeuwarden,
the Netherlands
Submitted
133132
Appendix B: Drugs classified as mildly to moderately constipating [15–17]
ATC code [13] Generic name
A03AB03 Oxyphenonium
A03BB01 Scopolamine
A04AA01 Ondansetron
C02AC01 Clonidine
C03CA01 Furosemide
C03CA02 Bumetanide
G02CB02 Lisuride
N02AD01 Pentazocine
N02AE01 Buprenorphine
N02AX02 Tramadol
N03AB02 Phenytoin
N03AE01 Clonazepam
N04BC01 Bromocriptine
N05AA02 Methotrimeprazine
N05AB03 Perphenazine
N05AD05 Pipamperone
N05AF01 Flupenthixol
N05AF05 Zuclopenthixol
N05AG02 Pimozide
N05AL01 Sulpiride
N05AX08 Risperidone
N05BX01 Mephenoxalone
N06AA01 Desipramine
N06AX03 Mianserin
N06AX05 Trazodone
N06AX11 Mirtazepine
R05DA04 Codeine
R06AB02 Dexchlorpheniramine
R06AD02 Promethazine
Int roduct ion
Oral anticoagulation with coumarin derivatives is widely used in the treatment and pro-
phylaxis of patients with thromboembolic diseases [1]. In Europe, acenocoumarol and phen-
procoumon are the drugs most commonly used. In view of the narrow therapeutic range and
the marked inter- and intra-individual variability, the intensity of anticoagulation is monitored
by measuring the prothrombin time [2]. The result of prothrombin time monitoring is expres-
sed as the International Normalised Ratio (INR). The anticoagulant effect of coumarins is in-
fluenced by many drug-food and drug-drug interactions [3]. Among the non-steroidal anti-
inflammatory drugs (NSAIDs), azapropazon and phenylbutazon are contraindicated in patients
treated with coumarins, because they strongly increase the INR as a result of a pharmacokine-
tic interaction [4]. The inhibition of the cytochrome P450 2C9-(CYP2C9) mediated metabolism
of coumarins probably plays a role. Also, displacement of protein binding sites may lead to ele-
vated coumarin levels. Other NSAIDs such as diclofenac, ibuprofen and naproxen have until
now not reported to result in an increase of the INR of patients treated with coumarins.
However, as a result of a pharmacodynamic interaction the risk of bleeding may also be incre-
ased because of the inhibition of thrombocyte aggregation [4]. Furthermore damage of the gas-
trointestinal mucosa may lead to subsequent risk of gastrointestinal bleeding. In view of these
adverse effects, these NSAIDs should only be used in combination with coumarins when no
alternatives are available and patients are advised to monitor an increased bleeding sensitivity.
In the Netherlands, the monitoring of outpatients on oral anticoagulant treatment is con-
ducted by Thrombosis Services [5]. At the Groningen Outpatient Thrombosis Service, physi-
cians occasionally noticed an increase in INR when NSAIDs such as dicofenac, naproxen and
ibuprofen were added to coumarin therapy. In view of the risks of an increased INR, such as
haemorrhage, we investigated the influence of NSAID therapy on the INR in more detail.
Furthermore, we studied the influence of several patient characteristics. It has been suggested
that drug-drug interactions may be partly due to genetic variability [6]. The role of pharmaco-
genomics, defined as the individualisation of drug therapies based on genetic information, in
the prevention of adverse drug reactions has been highlighted in several reviews [6-8]. It was
found that 59% of the drugs cited in studies on adverse drug reactions are metabolised by at
least one enzyme with a variant allele known to cause poor metabolism [6]. Recently it has
been demonstrated that (R)-acenocoumarol, the enantiomer that contributes mainly to the
pharmacological effect is metabolised by CYP2C9 [9]. Diclofenac, naproxen and ibuprofen are
also metabolised by CYP2C9 [10-13]. Furthermore, it has been described that the age-related
decrease in the content and function of CYP2C9 [14] could contribute to drug-drug interactions,
135
Abstract
Objective: To investigate the influence of diclofenac, naproxen and ibuprofen on the Inter-
national Normalised Ratio (INR) of outpatients stabilised on acenocoumarol therapy. To deter-
mine the role of cytochrome P450 2C9 (CYP2C9) polymorphism on coumarin dosage and INR in
NSAID users.
Methods: The study was carried out at the Groningen Outpatient Thrombosis Service. A retro-
spective study among patients who received both acenocoumarol and one of the NSAIDs under
study was performed. Patients whose INR rose above the upper level of the therapeutic range
(> 3.5) after adding an NSAID under study to the acenocoumarol therapy, were compared with
patients who did not show such an elevation. A two-sample t-test (average acenocoumarol
dosage, age), and chi-square tests (sex, therapeutic range, type of NSAID) were used to test for
differences. Genotyping was carried out by analysing blood samples for the relevant CYP2C9
alleles.
Results: The study population consisted of 112 patients on stable acenocoumarol therapy, of
which 52 (46%) showed an elevation of the INR above the desired therapeutic level (INR 3.5
and 4.0 respectively). In 12 patients, the INR increased above 6. The INR of the other 60 patients
(54%) remained constant after the start of one of the NSAIDs under study. There were no sta-
tistically significant differences between patients with increased INR and patients without
increased INR with regard to age, sex, therapeutic range and average acenocoumarol dosage.
Eighty patients, of whom 36 showed an increased INR as a result of a potential acenocouma-
rol-NSAID drug interaction, were included in the genotyping study. No association between
CYP2C9 genotype and an increased INR as a result of the drug-drug interaction was found.
Conclusion: In nearly half of a cohort of elderly patients, the INR increased beyond the thera-
peutic range (INR 3.5 or 4.0) as a result of a potential pharmacokinetic drug-drug interaction
between acenocoumarol and diclofenac, naproxen and ibuprofen. The average increase in INR
was between 1 and 4. Risk factors identifying patients most likely to be at risk for this potential
drug-drug interaction, such as polymorphism of CYP2C9, could not be found.
134
Data col lect ion
All report forms that were issued during the period 01-10-1999 and 01-05-2000 and concer-
ned one of the three NSAIDs under study, were studied. For each patient, the following data
were collected: age and sex, type of NSAID (diclofenac, naproxen or ibuprofen), dosage of ace-
nocoumarol, INR values before and after the administration of the NSAIDs under study, and the
therapeutic range in which the patient was maintained (low (2.5-3.5) or normal (3.0-4.0)).
Data analys is
For each patient a time-profile of the INR was constructed. Patients whose INR increased
above the upper level of the therapeutic range (3.5 and 4.0 respectively, depending on the the-
rapeutic range) after starting diclofenac, naproxen or ibuprofen in addition to the acenocou-
marol therapy, were compared with patients who did not show such an increase. For each
patient, the increase in INR was calculated as the first INR measurement after adding the
NSAID minus the last INR measurement before adding the NSAID. Also, for each patient the
average dosage of acenocoumarol before administration of the NSAID was determined. The
average dosages of patients who showed an elevation in the INR and patients who did not
show such an elevation were subsequently calculated for each therapeutic range.
The statistical software program SPSS 10.0 for Windows (SPSS Inc., Chicago, IL) was used.
A two-sample t-test (acenocoumarol dosage, age) and chi-square tests (sex, therapeutic range,
type of NSAID) were used to test for differences.
Genotyping study
A genotyping study was carried out to investigate whether CYP2C9 polymorphism was
associated with the occurrence of the potential interaction between acenocoumarol and the
NSAIDs under study. Participants were recruited from patients attending the Groningen
Outpatient Thrombosis Service who were initially included in the cohort study (n=112). The
Medical Ethical Committee of the ‘Stichting Beoordeling Ethiek Bio-Medisch Onderzoek’ at
Assen, the Netherlands, approved the study (July 06, 2001). The general practitioner (GP) of
each patient was contacted by telephone and by fax with details of the proposed investigation
and was asked written consent to contact their patient for inclusion in the genotyping study.
Patients were included in the study after written informed consent was obtained. At their regu-
lar visit for INR control, 4 ml blood was collected in EDTA tubes. DNA was isolated from blood
within 72 hours after blood was collected (High Pure PCR Template Preparation Kit, Roche).
Polymerase Chain Reaction (PCR) for the CYP2C9 variants was performed with subsequent Ava
II digestion (CYP2C9*2) and Nsi I and Kpn I digestion (CYP2C9*3). CYP2C9*1/*3 and CYP2C9*2
137
which could be relevant in an elderly population. Polymorphism of CYP2C9 could therefore be
a risk factor for developing a clinical relevant drug-drug interaction between acenocoumarol
and NSAIDs.
The aim of our study was to investigate whether diclofenac, naproxen or ibuprofen influ-
enced the INR of acenocoumarol users. Furthermore, we determined whether polymorphism of
CYP2C9 is associated with this potential drug-drug interaction.
Methods
Sett ing
The study was carried out at the Groningen Outpatient Thrombosis Service, which serves
about 11,000 patients in the province of Groningen, the Netherlands. In this anticoagulation
clinic, all activities with regard to monitoring and treatment of patients on coumarin therapy
are organised. For example, blood sampling is performed and laboratory determinations are
carried out. An especially trained physician establishes the dose of the coumarin using a com-
puter dosage program. Letters with the recommended dosages are subsequently sent to the
patient. Patient and medical data are stored in a centralised database (Trombosedienst
Information System; TDAS). Two oral anticoagulants are available in the Netherlands: aceno-
coumarol and phenprocoumon. Two therapeutic ranges are distinguished: a first intensity level
(INR therapeutic range 2.5-3.5), and a second intensity level (INR therapeutic range 3-4). The
indication and pathophysiology determines in which therapeutic range a patient is maintained.
Patients with venous thromboembolic disorders, such as atrial fibrillation, are mostly maintai-
ned in the first intensity level and patients with arterial thromboembolic diseases are mostly
maintained in the second intensity level. Patients are maintained in the therapeutic range by
regular checks of their INR. A special feature of the Groningen Outpatient Thrombosis Service
is that all reports concerning potential coumarin-drug interactions are systematically recorded
in a database.
Study populat ion
The study population consisted of outpatients on stable acenocoumarol treatment who
received one of the NSAIDs under study. Stable acenocoumarol treatment was defined as main-
tenance of the INR within limits of the therapeutic range. Patients, who used more drugs bes-
ides the NSAID, were excluded.
136
139
were detected by PCR-mediated site-directed mutagenesis followed by restriction analysis
according to the methods described by Wang and colleagues (*1/*3) [15] and Steward and col-
leagues (*2) [16] with slight modifications. Positive controls were included for method valida-
tion.
Results
The source population consisted of 244 patients. Of these patients, 132 (54%) were exclu-
ded because they used more medications besides the NSAID and acenocoumarol, leading to a
study population of 112 patients. There were no patients who used more than one NSAID. Of the
112 patients, 52 (46%) showed an elevation of the INR above the upper level of the therapeu-
tic range after adding diclofenac, naproxen or ibuprofen to acenocoumarol therapy. The INR of
the other 60 patients (54%) remained constant after the start of one of the NSAIDs under
study. In table 1, patient characteristics of the study population are given. There were no statis-
tically significant differences between patients with increased INR and patients without in-
creased INR for the following variables: age, sex, type of NSAID (diclofenac, naproxen or
ibuprofen), therapeutic range, average dosage of acenocoumarol (before the NSAID was
added) and CYP2C9 genotype. In table 2 the average increase in INR is given, stratified for thera-
peutic range and type of NSAID. In twelve patients the INR was 6 or higher. In figure 1, a time-
profile is given, illustrating the elevation in INR after adding diclofenac in one patient.
