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Predicting Medication Related Problems in Older People

Jennifer Stevenson (Clinical Pharmacy Research Fellow, KHP)

Kadri Kindsiko (Pharmacy Student, KCL)

Research Team:

Jennifer Stevenson (Clinical Research Fellow, KCL),

Josceline Williams (Senior Pharmacist Elderly Care/KCL),

Dr Rebekah Schiff (Lead Elderly Care Physician, GSTT)

Prof JG Davies (Institute Pharmaceutical Science, KCL)

Funding:

GST Charity

Acknowledgements:

David Erskine (Director, Medicines Information, GSTT)

Tom Burham (Medicines Information, GSTT)

Karen Poole (Information Specialist, KCL)

Peter Milligan (Statistician, KCL)

Dr. Vivien Auyeung (Senior Lecturer, KCL)

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Contents

1. Background

2. Systematic review

3. Application of risk prediction tools

i. Method

ii. Results

iii. Challenges

iv. Conclusions

4. Further work

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Rationale for research

1. 1 in 6 people are over 65 years old1

2. 13,000 >90years old in 1911, 430,000 >90years old in 20111

3. UK admission due ADR = 6.5%2

• Who is most at risk of suffering an ADR?

• What makes them have a higher risk of an ADR?

• Can we predict who these people are?

Can risk prediction models identify patients at risk of suffering an ADR?

3

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Methods - Systematic Review

1. Question posed

2. Databases searched

3. Search terms used

e.g. Older patients:

● Ageing

● Aged

● Aging

● Elderly care

● Older People

● Older Person

● Aged over 80

4. Search strategy checked

5. Inclusion criteria agreed

6. Title/abstract review – two pharmacists independently

Iden

tifica

tion

Records identified through database searching

(n=12269)

Additional records identified through other sources e.g.

hand search, grey literature

(n=1154)

Total number of records

(n=13423)

Records screened by title/abstract

(n=13006)

Full text articles assessed for eligibility

(n=15)

Studies included in qualitative synthesis

(n=4)

Records excluded

(n=12456)

Full text articles excluded (n=11):

• No predictive model (n=4)

• No validation ( n= 4)

• Outcome not ADE/ADR (n=2)

• Patient <65 years old (n=1 )

Scre

en

ing

E

ligib

ility

In

clu

de

Potential medication related problem

(n=550)

Records excluded (n=535)

• Observational (n=325)

• Tool development (n=63)

• Tool application (n=147)

Duplicates removed

(n=417)

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Records excluded (n=535)

Observational studies “Tool” development “Tool” application

n = 325 n = 63 n = 147

Incidence of ADRs/ADE

Factors associated with ADR/ADE

Quality prescribing indicators

Inappropriate medication lists

e.g. STOPP/START3, Beer’s Criteria4,5,6,7

Application of prescribing indicators to

population

Association between prescribing

indicator and ADE/ADR

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A “good model”

Author Study Design

Variables included in score

Validation Variable Score OR (95% CI)

McElnay et

al., 19978

Country: UK Setting: Hospital Inpatient

Outcome: inpatient ADE

Inclusion:≥65years, non-elective admission, consent

Method: Phase 1 variable identification and model design (n=

929), Phase 2 Internal Validation (n= 204). Chart review,

computerised hospital records, structured patient interview within

72hours of admission

Assessment of ADE/ADR: Modified Naranjo

Antidepressants

Digoxin

GI problems

Abnormal K+ level

Thinks drug responsible

Angina

COAD

No

score

5.79 (2.12-5.85)

1.99 (1.05-2.33)

2.57 (1.35-4.91)

4.21 (2.18-8.14)

0.17 (0.07-0.42)

2.40 (1.06-5.44)

Sig. p=0.15

Sensitivity = 40.5%

Specificity = 69.0%

Discrimination = Not

measured

Tangiisuran,

B, 20099

(BADRI Risk

Score)

Country: UK Setting: Hospital Inpatient

Outcome: inpatient ADR

Inclusion: (Phase 1)≥65years, not admitted with self-poisoning,

medical notes available (Validation) ≥65years, consent, no

anticancer medication, no ADR on/causing admission

Method: Phase 1 variable identification and model design (n=

690), Phase 2 External Validation (n= 483). Review of drug chart,

lab parameters, reports/referrals from other healthcare providers,

observational data on admission and daily thereafter

Assessment of ADE/ADR: Hallas algorithm and Likert scale

derived by Bates et al.(Phase 1), Naranjo (Phase 2)

