BMJ...2016/06/04 · 4 Nicotra1 ([email protected]), Lorenza Scotti 1...

Confidential: For Review O

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Non-steroidal anti-inflammatory drugs and the risk of heart

failure: a nested case-control study from four European countries in the SOS Project

Journal: BMJ

Manuscript ID BMJ.2015.029885.R1

Article Type: Research

BMJ Journal: BMJ

Date Submitted by the Author: 06-Apr-2016

Complete List of Authors: Arfe, Andrea; University of Milano-Bicocca, Department of Statistics and Quantitative Methods Varas-Lorenzo, Cristina; RTI Health Solutions, Epidemiology Nicotra, Federica; University of Milano-Bicocca, Department of Statistics and Quantitative Methods Scotti, Lorenza; University of Milano-Bicocca, Department of Statistics and Quantitative Methods Zambon, Antonella; University of Milano-Bicocca, Department of Statistics and Quantitative Methods Kollhorst, Bianca; Leibniz Institute of Prevention Research and Epidemiology, Clinical Epidemiology Schink, Tania; Leibniz Institute of Prevention Research and Epidemiology,

Clinical Epidemiology Garbe, Edeltraut; Leibniz Institute of Prevention Research and Epidemiology, Clinical Epidemiology Herings, Ron; PHARMO Institute, Straatman, Huub; PHARMO Institute Schade, Rene; Erasmus University Medical Center, Villa, Marco; Local Health Authority ASL Cremona Lucchi, Silvia; Local Health Authority ASL Cremona Valkhoff, Vera; Erasmus MC, Department of Gastroenterology and Hepatology Romio, Silvana; Erasmus University Medical Center, Department of Medical Informatics

Thiessard, Frantz ; University of Bordeaux Segalen Pariente, Antoine; Univ. de Bordeaux, U657; INSERM, U657 Sturkenboom, Miriam; Erasmus University Medical Center, Medical Informatics Corrao, Giovanni; University of Milano-Bicocca, Department of Statistics and Quantitative Methods

Keywords: NSAIDs, COX-1, COX-2, Heart Failure, Nested case-control study, Multi-centre, Population-based

https://mc.manuscriptcentral.com/bmj

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Non-steroidal anti-inflammatory drugs and the risk of heart failure: a nested 1

case-control study from four European countries in the SOS Project 2

Andrea Arfè1 ([email protected]), Cristina Varas-Lorenzo2 ([email protected]), Federica 3

Nicotra1 ([email protected]), Lorenza Scotti

1 ([email protected]), Antonella Zambon

1 4

([email protected]), Bianca Kollhorst3 ([email protected]), Tania Schink3 5

([email protected]), Edeltraut Garbe3 ([email protected]), Ron Herings4 6

([email protected]), Huub Straatman4

([email protected]), René Schade5 7

([email protected]), Marco Villa6 ([email protected]), Silvia Lucchi6 8

([email protected]), Vera Valkhoff5 ([email protected]), Silvana Romio5 9

([email protected]), Frantz Thiessard7 ([email protected]), Martijn Schuemie

5 10

([email protected]), Antoine Pariente7 ([email protected]), Miriam 11

Sturkenboom5 ([email protected]), and Giovanni Corrao1 ([email protected]); on 12

behalf of the Safety of Non-steroidal Anti-inflammatory Drugs (SOS) project consortium 13

1 Unit of Biostatistics, Epidemiology and Public Health. University of Milano-Bicocca, Milan, Italy. 2 RTI 14

Health Solutions, Barcellona, Spain. 3 Leibniz Institute of Prevention Research and Epidemiology, Bremen, 15

Germany. 4 PHARMO Institute, Utrecht, the Netherlands.

5 Department of Medical Informatics, Erasmus 16

University Medical Center, Rotterdam, the Netherlands. 6 Local Health Authority ASL Cremona, Cremona, 17

Italy. 7 University of Bordeaux Segalen, Bordeaux, France. 18

Key words: NSAIDs, COX-1, COX-2, Heart Failure, Nested case-control study, Multi-centre, 19

Population-based, Database. 20

Word count: 4,268 21

Corresponding Author: Giovanni Corrao. Unit of Biostatistics, Epidemiology and Public Health, 22

Department of Statistics and Quantitative Methods, University of Milano-Bicocca. Via Bicocca 23

degli Arcimboldi, 8, Edificio U7, 20126 Milano, Italy. Tel.: +39-02-6448-5854. E-Mail: 24

[email protected] 25

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Abstract 1

Objectives: To fill existing knowledge gaps on the cardiovascular safety of Non-Steroidal Anti-2

Inflammatory Drugs (NSAIDs) by estimating the risk of hospitalization for heart failure (HF) 3

associated with use of individual NSAIDs. 4

Methods: This is a case-control study, nested in a cohort of adult (≥18 years) new users of 5

NSAIDs, performed in 5 population-based healthcare databases from 4 European countries (the 6

Netherlands, Italy, Germany, and the United Kingdom). Overall, 92,163 hospitalizations for HF 7

were identified and compared with 8,246,403 controls (matched via risk-set sampling according to 8

age, gender, year of cohort entry, and time in cohort) with respect to use of 27 individual NSAIDs, 9

including 23 traditional NSAIDs (tNSAIDs) and 4 selective COX-2 inhibitors (COXIBs). The dose-10

response relation between NSAID and HF was also assessed. 11

Results: Current use (i.e. ≤14 days in the past) of any NSAID was found to be associated with a 12

19% increase of the risk of a HF hospitalization (adjusted odds ratio, OR: 1.19; 95% confidence 13

interval, CI: 1.17, 1.22) compared with past use of any NSAIDs (i.e. >183 days in the past). The 14

risk of HF hospitalization was increased for seven tNSAIDs (diclofenac, ibuprofen, indomethacin, 15

ketorolac, naproxen, nimesulide and piroxicam) and two COXIBs (etoricoxib and rofecoxib), with 16