138
Table 1: Characteristics of the study population (n=112) and differences between acenocoumarol users with increased
INR and acenocoumarol users without increased INR, and results of the genotyping study (n=80)
Variable Patients with increased INR (n=52) Patients without increased INR (n=60)
n (%) n (%)
Sex#
Male 21 (40.4) 26 (43.3)
Female 31 (59.6) 34 (56.7)
Age; average (± SD) # 72.2 (± 14.1) 70.9 (± 11.3)
Age distribution
<60 9 (17.3) 11 (18.3)
60-69 9 (17.3) 15 (25.0)
70-79 17 (32.7) 24 (40.0)
≥ 80 17 (32.7) 10 (16.7)
Therapeutic range#
2.5-3.5 30 (57.7) 34 (56.7)
3.0-4.0 22 (42.3) 26 (43.3)
Type of NSAID#
Diclofenac 32 (61.5) 34 (56.7)
Naproxen 8 (15.4) 16 (26.7)
Ibuprofen 12 (23.1) 10 (16.7)
Average acenocoumarol
dosage (mg) before
NSAID was added#
overall 2.7 3.1
2.5-3.5 2.8 2.9
3.0-4.0 2.6 3.2
CYP2C9 genotype¶
CYP2C9*1/*1 26 (72.2) 30 (68.2)
CYP2C9*1/*2 6 (16.7) 9 (20.5)
CYP2C9*2/*2 0 2 (4.5)
CYP2C9*1/*3 3 (8.3) 3 (6.8)
CYP2C9*2/*3 1 (2.8) 0
¶ Not all patients of the cohort (n=112) were included in the genotyping study (see also under Results): 36 patients
with increased INR; and 44 patients without increased INR# None of the differences between the two groups were statistically significant (p>0.05)
Genotyping study
All GPs gave consent to contact their patients. Subsequently, 80 patients gave their written
informed consent to participate in the study. Thirty-one patients could not be included, due to
different reasons such as hospital admission (n=5) or lost to follow-up (n=26). One patient did
not give informed consent. In table 3, the characteristics of the patients who were included in
the genotyping study are given. The CYP2C9 genotype distribution is summarised in table 1. Ten
patients (27.8%) in the case group were carriers of one or two of the allelic variants of CYP2C9
compared with 14 (31.8%) of the patients in the control group (Wilson test for difference: CI95
-0.16-+0.23). Among the patients with increased INR, the allele frequencies of CYP2C9 were
84.7%, 9.7% and 5.6% for the variants CYP2C9*1, *2 and *3, respectively. Among patients
without increased INR, the allele frequencies were 81.8%, 14.8% and 3.4% for the variants
CYP2C9*1, *2 and *3, respectively. The differences between both groups in allele frequency
were not statistically significant. For the study group as a whole (n=80), the allele frequencies
were 83.1%, 12.5% and 4.4% for the variants CYP2C9*1, *2 and *3, respectively. When we com-
pared patients having the wild-type genotype (*1/*1) with patients having one of the allelic
variants (*2 and/or *3), it was found that the latter group had a significantly lower dosage of
acenocoumarol (2,3 mg versus 3,0 mg; p=0.023).
141140
Table 2: Average increase of INR stratified for type of NSAID and therapeutic range in the study population (n=112)
Average increase in INR (range)#
Therapeutic range Diclofenac (n=32) Naproxen (n=8) Ibuprofen (n=12)
2.5 –3.5 + 2.3 (0.5-8.4) + 2.8 (0.5-5.3) + 3.8 (2.0-8.6)
(n=17) (n=7) (n=6)
3.0-4.0 + 2.2 (0.3-4.8) + 1.1 + 1.5 (0.4-2.4)
(n=15) (n=1) (n=6)
# defined as the difference between the last INR (among patients who showed an elevation in the INR) before the
start of an NSAID and the first INR after the start of an NSAID.
3,63,8
4,13,9
3,33,3
3,83,7
6,4
0
1
2
3
4
5
6
7
0 28 56 84 112 140 168 196 224 252
Time-intervals (days)
INR
INR
Min.th.value
Max.th.value
NSAID added²
Figure 1: Patient INR-profile showing an elevation in INR after adding diclofenac to coumarin therapy.
Table 3: Characteristics of the patients who were included in the genotyping study (n=80)
Genotype CYP2C9
Variable *1/*1 *1/*2 *2/*2 *1/*3 *2/*3
n(%) 56 (70) 15 (18.8) 2 (2.5) 6 (7.5) 1 (1.3)
Sex
Female 33 (58.9) 12 (80) 0 2 (33.3) 0
Male 23 (41.1) 3 (20) 2 (100) 4 (66.7) 1 (100)
Age 70.8 73.7 70.0 70.8 65.0
Therapeutic range
2.5-3.5 26 (65) 9 (64.3) 1 (50) 4 (100) 0
3.0-4.0 14 (35) 5 (35.7) 1 (50) 0 1 (100)
Type of NSAID
Diclofenac 34 10 1 3 0
Naproxen 13 1 1 2 0
Ibuprofen 9 4 0 1 1
Acenocoumarol dosage (mg)
3.048 2.628 2.071 1.482 1.786
mentioned earlier, phenylbutazone shows a pharmacokinetic interaction with coumarins [3],
resulting in an increase in INR. Other NSAIDs such as diclofenac and naproxen are reported to
increase the risk of bleeding, but no evidence of a pharmacokinetic interaction with coumarins
has been reported [20]. In an open-label study among 8 healthy volunteers no effect of piroxi-
cam on the pharmacokinetics of acenocoumarol enantiomers was found [21]. In a placebo-
controlled study among 56 osteoarthritis patients treatment with nabumetone for up to 4
weeks did not alter INR levels compared with placebo [22]. In an open crossover study among
6 healthy volunteers lornoxicam did not alter the pharmacokinetics of the clinically relevant
(R)-acenocoumarol or the anticoagulant activity of acenocoumarol [23]. Warfarin drug inter-
actions with NSAIDs have been described more frequently, possibly due to the fact that war-
farin is the oral anticoagulant most widely used in the United States. One case-report descri-
bing a patient with a seriously prolonged INR during the combined use of warfarin and indo-
methacin was found [24]. Recently, two case-reports [25,26] have been published showing a
significant increase in INR after adding celecoxib, a cyclooxygenase-2 selective NSAID, to war-
farin therapy. A possible mechanism to explain this interaction could be that both medications
are metabolised through the same cytochrome P450 pathway (namely CYP2C9). Karim and col-
legues showed no significant effect of celecoxib on pro-thrombin times or steady-state phar-
macokinetics of S- and R-warfarin in a small study with 24 healthy volunteers [27]. In an USA
ambulatory care anticoagulation clinic over a period of one year, 28 patients had been prescri-
bed either celecoxib or rofecoxib after being stable on warfarin therapy. Thirteen of these had
increases in INR, within this group 6 patients used only a coumarin (warfarin) with an NSAID
(cyclo-oxygenase-2 inhibitor) [28]. These results are comparable with our results of acenocou-
marol with NSAIDs. For celecoxib that is metabolized by CYP2C9 competitive inhibition of
CYP2C9 metabolism could lead to this potential pharmacokinetic drug interaction. Rofecoxib is
not reported to inhibit CYP2C9. However, rofexocib is approximately 87% bound to plasma pro-
tein, resulting in higher free-warfarin levels and thus possibly accounting for the increasing
INRs [28].
Different mechanisms may account for our findings. As stated earlier due to competitive
inhibition of the NSAIDs of (R)-acenocoumarol 7-hydroxylation, the plasma level of (R)-aceno-
coumarol may be increased. Furthermore, inhibition of NSAIDs of the p-glycoprotein pump
may account for increased acenocoumarol levels. Protein displacement processes may also play
a role. CYP2C9 genotype seems to be not a risk factor for the occurrence of a potential clinical
relevant increase in INR as a result of the interaction between acenocoumarol and diclofenac,
naproxen or ibuprofen. Although polymorphism of CYP-metabolising enzymes may play an
important role in predicting adverse drug effects, including drug-drug interactions [6], we could
143
Discuss ion
In this study we found that nearly half of a cohort of acenocoumarol users on average
showed an increase in INR between 1 and 4 after diclofenac, naproxen or ibuprofen was added
to the acenocoumarol therapy. Considering the estimation that the risk of bleeding increases
with 54% (CI95 44%-65%) for every unit increase in INR [2], these rises in INR should be con-
sidered clinically relevant. Recently it was shown that patients with INRs greater than 6 have
a significant short-term risk of major haemorrhage compared with patients with an in-range
INR [17]. In our study, the INR of 12 patients rose above 6. This means that 11% of the study
population was at risk of major haemorrhage. We did not find an association between CYP2C9
genotype and the risk of an increased INR as a result of the potential pharmacokinetic interac-
tion between acenocoumarol and NSAIDs. However, patients with one of the variant alleles (*2
or *3), had a significantly lower acenocoumarol dosage than patients without one of the variant
alleles. This could mean that acenocoumarol dosage requirements are dependent on CYP2C9
genotype. We are currently investigating this matter in more detail by analysing blood (R)- and
(S)-acenocoumarol levels.
Thijssen and co-workers showed a strong association between acenocoumarol sensitivity
and the presence of the CYP2C9*3 allele possibly due to impaired acenocoumarol clearance, in
particular the (S)-enantiomer [18]. They stated that because of genetic polymorphism, the meta-
bolic clearance of (S)-acenocoumarol is reduced profoundly, and this enantiomer, that is nor-
mally clinically inactive, may now exert main anticoagulant activity. In warfarin users, Aithal
found that patients with CYP2C9 variant alleles were found to require lower warfarin dosages
[19]. Our results seem to be in line with the results reported by Aithal and Thijssen.
In this study data from patients in daily clinical practice were used, reflecting the clinical
relevance of our findings. Furthermore, we restricted our analyses to patients who did not use
any other drugs, thus making a direct cause-and-effect relationship more likely. A limitation of
this study is that we can not rule out confounding by indication. It is known that during episo-
des of fever and inflammation, the breakdown of clotting factors is increased, leading to a
greater response to coumarin therapy and thus an increased INR. The fact that we used a con-
trol group that did not show an increase in INR, reduces the bias as a result of confounding by
indication considerably. Also, we excluded patients who used other drugs simultaneously, such
as antibiotics, further limiting confounding by indication. Finally, by stratifying patients in the-
rapeutic windows, depending on the diagnosis, a further limitation of bias due to confounding
by indication was achieved.
In the literature, several reports on coumarin-NSAID interactions have been published. As
142
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2 Van der Meer FJM, Rosendaal FR, Vandenbroucke JP, Briët E. Bleeding complications in oral anticoagulant
therapy. Arch Intern Med 1993; 153: 1557-62.
3 Harder S, Thürmann P. Clinically important drug interactions with anticoagulants. An update.
Clin Pharmacokinet 1996; 30: 416-44.
4 Brouwers JRBJ, De Smet PAGM. Pharmacokinetic-pharmacodynamic drug interactions with nonsteroidal
anti-inflammatory drugs. Clin Pharmacokinet 1994; 27: 462-85.
5 Breukink-Engbers WG. Monitoring therapy with anticoagulants in the Netherlands. Seminars in Trombosis
& Hemostasis 1999; 25: 37-42.