Hyperlipidaemia

No. of medications ≥8

Length of stay ≥12days

Hypoglycaemic agents

High WBC (admission)

1

1

1

1

1

3.32 (1.81-6.07)

3.30 (1.93-5.65)

2.27 (1.35-3.83)

1.91 (1.04-3.49)

1.55 (0.94-2.55)

Sig. p≤0.1

Sensitivity = 80.0%

Specificity = 55.0%

Discrimination

(AUCROC) = 0.73

(95% CI, 0.66-0.80)

Onder et

al.,201010

(GerontoNet

Risk Score)

Country: Italy Setting: Hospital Inpatient

Outcome: inpatient ADR

Inclusion:≥65years, taking medication, complete data for

variables available, consent, not on anticancer medication, no

ADR on/causing admission

Method: Phase 1 variable identification and model design (n=

5936), Phase 2 External Validation (n= 483). Review of chart, x-ray

films, lab parameters, medical histories to complete questionnaire

on admission and daily thereafter.

Assessment of ADE/ADR: Naranjo

≥4 co-morbidities

Heart failure

Liver disease

No. of drugs ≤5

No. of drugs 5-7

No. of drugs ≥8

Previous ADR

Renal failure

1

1

1

0

1

4

2

1

1.31 (1.04-1.64)

1.79 (1.39-2.30)

1.36 (1.06-1.74)

1.00 Reference

1.90 (1.35-2.68)

4.07 (2.93-5.65)

2.41 (1.79-3.23)

1.21 (0.96-1.51)

Sig. p<0.1

Sensitivity = 68.0%

Specificity = 65.0%

Discrimination

(AUCROC) = 0.70

(95% CI, 0.63-0.78)

Trivalle et al.,

201111

(Trivalle Risk

Score)

Country: France Setting: Rehabilitation centres

Outcome: inpatient ADE

Inclusion:≥65years, present for study duration

Method: n= 576. Weekly chart review, patient and nurse reporting.

Bootstrap validation.

Assessment of ADE/ADR: “Standarised 32 item checklist” with

monthly analyses by MDT to check if met 4 key criteria

No. of medications

0-6

7-9

10-12

≥13

Antipsychotic

Recent anticoagulant

0

6

12

18

9

7

1.9 (1.6-2.3)

2.5 (1.5-4.1)

2.0 (1.1-1.37)

Sig. p<0.05

Sensitivity = not

reported

Specificity = not

reported

Discrimination

(AUCROC) = 0.70

(95% CI, 0.65-0.74)

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A “good” model12

Four phase design

1. Development

2. Validation

3. Impact

4. Implementation

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Application of Risk Prediction Tools

Aims:

1. Apply risk tools to inpatient population

2. Assess usability of risk prediction tools

Methods:

Location: STH (Anne, Alex, Mark, Henry)

Timing: October discharges

Data source: EPR and EDL data

Pilot data collection – reviewed by 2 senior pharmacists

Data collected and manipulated in Excel

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Results

• Number of patients: 170

• Gender: 76 M (45%):94 F (55%)

• Mean number of drugs per patient: On admission = 6.0 (0-17) On discharge = 8.9 (2-24)

Top 10 drugs taken on

admission (n=1022)

Number of

prescriptions

Proportion

total drugs

(%)

Top 10 drugs taken on

discharge (n=1507)

Number of

prescriptions

Proportion

total drugs

(%)

vitamins 89 9 laxatives 142 9

lipid-regulating drugs 79 8 analgesics 131 9

antiplatelet drugs 77 8 vitamins 114 8

antisecretory drugs/mucosal

protectants 75 7 antisecretory drugs/mucosal

protectants 98 7

hypertension and HF 60 6 antiplatelet drugs 92 6

analgesics 56 6 lipid-regulating drugs 88 6

nitrates/CCBs/other antianginal

drugs 49 5 anaemias/other blood

disorders 72 5

anaemias/other blood disorders 46 5 hypertension and HF 63 4

drugs used in diabetes 41 4 nitrates/CCBs/other

antianginal drugs 54 4

diuretics 39 4 diuretics 52 4

• Age: Mean age = 82 years (66-104)

• Co-morbidities: Mean number of co-morbidities: 9.7

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Top 10 increase in number of drugs on discharge (per BNF class)

0

20

40

60

80

100

120

140

Nu

mb

er

of

med

icati

on

s

Medications on admission

Medications at discharge

Bone protection

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Challenging data

• Classifying co-morbidities

• Hypertension

• Heart failure

• Liver disease

• Procedure v comorbidity e.g. childhood tonsillectomy

• Smoking status

• Retired Council van driver

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How many co-morbidities?