ORs (95% CIs) ranging from 1.16 (1.07, 1.27) for naproxen to 1.83 (1.66, 2.02) for ketorolac. The 17

risk of HF was doubled when NSAIDs were used at very-high doses. For indomethacin and 18

etoricoxib, even more moderate doses were associated with increased risk. Overall, there was no 19

evidence that celecoxib increased the risk of HF hospitalization at commonly used doses. 20

Conclusions: The risk of hospitalization for HF associated with current use of NSAIDs appears to 21

vary between individual NSAIDs and this effect is dose-dependent. The risk of HF hospitalization 22

associated with the use of a large number of individual NSAIDs reported by this study may help to 23

inform both clinicians and health regulators. 24

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Introduction 1

Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are a broad class of agents with analgesic 2

and anti-inflammatory action exerted through inhibition of the two recognized isoenzymes of 3

prostaglandin G/H synthase (also referred to as cyclo-oxygenase [COX]), namely COX-1 and 4

COX-2 [1]. Because the therapeutic action of these drugs is mostly mediated by inhibition of 5

COX-2, while their gastrointestinal adverse reactions are largely due to COX-1 inhibition, 6

NSAIDs selectively inhibiting COX-2 (collectively known as COXIBs) were developed in the 7

1990s to reduce the risk of gastrointestinal toxicity [2]. 8

Nevertheless, reports of cardiovascular (CV) adverse reactions began to emerge during 2000-9

2003 [3,4], and subsequent placebo-controlled trials showed that COXIBs were associated with 10

an increased risk of atherothrombotic vascular events [5,6]. More recently, though, meta-11

analyses of randomized trials and observational studies have shown that the higher CV risk is not 12

restricted to COXIBs, but also applies to some traditional NSAIDs (tNSAIDs) [7-12]. 13

In particular, use of NSAIDs was found to be associated with an increased HF risk from several 14

randomized clinical trials [11] and observational studies [13,14]. A large meta-analysis of over 15

600 randomized trials showed that COXIBs and high-doses of tNSAIDs (i.e., diclofenac, 16

ibuprofen and naproxen) increase the risk of HF hospitalization from 1.9-fold to 2.5-fold in 17

comparison with placebo [11]. In light of this worrying evidence, current guidelines limit the use 18

of NSAIDs in patients predisposed to HF, with a full contraindication for patients with diagnosed 19

HF [15]. 20

Nonetheless, there is still limited information on the risk of HF associated with the use of 21

individual NSAIDs (both COXIBs and tNSAIDs) in the real-world clinical practice, and 22

especially their dose-response relationships. To address these knowledge gaps, HF was included 23

as an outcome of interest in the context of the overall CV and gastrointestinal risk evaluation of 24

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individual NSAIDs within the Safety of Non-Steroidal Anti-Inflammatory (SOS) Project, a 1

multinational project funded by the European Commission under the 7th

Framework Program. A 2

large, common-protocol, nested case-control study based on electronic healthcare databases from 3

4 European countries was carried out. 4

Methods 5

Data sources 6

This study is based on five electronic health databases (DBs) from four European countries, i.e. 7

the Netherlands (PHARMO), Italy (SISR and OSSIFF), Germany (GePaRD) and the United 8

Kingdom (THIN). Overall, these DBs covered a total of over 37 million persons with different 9

time-windows of data availability between 1999 and 2010. Table 1 summarizes their main 10

characteristics. 11

Briefly, PHARMO is a population-based medical record linkage system covering about over 2 12

million inhabitants from the Netherlands. SISR is an electronic administrative healthcare DB 13

covering the about 10 million residents of the Italian Lombardy region, all receiving free 14

healthcare assistance from the Italian National Health Service (NHS). OSSIFF is an healthcare 15

DB covering the about 3 million individuals who are beneficiaries of 8 Local Health Authorities 16

of the Lombardy Region (as OSSIFF covers a subset of the population covered by SISR, for this 17

study the latter included only the about 7 million beneficiaries of NHS not already included in 18

OSSIFF). GePaRD is a claims DB covering about 14 million individuals throughout Germany 19

enrolled in 4 German Statutory Health Insurance providers. Lastly, THIN is a general practice 20

DB comprising primary care medical records from more than 10 million individuals in the UK. 21

Each of these DBs longitudinally recorded data on each member of its target population, 22

including demographic data, hospital discharge diagnoses and outpatient drug prescriptions. Data 23

on outpatient diagnoses was also available from GePaRD. In two DBs (PHARMO and THIN) 24

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the daily dose prescribed by the physician was recorded for each dispensed prescription. Further 1

details are reported elsewhere [16]. 2

Harmonization and data processing 3

Since DBs differed in several aspects, including type of collected information (i.e. healthcare 4

utilization, claims, and primary care data) and classification systems used for disease and 5

medication coding (Table 1), a data harmonization was performed according to a procedure 6

developed for the European eu-ADR project [17]. Specifically, the Unified Medical Language 7

System [17] (for clinical diagnoses and conditions) and the Anatomic-Therapeutic-Chemical 8

(ATC) classification system (for drug prescriptions) were mapped into the coding systems used 9

by the individual DBs. This mapping ensured that the data extraction processes targeted the same 10

semantic concepts across all DBs, thus allowing analyses to be performed under a common data 11

model. 12

Anonymized data were extracted locally and processed with the software Jerboa©, developed by 13