6 Philips KA, Veenstra DL, Oren E, Lee JK, Sadee W. Potential role of pharmacogenomics in reducing adverse drug
reactions. JAMA 2001; 286: 2270-9.
7 Roland Wolf C, Smith G, Smith RL. Pharmacogenetics. BMJ 2000; 320: 987-90.
8 Meyer UA. Pharmacogenetics and adverse drug reactions. Lancet 2000; 356: 1667-71.
9 Thijssen HH, Flinois JP, Beaune PH. Cytochrome P450 2C9 is the principal catalyst of racemic acenocoumarol
hydroxylation reactions in human liver microsomes. Drug Metab Dispos 2000; 28: 1284-90.
10 Klose TS, Ibaenu GC, Ghanayem BI, Pedersen LG, Li L, Hall SD, et al. Identification of residues 286 and 289 as
critical for conferring substrate specificity of human CYP2C9 for diclofenac and ibuprofen. Arch Biochem
Biophys 1998; 357: 420-8.
11 Bliesath H, Huber R, Steinijans VW, Koch HJ, Wurst W, Mascher H. Lack of pharmacokinetic interaction between
pantoprazole and diclofenac. Int J Clin Pharmacol Ther 1996; 34 (suppl 1): S76-80.
12 Leemann T, Transon C, Dayer P. Cytochrome P450 TB (2C9): a major monooxygenase catalyzing
diclofenac 4’-hydroxylation in human liver. Life Sci 1993; 52: 29-34.
13 Miners JO, Coulter D, Tukey RH, Veronese ME, Birkett DJ. Cytochromes P450, 1A2 and 2C9 are responsible for the
human hepatic O-demethylation of R- and S-naproxen. Biochem Pharmacol 1996; 51: 1003-8.
14 Sotaniemi EA, Arranto AJ, Pelkonen O, Pasanen M. Age and cytochrome P450-linked drug metabolism in
humans: an analysis of 226 subjects with equal histopathological conditions. Clin Pharmacol Ther 1997; 61: 331-9.
15 Wang S-L, Huang J-D, Lai M-D, Tsai J-Jl. Detection of CYP2C9 polymorphism based on polymerase chain
reaction in Chinese. Pharmacogenetics 1995; 5: 37-42.
16 Steward D, Haining RL, Henne KR, Davis G, Rushmore TH, Trager WF, Rettie AE. Genetic association between
sensitivity to warfarin and expression of CYP2C9*3. Pharmacogenetics 1997; 7: 361-7.
17 Hylek EM, Chang YC, Skates SJ, Hughes RA, Singer DE. Prospective study of the outcomes of ambulatory
patients with excessive warfarin anticoagulation. Arch Intern Med 2000; 160: 1612-7.
18 Commissie Interacterende Medicatie Cumarines. Standaard afhandeling cumarine-interacties (in Dutch).
Wetenschappelijk Instituut Nederlandse Apothekers (WINAp). The Hague, The Netherlands, 1999.
19 Thijssen HHW, Verkooyen IWC, Frank HLL. The possession of the CYP2C9*3 allele is associated with low dose
requirement of acenocoumarol. Pharmacogenetics 2000; 10: 1-4.
20 Aithal GP, Day CP, Kesteven PJ, Daly AK. Association of polymorphism in the cytochrome P450 CYP2C9 with
warfarin dose requirement and risk of bleeding complications. Lancet 1999; 353: 717-9.
21 Bonnabry P, Desmeules J, Rudaz S, Leemann T, Veuthey JL, Dayer P. Stereoselective interaction beween
piroxicam and acenocoumarol. Br J Clin Pharmacol 1996; 41: 525-30.
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23 Masche UP, Rentsch KM, von Felten A, Meier PJ, Fattinger KE. No clinically relevant effect of lornoxicam intake
145
not confirm this role in the case of the interaction between acenocoumarol and the NSAIDs
studied. As a result of this finding, at present we are not able to identify patients most likely to
be at risk for a potential clinical relevant interaction between acenocoumarol and diclofenac,
naproxen or ibuprofen.
In conclusion, this study shows that diclofenac, naproxen and ibuprofen can lead to a clini-
cally relevant increase of the INR in patients treated with acencoumarol. In 11% of the study
population, this lead to INRs above 6 with a clinically relevant risk of severe haemorrhage [17].
Genetic polymorphism of CYP2C9 did not contribute to the occurrence of this potential clinical
relevant interaction. In view of the widespread use of NSAIDs and the fact that these drugs can
be purchased without a doctor’s prescription, further prospective studies on this drug-drug
interaction should be initiated. In the meantime, adequate surveillance and close monitoring
of the INR of patients receiving these drugs concomitantly is warranted.
Acknowledgements
We are grateful to C.Th. Smit Sibenga, PhD, and his colleagues from the Bloedbank Noord
Nederland, Groningen, the Netherlands, for their co-operation in transporting patient blood
samples. We thank the laboratory assistants of the Outpatient Thrombosis Service Groningen,
Groningen, the Netherlands, for their co-operation in taking blood samples. Furthermore we
are grateful to the laboratory assistants of the Clinical Chemistry Laboratory at Leeuwarden for
their help in the genotyping part of the study. C.S. de Vries, PhD, RPh, is thanked for her valu-
able comments on the manuscript.
144
147
on acenocoumarol pharmacokinetics and pharmacokinetics. Eur J Clin Pharmacol 1999; 54: 865-8.
24 Chan TY. Prolongation of prothrombin time with the use of indomethacin and warfarin.
Br J Clin Pract 1997; 51: 177-8.
25 Haasse KK, Rojas-Fernandez CH, Lane L, Frank DA. Potential interaction between celecoxib and warfarin
(letter). Ann Pharmacother 2000; 34: 666-7.
26 Mersfelder TL, Stewart LR. Warfarin and celecoxib interaction. Ann Pharmacother 2000; 34: 325-7.
27 Karim A. Tolber D, Piergies A, Hubbard RC, Harper K, Wallemark CB, Slater M, Geis GS. Celecoxib does not
significantly alter the pharmacokinetics or hypoprothrombinemic effect of warfarin in healthy subjects.
J Clin Pharmacol 2000; 40: 655-63.
28 Stading JA, Skrabal MZ, Faulkner MA. Seven cases of interaction between warfarin and cyclooxygenase-2
inhibitors. Am J Health-Syst Pharm 2001; 58: 2076-80.146
Chapter 4
General d iscuss ionand perspect ives
the potential risks associated with drug use in these nursing homes. Pharmacy dispensing data
for 2,355 residents were retrieved. It was found that the hospital pharmacy data were, in gener-
al, accurate, although completeness of discharge and admission date recording could be impro-
ved. With these data, the duration of stay in the nursing home could be calculated, as well as
the duration of drug use. Hospital pharmacy data proved to be an important data source, ena-
bling the performance of drug utilisation and drug safety studies. We demonstrated the impor-
tance of accurate and precise recording in the hospital pharmacy, of prescription data for indi-
vidual nursing home patients in order to reliably determine drug exposure. The main findings
and implications of these studies are described below. First, a drug utilisation study was per-
formed. We found high numbers of drug users and the chronic use of many drugs. This study
revealed several potential problem areas regarding the prescribing of drugs in the nursing
home setting. Prescribing of loop diuretics, laxatives, psychotropic drugs and ulcer-healing
drugs deserved attention, in view of high dosages, long-term use and a high proportion of
users. Prescribing practices in individual nursing homes regarding these drug groups may sub-
sequently be evaluated in pharmacotherapeutic discussion meetings or, on the level of an indi-
vidual patient in a one-to-one discussion with the prescriber. Hospital pharmacists can play a
key role in these evaluations since they have the tools to analyse individual prescription data.
From the drug utilisation studies, several determinants of drug use were found. Sex was found
to be a determinant of drug use in several cases, as was the type of care. It was shown that in
drug utilisation studies it is important to distinguish between nursing home residents residing
in psychogeriatric nursing homes, and those residing in somatic nursing homes as drug use
substantially differs between these populations. As expected, nursing home residents residing
in psychogeriatric nursing homes use more psycholeptic drugs (psychotropics and anxiolytics)
and less antithrombotic drugs, diuretic drugs and antacids than residents residing in somatic
nursing homes. Also, somatic residents showed a higher risk for the occurrence of potential
drug-drug interactions (DDIs). Female residents were more likely than male residents to expe-
rience a potential DDI and were more likely to receive NSAIDs, antirheumatic drugs and psy-
choanaleptic drugs. Male residents were more likely to receive psychotropic drugs. Other deter-
minants of drug use found were the number of medications prescribed. Residents with a hig-
her number of medications were more at risk for the occurrence of potential DDIs than resi-
dents with fewer drugs. Patients with Parkinson’s disease were less likely to be exposed to
potential DDIs.
In view of the high frequency of drug use, we studied the potential risks of polypharmacy
by carrying out a descriptive study on the extent and occurrence of potential DDIs. Several pre-
scribing indicators were used to assess the occurrence and nature of DDIs, such as the number
149148
Pharmacotherapy in f ra i l e lder ly
People residing in Dutch nursing homes are mostly frail, elderly people who are dependent
on continuous nursing and medical attention. Pharmacotherapy in this elderly group is an
important aspect of medical care. Drugs obviously have beneficial effects in the very old. For
example, the use of statins should not be limited to people younger than 70 years, as statins
can reduce the risk of myocardial infarction even in the very old [1,2]. In addition, the benefits
of oral anticoagulant therapy in patients suffering from atrial fibrillation, have been well ack-
nowledged. For some therapies, such as hormone-replacement therapy in elderly women for
reducing the risk of osteoporosis [3], it is not yet clear who will receive the most benefit.
However, the impact of adverse effects of pharmacotherapy in the frail elderly is generally higher
than in other populations. Non-steroidal anti-inflammatory drugs (NSAIDs) may lead to renal
dysfunction [4], anticholinergic drugs to confusion and delirium [5], and psychotropic drugs to
falls and fractures [6], often with significant impact on the quality of life. Therefore, the risks of
drug therapy should be carefully weighed against the potential beneficial effects, especially in
this vulnerable group of elderly persons. Pharmacoepidemiologic studies can provide insight
into this delicate balance. The benefits of drug use are mainly studied in randomised control-
led clinical trials (RCTs). Observational studies are less suitable for evaluating the benefits of
drug use in view of biases and confounding issues such as selection by disease severity.
However, observational studies are often used to study adverse drug effects. This is due to
various considerations. These include differences between the experimental RCT setting and
prescribing in daily clinical practice such as co-morbidity and comedication profiles and diffe-
rences in patient age. This thesis is largely based on observational studies.
Pharmacoepidemiology in f ra i l e lder ly
Organisational differences between nursing homes in the Netherlands and in other coun-
tries, in particular the United States, will have an impact on drug utilisation. Consequently, fin-
dings from one health care setting cannot automatically be considered true for another setting.
Therefore, to gain insight into local drug use, it is important that drug utilisation studies are
performed within the applicable health care system. This thesis reports on an investigation of
drug use and drug-related problems in the elderly in the Netherlands, in particular those resi-
ding in nursing homes. To draw conclusions regarding drug use and safety, a sufficiently large
number of subjects is needed. We collected pharmacy dispensing data from 6 nursing homes.
In this way, we were able to study the extent of drug use, problem areas in drug prescribing and
feasible to perform a prescription-sequence analysis using pharmacy data in the nursing home
setting. Although the constipating effects of certain drugs should be taken into account, this
may not necessarily explain the high rate of laxative use found in the nursing homes we stu-
died. Effects of dietary factors and physical activity on laxative use needs further investigation.