1. liver cirrhosis

2. alcoholic liver disease

3. oesophageal varices

4. hepatocellular carcinoma

5. hepatic encephalopathy

6. portal hypertension

7. gallstones with dilated CBD

8. DM type 2

9. chronic kidney disease

10. hyperkalaemia

11. renal impairment

12. diabetic retinopathy

13. peripheral vascular disease

14. MI in 2007

15. previous R little toe and L hallux amputation

16. confusion

17. ulcer on sole of L foot

18. cocaine misuse

19. cardiogenic shock

20. hypovolaemic shock

21. ITU admission

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Challenging data

• Classifying co-morbidities

• Hypertension

• Heart failure

• Liver disease

• Procedure v comorbidity e.g. childhood tonsillectomy

• Smoking status

• Retired Council van driver

• Determining previous ADR

• Location of ADR information

• Severity of ADR

• Available data

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ADR risk according to score

Tool Risk varibale and score Total Score Percentage ADR risk

BADRI

Hyperlipidaemia 1 0 1 2 3 4 5

3% 5% 9%

18% 32% 38%

No. of medications ≥8 1

Length of stay ≥12days 1

Hypoglycaemic agents 1

High WBC (admission) 1

GerontoNet

≥4 co-morbidities 1 0-1

2-3

4-5

6-7

≥8

5%

4%

7%

12%

28%

Heart failure 1

Liver disease 1

No. of drugs ≤5 0

No. of drugs 5-7 1

No. of drugs ≥8 4

Previous ADR 2

Renal failure 1

Trivalle

No. medications 0-6

7-12

13-18

>18

12%

28%

35%

52%

0-6 0

7-9 6

10-12 12

≥13 18

Antipsychotic 9

Recent anticoagulant 7

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ADR risk according to score

Tool Score Percentage ADR risk

BADRI

0 3%

1 5%

2 9%

3 18%

4 32%

5 38%

GerontoNet

0-1 5%

2-3 4%

4-5 7%

6-7 12%

≥8 28%

Trivalle

0-6 12%

7-12 28%

13-18 35%

>18 52%

Low risk <10% Medium risk 10-20% High Risk >20%

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Conclusion

• Producing a tool needs to follow a robust approach

• Common risk factor identified is polypharmacy

• Application of tools unexpected challenges

Challenges:

1. Outcomes to measure

2. Classification of variables

3. What to do once risk level identified?

Search for holy grail?

To join the search contact: jennifer.stevenson@kcl.ac.uk

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References

1. ONS Census 2011 http://www.ons.gov.uk/ons/rel/census/2011-census/population-and-household-estimates-for-england-and-wales/index.html [Accessed online: 30th August 2012]

2. Pirmohammed M et al. Adverse drug reactions as a cause of admission to hospital: prospective analysis of 18,820 patients. BMJ 2004;329:15-19

3. Gallagher P et al. STOPP (Screening Tool of Older Person’s Prescriptions) and START (Screening Tool to Alert doctors to Right Treatment). Consensus Validation. Int Journal Clin Pharm Therap 2008; 46(2):72-83

4. Beers MH et al. Explicit Criteria for Determining Inappropriate Medication Use in Nursing Home Residents. Arch Intern Med 1991;151:1825-1832

5. Beers MH et al. Explicit Criteria for Determining Potentially Inappropriate Medication Use by the Elderly - An Update. Arch Intern Med 1997;157:1531-1536

6. Fick DM et al. Updating Beers Criteria for Potentially Inappropriate Medication Use in Older Adults – Results of a US Consensus Panel of Experts. Arch Intern Med 2003;163:2716-2724

7. American Geriatric Society. American Geriatric Society Updated Beer’s Criteria for Potentially Inappropriate Medication Use in Older Adults. J Am Geriatr Soc 2012;60(4):616-631

8. McElnay JC et al. Development of a Risk Model for Adverse Drug Events in the Elderly. Clin Drug Invest 1997;13(1):47-55

9. Tangiisuran B. Predicting Adverse Drug Reactions in the Hospitalised Elderly. PhD Thesis 2009.

10. Onder G et al. Development and validation of a score to assess risk of adverse drug reactions among in-hospital patients 65 years or older: The GerontoNet ADR Risk Score. Arch Intern Med 2010;170(13):1142-1148

11. Trivalle C et al. Risk factors for adverse drug events in hospitalised elderly patients: a geriatric score. European Geriatric Medicine 2011;2:284-289

12. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation and Updating. 2010. Rotterrdam, Springer.