Erasmus MC, providing individual-level datasets in a common data format. These datasets were 14

securely transferred into the SOS data-warehouse, hosted by the University of Milano-Bicocca, 15

to be analysed in a centralized and secure way [18]. 16

Cohort selection and follow-up 17

Following the new-users paradigm [19], a cohort of individuals starting therapy with NSAIDs 18

was selected from all DBs. In detail, adults (≥18 years) who received at least one NSAID 19

prescription or dispensation (ATC: M01A; excluding topical NSAIDs) during 2000-2010 were 20

considered eligible to enter the cohort. The date of first recorded prescription or dispensation was 21

defined as the date of cohort entry. Subjects were excluded if: i) they did not have at least one 22

year of uninterrupted observation prior to the date of cohort entry, to ensure enough time of 23

observation for assessing baseline covariates and applying the next exclusion criteria; ii) they 24

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received one or more NSAIDs within the year preceding the date of cohort entry, to exclude 1

prevalent NSAIDs users; iii) they received a diagnosis of malignant cancer, with the exception of 2

non-melanoma skin cancers, to exclude patients who may have had particular contraindications; 3

or iv) they were hospitalized with a primary diagnosis of HF in the year before the date of cohort 4

entry, to avoid the inclusion of events which had occurred before the start of NSAIDs use (note 5

that secondary hospital or outpatient HF diagnoses were not considered as exclusion criteria). 6

Each cohort member accumulated person-years of follow-up from the date of cohort entry until 7

the earliest date among that of outcome onset, i.e. admission date for the first hospitalization 8

with a primary diagnosis of HF, or censoring, i.e. end of registration in the DB due to death or 9

emigration, diagnosis of malignancy (excluding non-melanoma skin cancers), or end of the DB-10

specific data availability. 11

Cases and controls 12

A case-control study was nested into the cohort of new users of NSAIDs. The endpoint of 13

interest was the first hospitalization for HF (i.e. with HF as the main cause or reason of hospital 14

admission) identified during follow-up. HF is a clinical syndrome involving several 15

pathophysiological mechanisms which, along with factors triggering circulatory 16

decompensation, may give heterogeneous clinical manifestations which often receive delayed 17

diagnosis. Therefore, our endpoint definition did not include i) diagnostic codes for clinical HF 18

in the outpatient setting and ii) secondary hospital discharge codes for HF (which likely 19

represent HF manifestations occurring during hospitalizations for other causes). 20

Consequently, cases were all cohort members hospitalized for HF during follow-up, identified 21

either from primary hospital discharge diagnoses (PHARMO, SISR, OSSIFF, GePaRD) or codes 22

registered by the general practitioner (THIN). The admission date of the first hospitalization for 23

HF identified during follow-up was defined as the index date. Codes used to identify HF cases in 24

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each DB are reported in the supplementary material (Table S1). 1

Each case was matched to up to 100 controls randomly selected by risk-set sampling from all 2

cohort members whose follow-up did not end prior to the index date of the considered case (i.e. 3

among those still at risk of experiencing a hospitalization for HF). Matching was performed 4

within each DB on the basis of gender, age at cohort entry (± 1 year), and date of cohort entry (± 5

28 days). 6

Exposure to NSAIDs 7

All NSAIDs dispensations received by cohort members during follow-up were identified. For 8

each cohort member, the period covered by the availability of each individual NSAID was 9

directly calculated by the prescribed daily dose, if available (i.e. PHARMO and THIN DBs), or 10

by dividing the total amount of drug prescribed for the defined daily dose 11

[http://www.whocc.no/ddd/]. 12

Cohort members were classified into the following categories of current, recent, and past NSAID 13

users. Current users were patients with NSAID availability at the index date or the preceding 14 14

days. The remaining patients were defined recent users if they had NSAID availability during the 15

time-window of 15-183 days before the index date, or past users otherwise (reference). 16

Covariates 17

Several covariates were assessed for each cohort member if available in the corresponding DB, 18

including: i) prior history of outpatient or secondary inpatient diagnoses of HF, comorbidities, 19

and lifestyle features or clinical characteristics, assessed in the 12 months before cohort entry; 20

and ii) concomitant use of specific drugs, assessed in the 90 days before the index date. 21

Comorbidities were assessed using hospital discharge diagnoses (PHARMO, GePaRD, SISR, 22

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OSSIFF), outpatient clinical diagnoses (GePaRD), clinical electronic GP records (THIN), and 1

use of specific drugs. The full list of covariates is reported in Table 2. 2

3

Statistical analysis 4

Individual-level data from all DBs were firstly gathered into a pooled dataset and analysed by 5

means of a multivariable conditional logistic regression model [20]. The Odds Ratio (OR), with 6

95% confidence intervals (CIs), estimated the risk of hospitalization for HF associated with 7

current use of individual NSAIDs with respect to past use of any NSAID. The OR associated 8

with recent use of any NSAID, compared with past use of any NSAID, was also estimated. 9

Among the abovementioned covariates, those available in all DBs (including prior history of 10

outpatient or secondary inpatient diagnoses of HF prior history of outpatient or secondary 11

inpatient diagnoses of HF) entered the model. Subgroups analyses were performed after 12

stratification for gender and prior history of HF diagnoses. 13

Because DBs differed with respect to covered populations, as well as type and level of detail of 14

available covariates, we evaluated the robustness of the pooled estimates using a meta-analytic 15

approach by means of the following procedure. Firstly, we separately fitted a conditional logistic 16

regression model to estimate the effect of each individual NSAID within each DB. To avoid 17

computational issues (i.e. model convergence failure due to sparse data), only individual 18

NSAIDs with at least five exposed cases were considered in the model. The covariates available 19

for all DBs were always forced to enter the model provided they reached at least 5% prevalence 20

among controls. Other covariates were included provided they 1) resulted statistically 21

significantly (p-value <0.05) associated with the outcome in a univariate analysis, and 2) were 22

selected from a backward procedure (p-value for removal >0.10). Secondly, a random-effects 23

meta-analytic model [21,22] was subsequently used to estimate a summarized OR accross 24