The studies discussed above were carried out to identify problem areas in drug prescribing in
the elderly, especially those residing in nursing homes. Results of these studies may help to
identify patients at increased risk of drug-related problems.
How to ident i fy pat ients at r i sk?
To enable the identification of patients at risk of drug-related problems, more information
than pharmacy data alone is needed. Clinical data and information from prescribers can provi-
de insight into clinical outcomes, as well as into any preventive measures that have already
been undertaken and how these patients are currently being monitored. One way to identify
patients at risk of unwanted pharmacotherapy effects, is to use prescribing indicators to signal
potentially suboptimal prescribing. Although many prescribing indicators have been developed
and used internationally [9], few of these can be used with pharmacy prescription data and few
are specific for drug-related problems common in nursing homes. In a pilot study [10], we found
that a set of prescribing indicators developed on the basis of current pharmacotherapy guide-
lines, could be used to evaluate prescribing practices in nursing homes and to identify patients
at risk of potential suboptimal prescribing. However, information from the prescriber on clini-
cal data was needed to identify patients who were actually at risk for drug-related problems. In
some cases, deviation from national guidelines and drug formularies may not mean that the
patient is treated suboptimally. This discrepancy between potential and actual inappropriate
prescribing has been reported earlier [11] and stresses the importance of taking the patient’s
clinical response and the prescribers’ rationale into consideration. Furthermore, it is often dif-
ficult to define prescribing indicators that actually reflect potential suboptimal prescribing.
Although evidence-based practice guidelines may serve as a basis, these sometimes differ from
expert opinions [12]. In nursing home practice, evidence-based guidelines are just beginning to
emerge, and the lack of specific nursing home guidelines hampers the development of good
prescribing indicators in this setting. In many cases, however, prescribing indicators can be
used to signal potentially suboptimal prescribing, as was also found in recent studies [12,13].
Together with clinical data, such as laboratory values or clinical outcomes, the risks for the
patient can be adequately perceived and preventive measures taken on both an individual
patient and population-based level. In our view, the pharmacological knowledge of the hospi-
151
of residents exposed to a DDI, the prevalence of a DDI, the percentage of days of concomitant
drug use, and the drugs most frequently involved in a DDI. Approximately one-third of the nur-
sing home residents was exposed to at least one clinically relevant DDI. However, for each
clinically relevant DDI registered, no more than 10% of the residents were affected. This indi-
cates that not one DDI was identified that could be considered potentially harmful to many
residents. The interaction between NSAIDs and loop diuretics, and between NSAIDs and oral
anticoagulants were the potential DDIs most frequently encountered (9.7% and 9.6% of the
study population respectively). It is important to know whether these potential DDIs actually
lead to clinically adverse outcomes. At present, the perceived clinical relevance of DDIs is often
based on theoretical considerations and case-reports [7]. However, the actual clinical relevan-
ce is unknown. Studies should be undertaken investigating the clinical effects of DDIs in order
to identify both DDIs that are likely to cause problems and people at a higher risk of developing
these problems [8]. In particular the frail elderly have a high risk of potential DDIs and drug-
disease interactions as a result of polypharmacy and co-morbidity, and knowledge on the
clinical relevance of DDIs could contribute to a safer use of drugs. At present, it is often difficult
to collect data on individual clinical outcomes, such as laboratory test results, on a large scale
because these data are often not stored in easily accessible files. Subsequently, we investiga-
ted in more detail the co-prescribing of several drug groups frequently used in the nursing
home population. We evaluated co-prescribing of benzodiazepines and antidepressant thera-
py to investigate previously reported differences in co-prescribing between tricyclic antide-
pressants (TCAs) and selective serotonin reuptake inhibitors (SSRIs). We found that in elderly
outpatients the risk for initiating benzodiazepines during antidepressant therapy was higher
for SSRI users than for TCA users. However, in the nursing homes patients no such difference
was found. This study also illustrated the need to perform drug utilisation studies in different
elderly populations as the results may differ depending on which population is studied. In
another study we investigated co-prescribing of NSAIDs and gastroprotective drugs in elderly
outpatients. It was found that 23% of NSAID users aged 65 and over were co-prescribed gastro-
protective drugs. Given this finding, initiatives should be taken to increase gastroprotective
drug use in elderly NSAID users. Also, more insight is needed into the reasons concerning why
prescribing gastroprotective drugs were not more frequently prescribed to this high-risk group.
Finally, to investigate the determinants of the high frequency of laxative use in the nursing
homes in more detail, a study was carried out to see whether laxative use was the consequen-
ce of prescribing constipating drugs, such as anticholinergics. It was shown that drugs classi-
fied as moderately to severely constipating were associated with laxative use. However, the
association was not as strong as previous studies have suggested. This study showed that it is
150
pital pharmacy computer systems.
• Clinical status of the patient: this includes diagnoses, data from the nursing home physi-
cians and medical specialists visits, and clinical outcomes. In Dutch nursing homes, these
data are manually recorded in the patients’ medical chart. Sometimes electronic patient
records are available.
Qual i ty of l i fe
It could be argued, that together with clinical outcomes such as the frequency of adverse events
another issue that needs to be taken into account is the quality of life (QoL) in the frail elderly
population. For instance, in clinical practice the impact of side effects from ACE-inhibitors in
this population, is higher than in the general population. This may lead to a more adverse risk
- benefit balance. The impact of various drug-related problems on this populations’ quality of
life, will therefore be different to that in the general population. Measuring QoL, however, is not
something that is routinely done in nursing home patients and such data are not readily avai-
lable. In the elderly, the ‘Short Form-36’ (SF36) is regarded as a suitable, reliable and valid
questionnaire and outcome tool [16,17]. However to our knowledge, this questionnaire has
been used only in community-dwelling elderly and not in Dutch nursing home residents. A high
incidence of non-completion on SF36 questions relating to physical and mental function has
been reported [16]. In nursing homes the prevalence of physical and mental disorders among
residents is much higher and therefore the SF-36 is possibly not a suitable tool for measuring
QoL. It is with these considerations in mind, that we believe that routinely measuring QoL in
nursing home patients is probably not one of the first issues to focus on. Instead, QoL should
be taken into account when the effects of drug use are evaluated on an individual basis where
relevant or when possible.
Future perspect ives
Opt imal use of c l in ica l and pharmacy data to monitor drug effects
Efforts should be directed towards optimal use of existing clinical, pharmacy and laborato-
ry records. This may lead to several advances in the pharmacotherapeutical care of the frail
elderly. First, on the basis of clinical characteristics individual patients could be monitored pro-
spectively regarding potential adverse drug effects and adverse outcomes. Record-linkage of
pharmacy and clinical data provides an excellent opportunity to optimally use both sources of
information. In the nursing home setting, most relevant information is available. Since not all
153
tal pharmacist together with the clinical knowledge of the nursing home physician may act
synergistically in detecting drug-related problems in the elderly. Also other caregivers, such as
nurses, may play an important role in signalling adverse drug effects in clinical nursing home
practice.
The ultimate question, however, is whether potentially suboptimal prescribing leads to
actual clinical problems [14]. We investigated this with respect to a frequently occurring, poten-
tial DDI. The potential DDI between acenocoumarol and NSAIDs constitutes one of the most
frequently encountered DDIs in the nursing home study population and to date its clinical rele-
vance remains unknown. The study presented in this thesis found that this interaction led to a
clinically relevant outcome in about half of all elderly outpatients exposed, namely the increa-
se of the prothrombin ratio expressed as the international normalised ratio (INR) above the
therapeutic window. To be able to identify patients at risk for this drug-drug interaction, sever-
al patient characteristics were investigated as potential determinants. One of the determinants
we included was genetic polymorphism of cytochrome P450 2C9 (CYP2C9). This enzyme is
involved in the metabolism of numerous drugs including acenocoumarol and NSAIDs, such as
diclofenac, naproxen and ibuprofen. We hypothesized that patients with a variant allele on
CYP2C9 (CYP2C9*2 and CYP2C9*3) would be more likely to experience the increase in pro-
thrombin time due to the DDI. We found that none of the determinants investigated was asso-
ciated with the occurrence of increase in prothrombin time. However, patients with one of the
variant alleles mentioned above, required a lower dose of acenocoumarol than patients who
had the wild-type genotype and we are currently investigating this matter in more detail. In the
meantime, all patients who are prescribed acenocoumarol and an NSAID simultaneously
should be monitored, in view of the increased risk for elevated INR levels.
The studies described above, show the importance of capturing data on drug use as well as
data on clinical variables to be able to identify patients at risk for drug-related problems. The
following clinical variables should be included:
• Laboratory test values: insight into potassium levels, creatinine levels, INR, glucose
levels, cholesterol levels and other parameters is often necessary to identify adverse
effects of drug therapy such as decreased renal function as a result of NSAID therapy.
These data are often available in clinical chemistry laboratory computer systems, and
sometimes in the nursing home computer systems. Hard copies of the laboratory test
results are available in the medical chart of the patient. In the near future, results of
genotyping may also be available [15].
• Serum drug levels: information on serum drug levels may be needed to determine the cli-
nical relevance of certain drug-drug interactions. These data are routinely stored in hos-
152
have all been associated with an increased risk of falls in nursing home residents. However,
other clinical outcomes have been studied less frequently. An example is the association
between bowel function and anticholinergic drug use [26]. Quality of life could also be an
important outcome when the risks and benefits of drug use in the frail elderly are balanced.
While the number of life years gained often plays a decisive role in determining the value of a
pharmacotherapeutic intervention, this may not be the case in the frail elderly. The impact of
adverse drug effects is much higher in this group, and quality of life could in some cases deter-
mine whether or not drug therapy should be started. For example, in the treatment of heart fai-
lure, the adverse effects of drug therapies may be more important than the number of life years
gained.
Role of the hospi ta l pharmacist
There are various stages at which hospital pharmacists can play a key role in monitoring
drug-related problems in frail, elderly people. Firstly, hospital pharmacists have the opportuni-
ty to use pharmacy data for drug utilisation research. The studies in this thesis have demon-
strated that hospital pharmacy data are a useful tool for studying the use and effects of drugs
in the elderly, especially in the nursing home setting. Further use of these data for research
should be encouraged, which implies the development of initiatives for anonymous and conti-
nual data collection. Special export files [27] would facilitate the building of a database that can
be continuously updated with current data from pharmacies. The fact that the number of hos-
pital pharmacies using the same computer system is still increasing in the Netherlands contri-
butes to the building of such a database and further collaboration is needed between hospital
pharmacies on this point. In particular, collaboration in the field of nursing home medicine
could contribute to the creation of large databases for adequate performance of drug utilisa-
tion and safety studies. An important prerequisite is that the prescription data cover a suffi-
ciently long period, preferably several years. Initiatives concerning hospital prescription data
arising from the Stichting Farmaceutische Kengetallen (SFK), an organisation that forms part of
the Royal Dutch Association for the Advancement of Pharmacy (KNMP), are currently being
explored. Secondly, hospital pharmacists can play an important role in initiating and conduc-
ting pharmacoepidemiology research in nursing homes. Special interest groups could be for-
med, to facilitate and carry out research projects related both to drug-related problems and
other relevant clinical outcomes in the nursing home. Monitoring of ADR occurrence is also an
issue in which hospital pharmacists could play an essential role as they have the tool to gather
relevant information from prescribers and patients.
Finally, the studies in this thesis show that use of existing clinical and laboratory data is
155
clinical information is stored in automated databases, initiatives should be taken to stimulate
computerised recording of such information. In our study, we used data from a national nursing
home database (SIVIS) to obtain information on clinical diagnoses. Attention should be given
to accurate recording of SIVIS diagnoses, as we found several discrepancies between SIVIS
data and pharmacy data. For example, it is highly unlikely that an antidiabetic drug is prescri-
bed without a registered indication of diabetes mellitus. Data on INR were collected for inves-
tigating the clinical relevance of the potential pharmacokinetic interaction between acenocou-
marol and NSAIDs. Such data were not readily available in computerised databases. It has been
shown that computerised surveillance of adverse drug reactions (ARDs) is feasible in hospitals
[8,18]. Others have suggested that detecting adverse drug reactions would be more effective
and less time-consuming if electronic patient records were available [18]. Ideally, all medical
information of individual patients should be available on an electronic patient record.
Electronic patient records are not usually available in Dutch nursing homes but initiatives are
currently being developed to change this. In primary care, an electronic ‘care card’ is being
developed, on which all patient related medical information is stored. In this way, health care
professionals can gain insight into the patient information needed in order to carry out their
professional duties. This is particularly advantageous when patients are admitted from home
to hospital and or from nursing home to hospital and vice versa. In this way, optimal seamless
care can be provided. An issue that should be considered here, is patient privacy. Information
that is not needed for carrying out professional activities should be seperated from information
that is necessary. This can easily be done once the data is in electronic format. Another advan-
tage of the use of electronically available patient information, is the possibility for monitoring
patient outcomes on real-time basis as information on drug use and clinical outcome can sto-
red in the same database. In the meantime, different databases can be record-linked to provi-
de the opportunity of real-time monitoring for drug-related problems. Internationally, it has
already been shown that it is feasible to record-link patient-specific information that is stored
separately into large databases to monitor and evaluate effects of drug use in large populations
(MEMO [19], SAGE [20]).
A second advantage of combining existing clinical, pharmacy and laboratory records, is the
possibility to study outcomes of drug use in more detail and to establish reliable risk-benefit
analyses of drug use. Outcomes that could be studied are hospital admissions, adverse drug
effects and quality of life, although as discussed earlier, measuring the latter is difficult.
Relatively little is known about the relationship between drug use and clinically adverse out-
comes in nursing homes in the Netherlands. Internationally, many studies have focussed on
falls and fractures [6,21-25]. Psychotropic drugs, such as antidepressants and benzodiazepines,
154
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Ann Intern Med 1999; 130: 897-904.
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the elderly. J Am Geriatr Soc 1999; 47: 507-11.
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management. Drug Saf 1999, 21: 101-22.
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home residents. Am J Epidemiol 1995; 142: 202-11.
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measures. Drugs & Aging 2000; 16: 437-50.
10 Van Dijk KN, Pont LG, Franken M, De Vries CS, Brouwers JRBJ, De Jong-Van den Berg LTW. Prescribing indicators
as a tool to evaluate drug use in nursing homes: a pilot study. Submitted
11 Oborne CA, Batty GM, Maskrey V, Swift CG, Jackson SHD. Development of prescribing indicators for elderly
medical inpatients. Br J Clin Pharmacol 1997; 43: 91-7.
12 Zahn C, Sangl J, Bierman AS, Miller MR, Friedman B, Wickizer SW, Meyer GS. Potentially inappropriate
medication use in the community-dwelling elderly. JAMA 2001; 286: 2823-9.
13 Knight EL, Avorn J. Quality indicators for appropriate medication use in vulnerable elders. Ann Intern Med 2001;
135: 703-10.
14 Avorn J. Improving drug use in elderly patients: getting to the next level. JAMA 2001; 286: 2866-8.
15 Ensom MH, Chang TK, Patel P. Pharmacogenetics: the therapeutic drug monitoring of the future?
Clin Pharmacokinet 2001; 40: 783-802.
16 Fowler RW, Congdon P, Hamilton S. Assessing health status and outcomes in a geriatric day hospital.
Public Health 2000; 114: 440-5.
17 Ray WA, Stein CM, Byrd V, Shorr R, Pichert JW, Gideon P, Arnold K, Brandt KD, Pincus T, Griffin MR. Educational
program for physicians to reduce use of non-steroidal anti-inflammatory drugs among community-dwelling
elderly persons: a randomized controlled trial. Med Care 2001; 39: 425-35.
18 Emerson A, Martin RM, Tomlin M, Mann RD. Prospective cohort study of adverse events monitored by hospital
pharmacists. Pharmacoepidemioly and Drug Safety 2001; 10: 95-103.
19 Evans JJ, MacDonald TM. Record-linkage for pharmacovigilance in Scotland. Br J Clin Pharmacol 1999; 47: 105-10.
20 Gambassi G, Landi F, Peng L, Bostrup-Jensen C, Calore K, Hiris J, Lipsitz L, Mor V, Bernabei R. Validity of diag-
nostic and drug data in standardised nursing home resident assessments: potential for geriatric pharmaco-
epidemiology. SAGE Study Group. Med Care 1998; 36: 167-79.
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hospitalization among nursing home residents. Am J Epidemiol 1997; 145: 738-45.
157
important to estimate the clinical relevance of drug-related problems, such as drug-drug inter-
actions. These can be monitored and studied both on an individual and on a population based
level. Co-operation with other health care organisations, such as Outpatient Thrombosis
Services and Clinical Chemistry Laboratories, can contribute to the optimal use of existing
experience and data.
156
Summary
This thesis focuses on drug use in the elderly, in particular those residing in Dutch nursing
homes: the frail elderly. These elderly are especially prone to drug-related problems because of
their age, frequently occurring co-morbidity and polypharmacy. Relatively little is known of
drug use and drug-related problems in these frail elderly. The studies described in this thesis
aim to increase the knowledge on drug use and drug-related problems in this population. The
thesis comprises four main parts.
In chapter 1, the introductory chapter, the scope and the objective of this thesis are descri-
bed and the problems of drug use in elderly people are outlined. Because of co-morbidity, redu-
ced homeostatic mechanisms and the prescription of several drugs simultaneously, elderly
people are at an increased risk of drug-related problems such as drug-drug interactions, drug-
disease interactions and adverse drug effects. Drugs may also inadvertently be withheld from
the elderly, sometimes as a result of underdiagnosing. In view of these considerations, prescri-
bers need a thorough understanding of the risks and benefits of drug therapy in the elderly,
especially in the frail elderly, most of whom will be residing in nursing homes. In the
Netherlands, relatively few epidemiological studies on drug use in nursing homes have been
carried out. The objective of this thesis is to provide insight in the extent, determinants and
characteristics of drug use, as well as the outcomes of drug use in frail elderly. Section 1.2 gives
an overview of the Dutch health care system for ambulatory and institutionalised elderly. The
Dutch nursing home is a healthcare institution for chronically ill persons in need of permanent
medical and paramedical attention and complex nursing care. The type of care can be charac-
terised as continuous, long-term, systematic and multidisciplinary. Recently it was concluded
that quality aspects should be more incorporated in medication distribution processes and in
pharmaceutical care activities in Dutch nursing homes. Hospital pharmacists play a role in drug
and therapeutics committees, the evaluation of prescribing practices on patient level, and deve-
lopment and implementation of drug formularies.
Chapter 2 describes several studies that investigated drug use in nursing home residents
and elderly outpatients. The first part describes the studies performed in nursing home resi-
dents. In section 2.1 an introduction is given to the field of drug utilisation studies and studies
that describe the quality of drug use in nursing homes. Many studies have investigated the
extent of drug use, whereas only few studies used longitudinal prescription data to evaluate
drug effects over time. The studies showed an average number of drugs prescribed per resident
159
22 Ray WA, Thapa PB, Gideon P. Benzodiazepines and the risk of falling in nursing home residents.
J Am Geriatr Soc 2000; 48: 682-5.
23 Thapa PB, Gideon P, Cost TW, Milam AB, Ray WA. Antidepressants and risk of falls among nursing home
residents. N Engl J Med 1998; 339: 875-82.
24 Yip YB, Cumming RG. The association between medications and falls in Australian nursing home residents.
Med J Aust 1994; 160: 14-8.
25 Wang PS, Bohn RL, Glynn RJ, Mogun H, Avorn J. Zolpidem use and hip fractures in older people.
J Am Geriatr Soc 2001; 49: 1685-90.
26 Monane M, Avorn J, Beers MH, Everitt DE. Anticholinergic drug use and bowel function in nursing home
patients. Arch Intern Med 1993; 153: 633-8.
27 De Vries CS, Van den Berg PB, Timmer JW, Reicher A, Blijleven W, Tromp ThFJ, De Jong-van den Berg LTW.
Prescription data as a tool in pharmacotherapy audit: (II) the development of an instrument.
Pharm World Sci 1999; 21: 85-90.
158
ber of different drugs (based on 5th level of Anatomical Therapeutic Chemical (ATC) code) per
resident was 8.9 (SD 4.9). Duration of drug use was relatively long: eight of the ten therapeutic
drug groups prescribed most frequently were used for more than 50% of the time spent in the
nursing home. In particular psycholeptic drugs, diuretics, and laxatives were used chronically
(83%, 81% and 80% of the nursing home stay, respectively). Except for laxatives and diuretics,
the prescribed daily dosages were relatively low. We concluded that drug use in the nursing
homes was high and many drugs were used chronically. In view of possible adverse effects and
the risks of parallel prescribing and drug-drug interactions, the prescribing of psycholeptic
drugs, laxatives, loop diuretics, and ulcer-healing drugs should be re-evaluated. In section 2.4
a study is presented on drug-drug interactions (DDIs) in the nursing home. We developed pre-
scribing indicators based on the frequency, nature and duration of DDIs to systematically
assess potential DDIs in the cohort of nursing home patients. We found 32% of all residents
were exposed to at least one clinically relevant DDI. The number of medications prescribed was
a strong predictor of the occurrence of a potential DDI. Drug groups most frequently involved in
DDIs were oral anticoagulants, antibiotics and theophylline. The interaction between non-ste-
roidal anti-inflammatory drugs (NSAIDs) and loop diuretics, and between NSAIDs and oral
anticoagulants were the potential DDIs most frequently encountered. The number of days on
which drugs were prescribed concomitantly was relatively high. Nineteen out of 32 DDIs were
prescribed for an average of 50 days or more per 100 days of index drug use. The prescribing
indicators developed in this study provide the tools to audit DDI occurrence in nursing homes
systematically. Finally, in section 2.5 a pilot study is presented that used several prescribing
indicators, based on the studies in sections 2.3 and 2.4, to evaluate drug prescribing in Dutch
nursing homes. We evaluated prescribing of benzodiazepines, NSAIDs, ulcer-healing drugs and
diuretics. Prescribing indicators were used to identify prescribing that was potentially not in
line with recommendations in national and regional prescribing guidelines. Both descriptive
indicators, such as percentage of users, and indicators reflecting potentially suboptimal pre-
scribing, such as use of drugs outside the drug formulary and prescription of drug dosages
above recommended values were used. We found the majority of prescribing to be in line with
recommendations upon which we based our prescribing indicators. Clinical information from
the prescriber was necessary to get insight into actual prescribing appropriateness. The second
part of chapter 2 describes two drug utilisation studies that were performed in ambulatory
elderly, and partly in nursing home patients. Section 2.6 presents a study on the concomitant
use of benzodiazepines and antidepressants in two cohorts of elderly outpatients and the nur-
sing home cohort. We assessed whether differences in co-prescribing between tricyclic antide-
pressants (TCAs) and selective serotonine reuptake inhibitors (SSRIs) existed. Pharmacy dis-
161
ranging from 2.5 to 8.8. The studies that were carried out in the Netherlands involved relative-
ly small numbers of residents, and did not study overall drug use on individual patient level. In
particular in the United States, much attention has been given to rationality and appropriate-
ness of prescribing in nursing homes. Several studies have focused on applying tools, also
referred to as quality or prescribing indicators, to measure medication appropriateness. These
studies have shown that a considerable proportion of the nursing home residents received
inappropriate prescribing. However, prescribing indicators used in one health care system are
not automatically applicable to other health care systems due to differences in pharmacothe-
rapy guidelines and drug formularies. Section 2.2 describes how computerised medication
order data were used to build a nursing home database with the aim to perform drug utilisa-
tion studies. We collected medication order data of all nursing home residents from 6 nursing
homes in Friesland, the Netherlands, for a 2-year study period between 01-10-1993 and 01-10-
1995. These records were subsequently record-linked with a national information system on
nursing home residents (SIVIS). The SIVIS database contains information on medical (such as
diagnoses), nursing (such as activities of daily living and mobility) and administrative data col-
lected on individual nursing home residents. The source population consisted of 2,966 patients.