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databases, with 95% CI, for current use of each individual NSAID (provided that a point 1

estimate was available from at least two DBs) compared with past use of any NSAID. 2

Heterogeneity between DB-specific ORs was assessed by means of the Cochran’s Q and 3

Higgins’ I2 statistics [23]. 4

5

Dose-response analysis 6

A dose-response analysis was performed to assess how the risk of HF hospitalization associated 7

with current use of individual NSAIDs varied along the considered prescribed daily dose 8

categories. As Italian and German DBs did not recorded data on prescribed daily doses, this 9

analysis was performed by pooling individual-level data gathered from DBs from the 10

Netherlands (PHARMO) and the United Kingdom (THIN). Patients for whom the information 11

on the prescribed daily dose was not available were excluded. 12

The prescribed daily dose was expressed in defined daily dose equivalents and categorized as 13

low (≤ 0.8 defined daily doses, DDDs), medium (from 0.9 to 1.2 DDDs), high (from 1.3 to 1.9 14

DDDs) or very high dose (≥ 2 DDDs) with respect to the corresponding defined daily dose 15

[http://www.whocc.no/ddd/]. To avoid computational issues, only NSAIDs for which all the 16

considered categories included at least one HF case were considered in the analysis. Tests for 17

trends in ORs were performed. 18

Statistical analyses were implemented using the SAS© software (v9.3; SAS Institute Inc. Cary, 19

NC, USA). All tests were two-sided and considered statistically significant for p-values <0.05. 20

21

Results 22

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Subjects 1

The flow-chart describing the attrition of eligible NSAIDs users after applying the exclusion 2

criteria is reported in the Supplementary Figure S1. Among the almost 10 million new users of 3

NSAIDs identified across all DBs, 7,680,181 met the inclusion criteria and constituted the study 4

cohort. Cohort members accumulated 24,555,063 person-years of follow-up and generated 5

92,163 cases of hospitalized HF (incident rate: 37.5 HF events per 10,000 person-years). Cases 6

were matched to 8,246,403 controls. 7

As shown in Table 2, mean age (SD) was 77 (11) and 76 (10) years respectively among cases 8

and controls. About 45% of both cases and controls were men. Compared to controls, cases had 9

more comorbidities (mainly CV disease, such as acute myocardial infarction, other ischemic 10

heart diseases, atrial fibrillation and flutter and valvular disease and endocarditis) and more often 11

received concomitant drug therapies (e.g., anticoagulants, cardiac glycosides, nitrates and 12

Cyp2C9 inhibitors). About 9.1% of cases and 2.5% of controls had a history of clinical 13

diagnoses of HF, either recorded as outpatient or a secondary hospital diagnoses in the year prior 14

to starting therapy with NSAIDs (cohort entry). 15

Use of NSAIDs and HF risk 16

Respectively 16,081 (14.5%) and 1,193,537 (14.4%) of cases and matched controls were current 17

users of NSAIDs. Figure 1 reports the distribution of current use of individual NSAIDs among 18

all cases and controls. Among controls, the most frequently used tNSAIDs were diclofenac 19

(2.9%), nimesulide (2.4%), and ibuprofen (1.7%), while the most frequently used COXIBs were 20

celecoxib (1.4%), rofecoxib (1.0%), and etoricoxib (0.6%). 21

According to the pooled analysis, subjects currently using any NSAID had a 20% higher HF risk 22

than past users (OR: 1.19; 95% CI: 1.17, 1.22). Conversely, there was no evidence that recent 23

use of any NSAID involved differences in HF risk with respect to past use (1.00; 0.99, 1.02). As 24

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shown in Figure 1, a statistically significant higher risk of HF was observed in association with 1

current use of 9 individual NSAIDs: ketorolac, etoricoxib, indomethacin, rofecoxib, piroxicam, 2

diclofenac, ibuprofen, nimesulide, and naproxen as compared with past use of any NSAIDs. 3

Other less frequently used NSAIDs (e.g. sulindac, acemethacin and dexibuprofen) were also 4

found to be associated with an increased risk of HF, although the 95% CIs included the null 5

value. 6

For the 9 abovementioned individual NSAIDs statistically significantly associated with HF risk, 7

their association was also confirmed regardless of whether there was recorded evidence of a 8

prior HF diagnosis or not as well as by gender (Table 3). Interestingly, among women, the 9

estimated risk of HF in associated with current use of nimesulide, etoricoxib, and indomethacin, 10

as compared with past use of any NSAIDs, was lower in magnitude than among men. 11

According to meta-analytic analysis, subjects currently using any NSAID had a 24% higher HF 12

risk than past users (OR: 1.24; 95% CI: 1.12, 1.36). Besides the abovementioned 9 individual 13

NSAIDs, current use of nabumetone was also found associated with higher HF risk (Figure 2). 14

Although between-DB heterogeneity was relevant (I2 > 70%), meta-analytic OR estimates were 15

generally consistent with the corresponding ones obtained from the analysis of pooled 16

individual-level data. 17

Dose-response relationship 18

A total of 20 (0.2%) cases and 855 (0.1%) controls from PHARMO and 753 (4.3%) cases and 19

61,777 (4.3%) controls from THIN were excluded because prescribed daily dose data were not 20

recorded. The remaining 25,179 cases and 2,083,706 controls gathered from PHARMO and 21

THIN entered the dose-response analysis. 22

As shown in Figure 3, current users of very-high doses of diclofenac, etoricoxib, indomethacin, 23

piroxicam, and rofecoxib had a more than two-fold higher risk of HF than past users. The OR 24