As a result of the record-linkage with SIVIS, missing data and age-limits (residents aged < 65
years were excluded), the final study population consisted of 2,355 residents. We have made
several recommendations for those who want to collect medication order data from nursing
home residents to perform pharmacoepidemiological studies. For example, an adequate samp-
le size is necessary, and data confidentiality must be guaranteed. Data should be as accurate
and complete as possible, which can be ensured by adequate data entry in hospital pharmacy
computer systems and checks against other sources, e.g. SIVIS data. Furthermore, it is impor-
tant that the data can be collected on a continuous basis, as longitudinal data are required to
study drug use over time. Keeping individual medication histories for several years is therefo-
re a prerequisite. Another important aspect is the registration of admission and discharge dates
in (pharmacy) computer systems so actual duration of stay in the nursing home can be calcu-
lated and provide person time as the denominator when studying person time exposed to
drugs. We found little agreement between SIVIS diagnoses data and pharmacy prescription
data for both diabetes mellitus and Parkinson’s disease, indicating that both pharmacy data and
SIVIS should be verified against each other in order to get the right estimation of disease pre-
valence in the nursing home population. In section 2.3 we performed a drug utilisation study
among 2,355 nursing home residents. During the two-year study period, 89%, 77% and 56%
of the study population used a drug from ATC main group N (central nervous system), A (ali-
mentary tract and metabolism), and C (cardiovascular system), respectively. The average num-
160
drugs that exhibit moderately to strongly constipating effects and occurrence of constipation
was found, the risk was not as high as seen in previous studies. In section 3.2 the clinical effect
of a DDI was investigated. In a cohort of elderly outpatients attending the Groningen
Outpatient Thrombosis Service, we studied the effects of the interaction between three NSAIDs
(diclofenac, naproxen and ibuprofen) and the oral anticoagulant acenocoumarol on prothrom-
bin time, expressed as the International Normalised Ratio (INR). Genotyping of cytochrome
P450 2C9 was performed to determine whether genotype was a predictive variable for the
occurrence of an increased INR as a result of this DDI. The study population consisted of 112
patients stable on acenocoumarol therapy, of whom 52 (46%) showed an elevation of the INR
above the desired therapeutic level (average INR increase between 1 and 4 units). In 12
patients, the INR increased above 6, indicating a clinically relevant risk of severe haemorrha-
ge. No association between CYP2C9 genotype and an increased INR as a result of the DDI was
found, and no other predictive variables were identified. We recommend close monitoring of
the INR of all patients receiving NSAIDs and acenocoumarol as at present we cannot predict
who will and who will not be affected by this DDI.
In chapter 4 the results of the studies described in the thesis are placed in a broader per-
spective and suggestions for clinical practice and further study are given. For example, hospi-
tal pharmacists can play a leading role in the monitoring of drug-related problems in the frail
elderly both on an individual and a population based level.
163
pensing data from the InterAction database were used for the study among ambulatory elder-
ly. We found that in two cohorts of elderly (one during 1994-1995 and one during 1998-1999) the
risk of initiating benzodiazepine drug therapy during antidepressant therapy was higher for
SSRI users than for TCA users (overall incidence RR 1.6; CI95 1.3-2.0). This could be due to the
fact that the less sedative effects of SSRIs may contribute to the increased frequency of benzo-
diazepine prescribing. In the nursing home cohort, no difference in frequency of benzodiazepi-
ne co-prescribing was found between SSRI users compared with TCA users. Partly this may be
due to the fact that hypnotic drug use in this population was already high, as was shown in sec-
tion 2.3. The prevalence of concomitant prescribing was considerable: in both ambulatory and
institutionalised elderly more than 50% of TCA and SSRI users were prescribed a benzodiaze-
pine drug concomitantly. On average, concomitant drug use lasted for greater than 67 days per
100 days of antidepressant drug use. The combined use of TCAs and benzodiazepines seems of
concern in view of the cumulation of adverse effects such as excess sedation and an increased
risk of falls. In section 2.7, we studied a potential beneficially combination of drugs: the conco-
mitant use of NSAIDs and gastroprotective drugs in a cohort of elderly outpatients. Use of
NSAIDs is associated with an increased risk of gastrointestinal toxicity, in particular when risk
factors such as advanced age are present. We studied the prevalence of concomitant prescri-
bing, as well as the prophylactic prescribing of gastroprotective drugs among ambulatory
NSAID users aged 65 years and over. Co-prescribing of gastroprotective drugs occurred in 23%
of the NSAID users (n=6,557), with an average duration of 67 days per 100 days of NSAID use.
Concomitant use of oral corticosteroids, coumarins and low dose aspirin were significantly
associated with both prophylactic and concomitant prescribing of gastroprotective agents
during NSAID therapy. We recommend giving feedback to prescribers to improve prescribing
practices in this high risk group.
In chapter 3, outcomes of drug use are studied in nursing home patients and elderly outpa-
tients. Section 3.1 presents a study among nursing home residents, in which the association
between drug use and constipation was investigated. We performed a prospective cohort study
of 2,355 nursing home patients to estimate the incidence relative risk of constipation associa-
ted with drug use using prescription sequence analysis of each resident’s detailed pharmacy
records and data on morbidity and mobility. Use of drugs that according to the summaries of
product characteristics and the literature on adverse effects have moderately to strongly con-
stipating properties was associated with a relative risk of 1.6 (CI95 1.2-2.0) for the occurrence of
constipation during exposure. Use of drugs with mildly to moderately constipating effects was
not associated with an increased frequency of laxative use. Although an association between
162
aandoeningen. De medische zorg wordt geleverd door speciaal hiervoor opgeleide verpleeg-
huisartsen. De geneesmiddelendistributie en farmaceutische zorg worden geleverd door open-
bare apothekers of ziekenhuisapothekers.
Het gebruik van geneesmiddelen bi j ouderen
Hoofdstuk 2 gaat nader in op het geneesmiddelgebruik bij ouderen. Het eerste deel
beschrijft het geneesmiddelgebruik bij verpleeghuisbewoners. In paragraaf 2.1 wordt een lite-
ratuuroverzicht gegeven van de studies die het geneesmiddelgebruik bij verpleeghuisbewo-
ners beschrijven. Deze studies zijn voornamelijk in de Verenigde Staten uitgevoerd. Aangezien
Amerikaanse ‘nursing homes’ nogal verschillen van de Nederlandse verpleeghuizen, zijn de
resultaten van deze studies niet zonder meer van toepassing op de Nederlandse situatie. In
Nederland is slechts weinig onderzoek uitgevoerd waarin het geneesmiddelgebruik op indivi-
dueel patiëntniveau wordt beschreven. Dit komt mede doordat het geneesmiddelgebruik pas
sinds een aantal jaren wordt vastgelegd in geautomatiseerde gegevensbestanden. Vóór de
jaren negentig werden de geneesmiddelgegevens vaak alleen in de medische status opge-
schreven, hetgeen het uitvoeren van farmacoepidemiologisch onderzoek, dat gegevens van
vele patiënten nodig heeft, bemoeilijkt. Paragraaf 2.2 beschrijft hoe de geneesmiddelgegevens
van alle verpleeghuisbewoners van zes verpleeghuizen zijn verzameld in één databestand. De
gegevens betroffen de periode 1-10-1993 tot 1-10-1995. Dit databestand is gekoppeld aan een
landelijk verpleeghuis-databestand (SIVIS), waarmee gegevens over de diagnosen, de aard
van de verpleging (somatisch of psychogeriatrisch) en de mobiliteit van de verpleeghuispa-
tiënten werden verkregen. De uiteindelijke studiepopulatie omvatte 2355 verpleeghuisbewo-
ners. In deze paragraaf worden aanbevelingen gedaan voor het uitvoeren van farmacoepide-
miologisch onderzoek met behulp van apotheekgegevens. Van belang is dat alleen geanonimi-
seerde gegevens verzameld worden, dat de gegevens een redelijke tijdsperiode (minimaal 2
jaar) beslaan, en dat de gegevens zo volledig mogelijk zijn (bijvoorbeeld een juiste registratie
van opname- en ontslagdata van verpleeghuisbewoners). Met het databestand is een genees-
middelgebruiksstudie uitgevoerd (paragraaf 2.3). Het geneesmiddelgebruik bij de verpleeg-
huispatiënten bleek hoog te zijn. Gemiddeld gebruikte iedere bewoner 4,9 verschillende
geneesmiddelen per dag. Geneesmiddelen die werken op het centrale zenuwstelsel (zoals mid-
delen tegen psychosen, slaap- en kalmeringsmiddelen), laxeermiddelen, pijnstillende midde-
len en middelen ter voorkoming van bloedstolling werden het meest frequent gebruikt. Veel
geneesmiddelen werden langdurig gebruikt, met name psychofarmaca, diuretica en laxantia:
deze werden gedurende meer dan driekwart van de verblijfsduur in het verpleeghuis gebruikt.
165
Samenvatt ing
Dit proefschrift beschrijft het gebruik van geneesmiddelen bij ouderen, in het bijzonder
verpleeghuisbewoners. Vanwege hun leeftijd zijn ouderen gevoeliger voor de effecten en bij-
werkingen van geneesmiddelen. Verpleeghuisbewoners lijden vaak gelijktijdig aan verschil-
lende aandoeningen met als gevolg het gelijktijdig gebruik van verschillende geneesmiddelen
(polyfarmacie). Er is weinig bekend over het gebruik van geneesmiddelen en het optreden van
problemen als gevolg van geneesmiddelgebruik bij deze kwetsbare groep ouderen. De onder-
zoeken die in dit proefschrift zijn beschreven beogen de kennis over het geneesmiddelgebruik
en geneesmiddelgerelateerde problemen bij ouderen te vergroten.