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associated with current high-dose use of ibuprofen was also compatible with an increased risk 1

despite the wide confidence interval. Finally, there was no evidence that celecoxib increased the 2

risk of hospitalized HF at commonly used doses compared with past use of any NSAIDs. An 3

increase in risk cannot however be excluded when celecoxib is used at very high doses given the 4

wide confidence intervals obtained for this dose class. 5

6

Supplementary findings 7

The distributions of case and controls according to the considered covariates, the use of 8

individual NSAIDs, and the dose categories of current NSAIDs use (in DDD equivalents and 9

corresponding daily amount of active principle in mg), as well as the effects of individual 10

NSAIDs on the HF risk, are reported in Supplementary Tables S2-S5. 11

Discussion 12

Our study based on “real-world” data on almost 10 million NSAIDs users from four European 13

countries, provides evidence that current use of both coxibs and traditional NSAIDs are 14

associated with increased risk of HF and that this association varies between individual NSAIDs 15

and according to the prescribed dose. 16

NSAIDs exert their pharmacological action by inhibiting the COX-1 and COX-2 prostaglandin 17

G/H synthase isoenzymes [1]. The overall effects of the NSAID-mediated inhibition of the 18

prostaglandin synthesis are to increase peripheral systemic resistance and reduce renal perfusion, 19

glomerular filtration rate, and sodium excretion in susceptible individuals [24,25]. Taken 20

together, these mechanisms may potentially trigger clinical HF manifestations, especially in 21

susceptible patients [26]. Additionally, since the level of prostaglandin inhibition mediated by 22

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NSAIDs increases with dose [14,27], the risk of clinical HF may be expected to increase along 1

with the used NSAIDs dose. 2

Consistently, our study found an increased risk of hospitalization for HF in association with 3

current use of several tNSAIDs (i.e. diclofenac, ibuprofen, indomethacin, ketorolac, naproxen, 4

nimesulide and piroxicam, and possibly nabumetone) and two COXIBs (etoricoxib and 5

rofecoxib). Additionally, we found evidence that the increased HF risk also concerned patients 6

without prior outpatient HF diagnosis or secondary hospital diagnosis, i.e. patients ideally less 7

susceptible to HF decompensations. We also observed a dose-dependent increasing HF risk for 8

most individual NSAIDs. Finally, indomethacin and etoricoxib appeared to increase the risk of 9

HF hospitalization even if used at medium doses. 10

No statistically significant differences in the magnitude of the association between use of 11

individual NSAIDs and HF risk were found between patients with/without prior HF (for all 12

NSAIDs) and between genders (with a few exceptions). However, power may have been low to 13

detect significant differences between the considered subgroups. 14

Our study did not find that celecoxib, the most widely prescribed selective COX-2 inhibitor, 15

increases the risk of HF hospitalization. Power limitations unlikely explain such lack of 16

evidence. Indeed, we calculated that our main analysis had 80% power to detect significant ORs 17

as low as 1.08 for current use of celecoxib [28]. Additionally, celecoxib used at the highest doses 18

did not show increased HF risk but power was low for this dose class (our analysis had only 19

about 30% power to detect significant ORs of 2.00 for this dose class). 20

All these findings extend those of the meta-analysis of randomized trials showing that the risk of 21

hospitalization due to HF was roughly doubled by all studied NSAID regimens compared with 22

placebo [11]. Similarly, a meta-analysis of six trials did not show differences in HF risk between 23

tNSAIDs and COXIBs [13]. The estimates provided by the few published observational studies 24

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on the NSAID-HF association were compatible with an increased HF risk associated with 1

naproxen, ibuprofen, ketoprofen, piroxicam, indomethacin, and rofecoxib, but not for celecoxib 2

[14,26, 29-32]. Our results are also compatible with the body of evidence supporting the relative 3

CV safety of low-medium doses celecoxib for treatment of arthritis compared to all other 4

COXIBs [33]. 5

Taken together, our findings support the hypothesis that both selective and non-selective COX-2 6

inhibitors increase the risk of HF, but that the magnitude of this effect varies between individual 7

drugs and according to the used dose [24]. The effect of individuals NSAIDs may in fact depend 8

upon a complex interaction of pharmacological properties, including duration and extent of 9

platelet inhibition, extent of blood pressure increase, and properties possibly unique to the 10

molecule [33]. 11

Importantly, our findings, which focused only on prescription NSAIDs, might apply to over-the-12

counter (OTC) NSAIDs as well. Although the latter are probably typically used at lower doses, 13

by younger people, and for shorter durations than prescribed NSAIDs, OTC NSAIDs are 14

sometimes available at the same doses than those prescribed [34] and they may be 15

inappropriately overused [35]. This suggests that our findings may have large-scale public health 16

consequences and that further research is needed to assess the safety of OTC NSAIDs under the 17

conditions they are typically used. 18

The present study, conducted as part of the EU-funded SOS project, is based on data from very 19

large and unselected populations from four European countries. Data were derived from 5 20

healthcare databases which collectively represented a precious source of information to 21

investigate the safety of a large number of individual NSAIDs. The heterogeneity of these 22

sources should be considered a strength of the SOS project, since it allowed to compare the risk 23

of HF associated with a large number of individuals NSAIDs as used in different populations and 24

healthcare systems. 25

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Some limitations should however be acknowledged. First, our study may not have captured all 1

NSAID exposure, since some of these drugs (e.g. ibuprofen) are also available OTC in all the 2

four countries. Hence patients classified as non-current users of NSAIDs in this study may 3

actually have been current users of OTC NSAIDs. Such misclassification would tend, on 4

average, to bias estimates toward the null [36,37], with the implication that our findings may 5

understate the actual association between use of individual NSAIDs and HF risk. 6

Second, validity of outcome ascertainment may be of concern since HF is often associated with 7

other CV diseases (e.g. myocardial infarction) which may affect how hospital discharge codes 8

are recorded. Nevertheless, although privacy concerns inhibited the validation of records in most 9

participating DBs, the positive predictive value for HF hospitalizations included in the Italian 10