In le id ing
In het eerste hoofdstuk worden de achtergrond en de doelstelling van het proefschrift
beschreven. Oudere patiënten lopen meer risico op het krijgen van geneesmiddelgerelateerde
problemen. Voorbeelden zijn interacties tussen geneesmiddelen, waardoor de werking van een
geneesmiddel verzwakt of juist versterkt wordt. Ook treden bijwerkingen van geneesmiddelen
vaker op bij ouderen, bijvoorbeeld maagklachten als gevolg van bepaalde pijnstillers
(NSAID’s). Ook het ten onrechte niet aan oudere patiënten voorschrijven van geneesmiddelen,
bijvoorbeeld wanneer een bepaalde aandoening (zoals een depressie) niet wordt herkend, kan
voorkomen. Men spreekt dan van onderbehandeling. Om deze redenen is het van belang dat de
voorschrijvend arts een goed beeld heeft van de effecten en bijwerkingen van geneesmiddelen
bij ouderen, met name de kwetsbare ouderen die in verpleeghuizen verblijven. Door middel
van farmacoepidemiologisch onderzoek kan men inzicht krijgen in het gebruik van geneesmid-
delen en de factoren die van invloed zijn op dit gebruik, in bepaalde populaties in de dagelijk-
se praktijk. Ons onderzoek geeft inzicht in het geneesmiddelgebruik, de factoren die dit gebruik
bepalen (‘determinanten’) en de uitkomsten van geneesmiddelgebruik bij ouderen, in het bij-
zonder verpleeghuisbewoners. Paragraaf 1.2 beschrijft de zorg voor thuiswonende ouderen en
voor ouderen die wonen in verzorgings- en verpleeghuizen. Het verpleeghuis in Nederland
biedt verzorging en verpleging aan personen die chronisch ziek zijn en continue medische en
paramedische zorg nodig hebben. Het type zorg dat in Nederlandse verpleeghuizen wordt gele-
verd wordt gekenmerkt als continu, op de lange termijn gericht, systematisch en multidiscipli-
nair. Er is een onderscheid tussen de zorg voor somatische verpleeghuisbewoners, die lijden
aan ernstige lichamelijke aandoeningen (zoals de ziekte van Parkinson) en de zorg voor psy-
chogeriatrische verpleeghuisbewoners, die lijden aan dementie en andere psychogeriatrische
164
namelijk die tussen NSAID’s en maagbeschermende geneesmiddelen. NSAID’s zijn bekend
vanwege maagdarmklachten, zoals maagdarmzweren en maagdarmbloedingen. Met name
ouderen zijn hier gevoelig voor. De aanbeveling is dat mensen ouder dan 65 jaar die een NSAID
nodig hebben, tevens een geneesmiddel moeten gebruiken dat de maag beschermt. We onder-
zochten of dit ook gebeurt in de dagelijkse praktijk. Dit was slechts het geval bij 23% van de
NSAID gebruikers boven de 65 jaar. Verder onderzoek is nodig om te achterhalen waarom 77%
van de oudere NSAID gebruikers niet tegelijkertijd een maagbeschermer kreeg voorgeschreven
en of er bij die patiënten inderdaad klinisch relevante maagdarmklachten optreden.
Bi jwerkingen van geneesmiddelen in de prakt i jk
Hoofdstuk 3 richt zich op de uitkomsten van geneesmiddelgebruik bij ouderen. Paragraaf 3.1
beschrijft een onderzoek naar de relatie tussen het laxantiagebruik in verpleeghuizen en het
gebruik van obstiperende medicatie. In dit onderzoek werd een epidemiologische techniek,
prescriptie-sequentie-analyse, toegepast. Dit houdt in dat we bestudeerden of laxeermiddelen
vaker werden voorgeschreven aan gebruikers van obstiperende geneesmiddelen om de bij-
werkingen van deze geneesmiddelen (obstipatie) tegen te gaan. Verpleeghuisbewoners die
geneesmiddelen met een matig tot sterk obstiperende werking gebruikten, hadden 60% meer
risico om een laxans te gebruiken dan verpleeghuisbewoners die deze geneesmiddelen niet
gebruikten. Dit risico was minder hoog dan in de literatuur is gemeld. In paragraaf 3.2 onder-
zochten we de klinische relevantie van een geneesmiddelinteractie, namelijk die tussen
NSAID’s en acenocoumarol (een bloedverdunnend middel). Beide middelen worden frequent
door ouderen gebruikt. De studiepopulatie bestond uit 112 patiënten die geselecteerd waren via
de Stichting Trombosedienst Groningen. Bij 46% bleek de INR (International Normalised
Ratio, een maat voor de stolling van het bloed) verhoogd te zijn wanneer deze patiënten
behandeld waren met een NSAID. Dit betekent een verhoogde kans op bloedingen. Het ver-
moeden bestond dat een bepaalde erfelijke eigenschap, namelijk een mutatie op het enzym-
systeem cytochroom P450 2C9 (CYP2C9), verantwoordelijk zou kunnen zijn voor de verhoogde
INR als gevolg van de geneesmiddelinteractie. Daarom is bij 80 patiënten het genotype van
CYP2C9 bepaald. Er bleek geen relatie aantoonbaar te zijn tussen de verhoogde INR (als gevolg
van de geneesmiddelinteractie) en het CYP2C9 genotype. Dit betekent dat op dit moment (nog)
niet voorspeld kan worden bij wie de INR stijgt door deze geneesmiddelinteractie en bij wie
niet. Deze studie laat zien dat het nuttig is om klinische gegevens (zoals de INR) en voor-
schrijfgegevens te gebruiken om mogelijk bijwerkingen van geneesmiddelen te kunnen detec-
teren. Daarnaast is duidelijk dat het lastig is om te voorspellen bij wie de interactie zal optre-
den en bij wie niet.
167
De doseringen die werden voorgeschreven waren relatief laag, met uitzondering van diuretica
en laxantia. In paragraaf 2.4 gaan we in op het gelijktijdig voorschrijven van geneesmiddelen
waarvan gelijktijdig gebruik juist wordt afgeraden, hetgeen tot een klinisch relevante bijwer-
king kan leiden. We hebben de frequentie, de aard en de duur van een aantal potentieel
schadelijke geneesmiddelcombinaties in hetzelfde cohort verpleeghuispatiënten onderzocht.
Hier bleek dat 32% van de populatie tenminste één ongewenste geneesmiddelcombinatie
kreeg voorgeschreven gedurende de studieperiode van 2 jaar. De interactie tussen lisdiuretica
en NSAID’s en de interactie tussen orale anticoagulantia (bloedverdunnende middelen) en
NSAID’s kwamen in deze populatie het meest frequent voor. Ook bleek dat per interactie niet
meer dan 10% van de bewoners waren blootgesteld. Paragraaf 2.5 beschrijft een pilot onder-
zoek waarin het geneesmiddelgebruik in twee verpleeghuizen is geëvalueerd door middel van
het toepassen van voorschrijfindicatoren (‘prescribing indicators’). In dit onderzoek wordt de
mate waarin het voorschrijven van geneesmiddelen afwijkt van bepaalde richtlijnen in ver-
pleeghuizen, zoals een geneesmiddelformularium, bestudeerd. Hier bleek dat het voorschrijf-
gedrag weinig afweek van de richtlijnen. Als er werd afgeweken, bijvoorbeeld bij een te hoge
dosering, bestond daar een goede reden voor en werden eventuele bijwerkingen in de gaten
gehouden. De voorschrijfindicatoren bleken goed bruikbaar om het voorschrijfgedrag in kaart
te brengen. Om vanuit voorschrijfgegevens suboptimaal voorschrijven te kunnen detecteren,
was vaak klinische informatie over de patiënt nodig. Het tweede deel bestaat uit 2 onderzoe-
ken die zijn uitgevoerd door middel van analyse van voorschrijfgegevens van ambulante oude-
ren. Hiervoor werd gebruik gemaakt van de InterActie-databank, een databestand waarin de
geneesmiddelgegevens van circa 135.000 mensen in de regio Noordoost Nederland geanoni-
miseerd worden vastgelegd. Paragraaf 2.6 beschrijft het gebruik van benzodiazepinen (slaap-
en kalmeringsmiddelen) bij ouderen boven de 65 jaar en verpleeghuisbewoners die antide-
pressiva gebruikten. Het bleek dat de ambulante ouderen die nieuwere antidepressiva gebruik-
ten (selectieve serotonine heropname remmers (SSRI’s)), een grotere kans hadden om met een
benzodiazepine te starten dan personen die klassieke antidepressiva (tricyclische antidepres-
siva (TCA’s)) gebruikten. Mogelijk komt dit doordat TCA’s een groter kalmerend effect hebben,
waar met name ouderen gevoelig voor zijn. Bij de verpleeghuispatiënten kon een dergelijk
verhoogde kans niet worden aangetoond, wellicht doordat deze mensen al veel benzodiazepi-
nen gebruikten. Opvallend was dat het gecombineerd gebruik van antidepressiva en benzodia-
zepinen groot was: meer dan 50% van de TCA en SSRI gebruikers kreeg tevens een benzodia-
zepine voorgeschreven. Het gelijktijdig gebruik bleek ook langdurig te zijn. Vervolgonderzoek
zou inzicht moeten geven in de redenen waarom deze middelen zo lang gelijktijdig voorge-
schreven worden. In paragraaf 2.7 onderzochten we een wenselijke geneesmiddelcombinatie,
166
Dankwoord
Dat dit proefschrift er ligt is voor het overgrote deel te danken aan de inspirerende, motiveren-
de en ondersteunende bijdragen van velen.
Allereerst mijn beide promotores: Lolkje de Jong-van den Berg en Koos Brouwers.
Lolkje, dankzij jouw enthousiasme, steun en vertrouwen in dit onderzoek is dit proefschrift nu
geworden wat het is. Ik ben ontzettend blij dat ik met je heb mogen samenwerken. Altijd wist
je me weer te overtuigen van de relevantie van het onderzoek. Ik heb veel geleerd van de
manier waarop jij onderzoek doet en steeds het wetenschappelijk aspect van elke studie bena-
drukt. Daarbij was het natuurlijk ook erg gezellig! Dank je voor de tijd die je altijd voor me had.
Koos, met jou begon dit onderzoek toen je me in 1994 de mogelijkheid gaf om het laxantia-
gebruik bij verpleeghuisbewoners nader te onderzoeken. Jij was de ‘linking-pin’ met de uni-
versiteit en degene die me enthousiast maakte voor het onderzoek. Ik waardeer jouw klinisch-
farmacologische blik altijd zeer. Dank je voor je vertrouwen en je grote bijdrage aan dit onder-
zoek.
Zonder mijn referent, Corinne de Vries, had dit proefschrift zeker nog wat langer op zich
moeten laten wachten. Corinne, vanaf het eerste artikel tot en met de discussie: je was altijd
ontzettend betrokken bij dit onderzoek. Kortom, de ‘ideale referent’! Je vertrek naar Engeland
veranderde hier gelukkig niets aan: via de mail heb je talloze versies van artikelen gecorrigeerd
en aangevuld. Jouw epidemiologische inbreng heeft mede voor de publicaties gezorgd. Dank je
voor zoveel input!!
De leden van de leescommissie, prof. dr. A.C.G. Egberts, prof. dr. F.M. Haaijer-Ruskamp en
prof. dr. J.P.J. Slaets, dank ik voor hun snelle beoordeling van het manuscript en de waardevol-
le opmerkingen.
Paul van den Berg, mede-auteur van bijna alle hoofdstukken van dit boekje, was het brein
achter alle analyses. Paul, van jouw expertise op het gebied van databestanden heb ik dank-
baar gebruik gemaakt. Je wist tot ver in het SQL-tijdperk nog met mijn dBase-bestanden om te
gaan, gelukkig zonder al te veel morren. Dank je voor je geduld en je kritische opmerkingen.
Dick Bloemhof en Jan Sijtsma: ik ben jullie zeer erkentelijk voor de hulp met het verzame-
len van de voorschrijfgegevens uit de verpleeghuizen.