OSSIFF DB was found to be 80% (95% CI: 66%, 90%). Additionally, high positive predictive 11

values have been reported by other investigations based on healthcare databases for hospital 12

discharge HF diagnosis codes considered in our study [38]. In fact, the incidence of almost 37.5 13

HF cases every 10,000 person-years observed in our study does not substantially differ from 14

rates reported by available population-based studies [39]. Still, even admitting some outcome 15

misclassification [40], this is expected to be non-differential, i.e. independent of current use of 16

NSAIDs, leading to a bias dragging estimated associations towards the null [41]. It should be 17

however emphasized that non-differential misclassification (of outcome or exposure) might lead 18

to inflated observed associations due to chance alone [42]. 19

Third, our dose-response analysis may have been underpowered for some dose classes for some 20

NSAIDs since only the PHARMO and THIN databases could be considered. Additionally, a 21

portion of patients registered in the PHARMO and THIN databases had to be excluded from the 22

dose-response analysis because they lacked the prescribed daily dose information. Although this 23

might have led to some bias [43], the number of excluded subjects was low. Hence, it seems 24

implausible that their exclusion could have had a significant impact on the results. 25

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Fourth, the impact of heterogeneous baseline patients’ characteristics must be considered when 1

interpreting our findings. Indeed, some individual NSAIDs more frequently used for different 2

acute or chronic indications may have resulted in different patterns of use, as well as in different 3

types of populations of users [44]. To address this possibility, our pooled estimates were adjusted 4

for several patients’ baseline demographic, therapeutic, and clinical characteristics (including 5

osteoarthritis, rheumatoid arthritis and inflammatory polyarthritis) measured in all the included 6

data sources. In addition, estimates did not substantially change in the random-effects meta-7

analytic approach, where DB-specific estimates were adjusted for all baseline covariates 8

available in the considered data source. Moreover, relative risk estimates for individual NSAIDs 9

among patients with prior outpatient or secondary hospital diagnoses of HF (i.e. patients with a 10

contraindication for NSAIDs use who also should be more susceptible for acute clinical 11

manifestations of HF) did not appear to be substantially different than those obtained in the 12

overall analysis. Taken together, these results provide some protection to our findings. 13

Nevertheless, we cannot exclude that residual differences in patient's baseline characteristics 14

may account for some of the observed variations in relative risk estimates associated with 15

different individual NSAIDs. 16

Lastly, residual confounding must also be considered. This is related to the fact that some 17

diseases that modify both the risk of HF and the probability of current NSAID use may have not 18

been fully accounted for in this study. To protect against this possibility, all our estimates were 19

adjusted for concomitant (i.e. in the current period) use of specific drugs (e.g. nitrates, diuretics, 20

other drugs for CV diseases) as a proxy of patients’ current health status. Still, residual 21

confounding cannot be excluded. For example, gout is potentially an uncontrolled confounder of 22

the association between current use of NSAIDs and HF risk in this study. This is because i) gout 23

is an independent risk factor for HF [45] and ii) NSAIDs are the first pharmacological choice for 24

treating acute gout episodes [46]. However, assuming that gout has a 1% prevalence in our 25

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source population and that it increases HF risk by 1.74-fold [45,47], we estimated [49] that acute 1

gout episodes should increase the odds of being treated with naproxen (the NSAID with the 2

weakest statistically significant association with HF among those investigated) in the current 3

rather than the past period by 33-fold (an implausibly high amount) to fully explain the observed 4

naproxen-HF association. These considerations further strengthen our conclusions. 5

In conclusion, our study offers further evidence that the most frequently used traditional NSAIDs 6

and selective COX-2 inhibitors are associated with an increased risk of hospitalization for heart 7

failure. Moreover, the risk appears to vary between molecules and according to the dose. For the 8

less frequently used NSAIDs we were not able to exclude a risk of low-moderate magnitude due 9

to the limited numbers of exposed cases identified in this study. Since any potential increased 10

risk may result in a considerable public health impact, the risk effect estimates provided by this 11

study may help inform both clinical practices and regulatory activities. 12

Conflicts of Interests 13

Giovanni Corrao collaborated with the advisory boards of Novartis and Roche and participated in 14

projects funded by GSK. Huub Straatman; Ron Herings: are employees of the PHARMO Institute. 15

This independent research institute performs financially supported studies for government and 16

related healthcare authorities and several pharmaceutical companies. Bianca Kollhorst and Tania 17

Schink are working in departments that occasionally perform studies for the pharmaceutical 18

companies. These include Bayer-Schering, Celgene, GSK, Mundipharma, Novartis, Purdue, Sanofi-19

Aventis, Sanofi-Pasteur, Stada, and Takeda. Edeltraut Garbe is running a department that 20

occasionally performs studies for pharmaceutical industries. These companies include Bayer, 21

Celgene, GSK, Mundipharma, Novartis, Sanofi, Sanofi Pasteur MSD, and STADA. EG has been a 22

consultant to Bayer, Nycomed, Teva, GSK, Schwabe and Novartis. SOS was not (co)-funded by 23

any of these companies. Silvia Lucchi and Marco Villa, as employees of the Local Health Authority 24

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of Cremona, have perfomed research studies sponsored by pharmaceutical companies (Pfizer Italia, 1

GlaxoSmithKline and Novartis V&D) unrelated to this study. Cristina Varas-Lorenzo, as employee 2

of RTI Health Solutions, worked on projects funded by pharmaceutical companies including 3

manufacturers of treatments for pain and inflammation and also participates in advisory boards 4

funded by pharmaceutical companies. Martijn J. Schuemie, has since completion of this research 5

accepted a full-time position at Janssen R&D. Vera Valkhoff, as employee of Erasmus MC, has 6

conducted research for AstraZeneca. Miriam Sturkenboom is head of a unit that conducts some 7

research for pharmaceutical companies: Pfizer, Novartis, Lilly and Altana. The SOS Project was not 8

(co)-funded by any of these companies. All other authors have no conflicts of interest to declare. 9

Acknowledgements and Funding 10

The research leading to the results of this study has received funding from the European 11

Community’s Seventh Framework Programme under grant agreement number 223495 - the SOS 12

project. We thank all members of the SOS project consortium for their collaborative efforts 13

(http://www.sos-nsaids-project.org/). 14

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19

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Tables 1

Table 1. Databases considered as data sources in this study among those participating to the Safety 2

of Non-Steroidal Anti-Inflammatory (SOS) Project 3

Country Database* Type

Size of the

covered

population

Covered

period

Diagnoses

coding Drugs coding

The

Netherlands

PHARMO (PHARMO Institute for

Drug Outcomes

Research)

Record

linkage

system

2.2 million 1999-2008 ICD-9-

CMa

ATCd

Italy

SISR**

(Sistema Informativo

Sanitario Regionale)

Healthcare

Utilization

DB

10.4 million

(excluding

OSSIFF:

7.5 million)

2003-2008 ICD-9-

CMa

ATCd

OSSIFF (Osservatorio

Interaziendale per la

Farmacoepidemiologia e

la Farmacoeconomia)

Healthcare

Utilization

DB

2.9 million 2000-2008 ICD-9-

CMa

ATCd

Germany

GePaRD (German

Pharmacoepidemiological

Research Database)

Claims DB 13.7 million 2004-2009 ICD-10-

GMb

ATCd

United

Kingdom

THIN (The Health Improvement

Network)

General

Practice

DB

11.1 million 1999-2010 READ v2c BNF/Multilex

e

4 * Other databases participated in the SOS Project but did not contribute data to this study. See Reference 19. 5

** Because OSSIFF covers a subset of patients also covered by SISR, this database excluded the common subset of 6 patients to avoid overlap 7 a International Classification of Diseases, 9th revision, clinical modification [http://www.who.int/classifications/icd/en] 8

b International Classification of Diseases, 10th revision, German modification 9

c READ clinical classification system [Chisholm J. The Read clinical classification. BMJ 1990;300:1092] 10 d Anatomical-Therapeutic-Chemical classification system [http://www.whocc.no/atc/] 11

e British National Formulary codes / Multifunctional Standardised Lexicon for European Community Languages codes 12

[http://www.fdbhealth.com/multilex-drug-terminology] 13 14

15

16

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Table 2. Clinical features and other selected characteristics of the 92,163 patients hospitalized for 1

heart failure and the 8,246,403 matched control patients included into the study. The Safety of Non-2

Steroidal Anti-Inflammatory (SOS) Project 3

Case patients Controls N 92,163 8,246,403

Men 41,652 (45.2%) 3,671,565 (44.5%)

Age at cohort entry: mean (SD) 77 (11) 76 (10)

Comorbidities and other characteristics a

Acute Myocardial Infarctionc

3,063 (3.3%) 81,222 (1.0%)

Alcohol Abuse (ATC: N07BB*) 1,942 (2.1%) 128,871 (1.6%)

Asthmac 1,031 (1.1%) 57,079 (0.7%)

Atrial Fibrillation and Flutterc 4,606 (5.0%) 110,217 (1.3%)

Chronic Liver Diseasec 1,815 (2.0%) 98,762 (1.2%)

Chronic Respiratory Diseasec (ATC: R03*) 16,190 (17.6%) 870,497 (10.6%)

Diabetesc

(ATC: A10*) 17,888 (19.4%) 725,320 (8.8%)

Heart Failurec (ATC: C07AG02) 8,353 (9.1%) 209,125 (2.5%)

Hyperlipidemiac (ATC: C10*) 18,793 (20.4%) 1,160,532 (14.1%)

Hypertensionc 19,905 (21.6%) 1,515,002 (18.4%)

Iron Deficiency Anemia (ATC: B03A*) 2,159 (2.3%) 83,926 (1.0%)

Ischemic Heart Diseasec 8,406 (9.1%) 294,986 (3.6%)

Kidney Failure 1,445 (1.6%) 41,094 (0.5%)

Obesity (ATC: A08A*) 4,555 (4.9%) 181,104 (2.2%)

Osteoarthritisc 6,916 (7.5%) 483,721 (5.9%)

Other Cardiovascular Diseasec,d

(ATC: C01B*) 13,055 (14.2%) 463,797 (5.6%)

RA and Inflammatory Polyarthritisc

(ATC: M01C*) 736 (0.8%) 40,269 (0.5%)

Smoking 164 (0.2%) 8,155 (0.1%)

Strokec 1,869 (2.0%) 85,109 (1.0%)

Valvular Disease and Endocarditisc 2,383 (2.6%) 70,646 (0.9%)

Concomitant use of other drugs b

ACE Inhibitor/AT-II Antagonistsc 38,834 (42.1%) 2,030,050 (24.6%)

Anticoagulantsc 17,589 (19.1%) 442,725 (5.4%)

Aspirinc 31,658 (34.4%) 1,669,443 (20.2%)

Beta Blockersc 22,506 (24.4%) 1,253,749 (15.2%)

Calcium Channel Blockersc 28,911 (31.4%) 1,754,965 (21.3%)

Cardiac Glycosidesc 14,429 (15.7%) 342,042 (4.1%)

Cyp2C9 Inducers 38 (0.0%) 1,149 (0.0%)

Cyp2C9 Inhibitors 8,289 (9.0%) 174,253 (2.1%)

Diureticsc 48,991 (53.2%) 1,536,700 (18.6%)

Glucocorticoidsc 8,636 (9.4%) 349,012 (4.2%)

Nitratesc 24,029 (26.1%) 717,669 (8.7%)