Margriet Piersma-Wichers, mede dankzij jouw enthousiasme is paragraaf 3.2 in dit proef-
schrift verschenen. Ik vond het erg leuk dat je betrokken was bij dit onderzoek.
Lisa Pont: gelukkig kwam er toch nog een artikel over de ‘prescribing indicators’. Dank voor
je inbreng hierbij en natuurlijk ook voor de Engelse correcties in enkele stukken.
169
Betekenis voor de prakt i jk
In hoofdstuk 4 worden de resultaten van de onderzoeken in dit proefschrift in breder per-
spectief geplaatst en worden suggesties voor de praktijk en voor verder onderzoek gegeven.
Bijwerkingen van geneesmiddelen kunnen bij deze kwetsbare ouderen grote gevolgen op de
kwaliteit van leven hebben, zoals de kans op verminderd geheugen door benzodiazepinen, de
kans op een delier (acute verwardheid) door bepaalde (anticholinerge) medicatie, en de kans
op vallen door psychofarmaca. Daarom dienen de voordelen van het gebruik van geneesmid-
delen altijd afgewogen te worden tegen de mogelijke bijwerkingen, met name bij ouderen. Om
te bepalen welke patiënten het hoogste risico lopen op geneesmiddelgerelateerde problemen,
is onderzoek zoals in dit proefschrift beschreven nodig. Naast voorschrijfgegevens zijn ook kli-
nische gegevens (zoals laboratoriumuitslagen en gegevens over de diagnosen van een patiënt)
nodig om inzicht te krijgen in de relevantie van geneesmiddelgerelateerde problemen. Een
voorbeeld is de interactie tussen NSAID’s en anticoagulantia: we weten dat deze interactie rela-
tief veel voorkomt en mogelijk tot problemen kan leiden. Op dit moment weten we nog niet hoe
we de personen die het meeste risico lopen op een klinische relevant effect, van te voren kun-
nen herkennen. Door het combineren van individuele medicatiegegevens en klinische data kan
op patiëntniveau het optreden van geneesmiddelgerelateerde problemen bewaakt worden.
Tevens kan door het combineren van deze gegevens op grote schaal farmaco-epidemiologisch
onderzoek worden uitgevoerd waarbij de nadruk ligt op het bestuderen van uitkomsten van
geneesmiddelgebruik (zoals een ziekenhuisopname als gevolg van een bijwerking van een
geneesmiddel). Hierbij kunnen verschillende disciplines, zoals ziekenhuisapothekers, ver-
pleeghuisartsen en klinisch chemici een belangrijke rol spelen. De rol van de ziekenhuisapo-
theker kan zowel initiërend als faciliterend zijn, zowel op het gebied van de verzameling van
voorschrijfgegevens als op het gebied van het in samenwerking met universitaire centra uit-
voeren van farmacoepidemiologisch onderzoek.
168
En dan als laatste, de onmisbare basis die gevormd wordt door familie en vrienden. Lieve mam
en pap: jullie dank ik voor jullie niet aflatende steun en vertrouwen. Paula, Wiebe en Veerle:
Haarlem was (en is) altijd een prima afleiding! Petra en Karen: ik ben heel blij dat jullie mijn
paranimfen willen zijn. Lieve Peter, dank, heel veel dank voor al je liefde en support en voor
zoveel meer dat eigenlijk niet in dit boekje thuishoort.
171
Dan waren er nog vele SFF-ers die mijn pad hebben gekruist tijdens mijn wekelijkse dag-
jes op de universiteit. Roel, het was leuk om met jou en Taco de farepi-cursus in Boston te vol-
gen. Taco, dank voor je hulp bij de voor mij soms ingewikkelde logistische regressie analyses.
Hilde, bij jou kon ik altijd terecht met een statistisch vraagje. Op de valreep heb ik nog veel van
je geleerd tijdens je statistiek cursus. Ada, Bert, Claudia, Eric, Evelyn, Jackie, Janet, Jasper,
Jeroen, Jos, Jos, Maarten, Marijke, Sipke, Willemijn, René, Rogier: dank voor jullie meeleven tij-
dens de vorderingen van dit onderzoek. De SFF-borrels, lunches en de jaarlijkse bbq waren
altijd erg leuk (dit geldt ook voor de ex-SFF-ers)! De leden van de dRUGs-werkgroep dank ik
voor hun interesse en feedback.
Een aantal enthousiaste bijvakstudenten heeft mij geholpen bij de onderzoeken die zijn
beschreven in dit boekje. Anne-Margreeth Dijkema, jij was de eerste ‘bijvakker’ en jij hebt zeker
je bijdrage geleverd aan paragraaf 3.1. Maria Franken, jij hebt een belangrijk onderdeel van
paragraaf 2.5 uitgevoerd. Het was plezierig met je samen te werken. Arian Plat: jij was de (snel-
le) motor achter het onderzoek bij de coumarine-gebruikers. Dankzij jouw inzet bij de recrute-
ring van de patiënten was het mogelijk om het onderzoek naar het polymorfisme van CYP2C9
uit te voeren. Veel succes met jouw promotieonderzoek! Alieke van Dijk was er vervolgens om
de genotypering van 80 patiënten uit te voeren. Alieke, dank je voor je nauwgezette werk!
Aukje Stenekes bepaalde de R- en S-acenocoumarolspiegels (helaas kon dat niet meer in het
boekje). Dank voor je hulp bij het transport van de bloedmonsters.
Dan waren er natuurlijk de collega-ziekenhuisapothekers, die zorgden voor een goede
basis op het werk. Toen ik in 1997 in de apotheek van het Wilhemina Ziekenhuis Assen voor 0.8
begon, wist ik nog niet hoeveel tijd die andere 0.2 me zou kosten. Uiteindelijk was het de moei-
te waard en dat kan denk ik alleen als de basis goed is. Jaap: je was altijd geïnteresseerd in het
onderzoek. Daarbij kwam het goed uit dat ik als aandachtsgebied de verpleeghuizen had. Dank
je voor de mogelijkheid om de zomercursus Epidemiologie in Boston te kunnen volgen en voor
je hulp bij de medisch-ethische toetsing van het coumarine onderzoek. Wobbe, Lous en Hans:
ook jullie dank ik voor de interesse die jullie altijd hadden in mijn onderzoek. Hans: veel suc-
ces met jouw onderzoek. Alle andere apotheekmedewerkers: ik kijk met veel plezier terug op
mijn WZA-tijd! Sinds een paar maanden weer terug in het Friese: mijn kersverse collega’s van
de apotheek Zorggroep Noorderbreedte hebben alleen het staartje van het onderzoek meege-
maakt. Het was een drukke tijd, met veel printjes en ritjes naar Groningen. Bob, dank je voor
het meedenken met het coumarine onderzoek. Eric, Folgert, Jan Peter, Nicole, Rients, Romke:
dank voor jullie flexibiliteit. Dit geldt natuurlijk ook voor alle andere apotheek ZNB-medewer-
kers.
170
Curr icu lum vi tae
Karen van Dijk werd op 23 oktober 1966 geboren in Den Haag. Aan het Rhedens Lyceum te
Rozendaal behaalde zij in 1985 haar VWO diploma. Aansluitend begon zij met de studie
Farmacie aan de Rijksuniversiteit Groningen. Het doctoraalexamen werd in 1991 gehaald,
waarna zij een maand Frans studeerde in Besançon. Het apothekersexamen werd in 1992
behaald. Na een korte tijd werkzaam geweest te zijn in de openbare farmacie in Den Haag
(Waldeck Apotheek), werd in 1993 gestart met de opleiding tot ziekenhuisapotheker in
Heerenveen en daarna Leeuwarden (opleiders: prof.dr. J.R.B.J. Brouwers en drs. R.J. Boskma).
Het registratieonderzoek leidde tot een promotieonderzoek aan de Rijksuniversiteit
Groningen, vakgroep Sociale Farmacie, Farmacoepidemiologie en Farmacotherapie (prof.dr.
L.T.W. de Jong-van den Berg, prof. dr. J.R.B.J. Brouwers en dr. C.S. de Vries), dat in de periode
1995-2001 werd uitgevoerd. Van april 1997 tot oktober 2001 was zij als ziekenhuisapotheker
werkzaam in het Wilhelmina Ziekenhuis te Assen. In 1999 volgde zij de zomercursus
Epidemiologie aan het Epidemiology Research Institute te Boston. Sinds november 2001 werkt
zij in het Medisch Centrum Leeuwarden.
173
Publ icat ions
Publ icat ions re lated to the thes is
Van Dijk KN, De Vries CS, Brouwers JRBJ, De Jong-van den Berg LTW. Farmaco-epidemiolo-
gisch onderzoek in verpleeghuizen: een prospectief vervolgonderzoek naar de associatie tus-
sen medicatie en obstipatie. Ziekenhuisfarmacie 1996; 4: 242-4.
Van Dijk KN, De Vries CS, Van den Berg PB, Dijkema AM, Brouwers JRBJ, De Jong- van den
Berg LTW. Constipation as an adverse effect of drug use in nursing home patients: an overesti-
mated risk. Br J Clin Pharmacol 1998; 46: 255-61.
Van Dijk KN, De Vries CS, Van den Berg PB, Brouwers JRBJ, De Jong-van den Berg LTW. Drug
utilisation in Dutch nursing homes. Eur J Clin Pharmacol 2000; 55: 765-71.
Van Dijk KN, De Vries CS, Van den Berg PB, Brouwers JRBJ, De Jong-van den Berg LTW.
Occurrence of potential drug-drug interactions in nursing home patients. Int J Pharm Pract
2001; 9:45-52.
Van Dijk KN, Ter Huurne K, De Vries CS, Van den Berg PB, Brouwers JRBJ, De Jong-van den
Berg LTW. Prescribing of gastroprotective drugs among elderly NSAID users in the Netherlands.
Pharm World Sci (in press).
Van Dijk KN, De Vries CS, Ter Huurne K, Van den Berg PB, Brouwers JRBJ, De Jong-van den
Berg LTW. Concomitant prescribing of benzodiazepines during antidepressant therapy in the
elderly. J Clin Epidemiol (in press).
Van Dijk KN, Pont LG, De Vries CS, Franken M, Brouwers JRBJ, De Jong-van den Berg LTW.
Prescribing indicators as a tool to evaluate drug use in nursing homes: a pilot study. Submitted.
Van Dijk KN, Plat AW, Van Dijk AAC, Piersma-Wichers G, De Vries-Bots AMB, Slomp J, De
Jong-van den Berg LTW, Brouwers JRBJ. Potential interaction between acenocoumarol and
diclofenac, naproxen and ibuprofen and the role of CYP2C9 genotype. Submitted.
Other publ icat ions
Bloemhof H, Van Dijk KN, De Graaf SSN, Vendrig DEMM, Uges DRA. Sensitive method for
the determination of vincristine in human serum by high-performance liquid chromatography
after on-line column extraction. J Chromatogr 1991; 572: 171-9, Biomedical Applications.
Van Dijk KN, Van der Meer YG. Medicamenteuze behandeling van diabetische neuropathie.
Pharm Weekbl 1992; 127: 1081-2.
Van Dijk KN. Miltefosine: lokaal cytostaticum. Ziekenhuisfarmacie 1996; 12: 106.
Lentelink MB, De Vries TW, Van Dijk KN. Accidental metronidazol overdose in a preterm
newborn [letter]. Clin Pharmacokin 1997; 32: 496-7.
172