Platelet Aggregation Inhibitorc 9,105 (9.9%) 367,716 (4.5%)

Vasodilatorsc 1,654 (1.8%) 44,916 (0.5%)

a Assessed in the 12 months before cohort entry. Based on inpatient diagnoses, outpatient diagnoses (GePaRD only), 4 medical history (THIN only), or selected drug prescriptions belonging to the indicated ATC codes (only for specific 5 covariates) 6 b Assessed in the 14 days preceding the index HF hospitalization 7

c Available in all databases 8 d Other cardiovascular diseases included: cardiac arrhythmia/conduction disorders and arrest, cardiomyopathies, 9

peripheral arterial diseases, arterial embolism and thrombosis, myocarditis and pericarditis 10

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Confidential: For Review Only26

Table 3. Pooled odds ratio (ORs), and 95% confidence intervals (95% CIs), for the association between current use of individual NSAIDs, recent

use of any NSAID, compared with past use of any NSAIDs with the risk of hospitalization for heart failure (HF), according to the evidence of prior

HF (i.e. outpatient or secondary hospital HF diagnoses prior to NSAIDs therapy initiation) and gender. P-values testing between-strata homogeneity

of ORs are reported.

Strata No prior HF

(case patients: 83,810) Prior HF

(case patients: 8,353)

Men (case patients: 41,652)

Women (case patients: 50,511)

OR 95%CI OR 95% CI P-value for

homogeneity OR 95%CI OR 95% CI

P-value for

homogeneity

Current use of:

Indomethacin 1.52 (1.31, 1.77) 1.58 (0.55, 4.51) 0.943 1.71 (1.41, 2.07) 1.25 (1.00, 1.57) 0.038

Sulindac 1.62 (0.90, 2.94) n.a. n.a. n.a. 2.19 (0.80, 5.97) 1.50 (0.71, 3.16) 0.554

Diclofenac 1.21 (1.16, 1.26) 1.14 (0.91, 1.42) 0.606 1.21 (1.13, 1.29) 1.19 (1.13, 1.26) 0.703

Etodolac 0.83 (0.59, 1.17) n.a. n.a. n.a. 0.92 (0.56, 1.50) 0.77 (0.48, 1.23) 0.609

Acemetacin 1.67 (0.83, 3.35) 0.28 (0.03, 2.32) 0.125 1.22 (0.52, 2.85) 2.08 (0.84, 5.12) 0.399

Ketorolac 1.94 (1.71, 2.19) 5.09 (0.97, 26.57) 0.254 1.86 (1.52, 2.28) 1.96 (1.70, 2.27) 0.680

Aceclofenac 1.00 (0.87, 1.14) 0.89 (0.20, 3.89) 0.878 1.13 (0.90, 1.41) 0.94 (0.80, 1.11) 0.194

Diclofenac, combinations 1.02 (0.92, 1.14) 0.89 (0.36, 2.16) 0.767 1.03 (0.87, 1.22) 1.01 (0.89, 1.16) 0.858

Piroxicam 1.31 (1.21, 1.41) 1.90 (1.01, 3.59) 0.254 1.34 (1.18, 1.53) 1.31 (1.20, 1.44) 0.780

Tenoxicam 1.03 (0.74, 1.43) n.a. n.a. n.a. 0.88 (0.47, 1.62) 1.07 (0.75, 1.53) 0.592

Lornoxicam 1.13 (0.81, 1.57) 2.25 (0.28, 18.08) 0.522 1.22 (0.68, 2.18) 1.07 (0.73, 1.56) 0.712

Meloxicam 0.99 (0.91, 1.09) 0.95 (0.43, 2.07) 0.919 1.13 (0.97, 1.31) 0.95 (0.85, 1.06) 0.068

Ibuprofen 1.15 (1.08, 1.21) 1.34 (1.05, 1.70) 0.226 1.18 (1.09, 1.29) 1.16 (1.09, 1.25) 0.758

Naproxen 1.19 (1.08, 1.31) 0.87 (0.32, 2.38) 0.543 1.24 (1.08, 1.42) 1.15 (1.01, 1.30) 0.428

Ketoprofen 1.04 (0.95, 1.13) 1.00 (0.50, 2.03) 0.913 1.15 (1.00, 1.32) 0.98 (0.88, 1.09) 0.074

Flurbiprofen 1.08 (0.72, 1.62) n.a. n.a. n.a. 1.19 (0.62, 2.31) 0.83 (0.50, 1.39) 0.397

Oxaprozin 0.82 (0.55, 1.23) 0.26 (0.02, 3.77) 0.396 0.45 (0.19, 1.08) 0.96 (0.61, 1.51) 0.130

Dexibuprofen 1.24 (0.89, 1.74) n.a. n.a. n.a. 0.92 (0.50, 1.67) 1.38 (0.93, 2.03) 0.269

Celecoxib 0.95 (0.89, 1.02) 1.05 (0.53, 2.06) 0.774 1.01 (0.90, 1.13) 0.94 (0.87, 1.01) 0.301

Rofecoxib 1.34 (1.25, 1.44) 0.91 (0.35, 2.42) 0.434 1.35 (1.20, 1.52) 1.37 (1.26, 1.48) 0.840

Valdecoxib 1.04 (0.69, 1.56) 0.47 (0.03, 8.01) 0.581 0.95 (0.45, 2.02) 1.09 (0.70, 1.71) 0.758

Etoricoxib 1.55 (1.42, 1.69) 1.35 (0.75, 2.44) 0.650 1.80 (1.57, 2.07) 1.45 (1.31, 1.61) 0.014

Nabumetone 1.07 (0.81, 1.43) 11.14 (0.67, 184.24) 0.104 1.14 (0.73, 1.80) 1.17 (0.83, 1.64) 0.928

Nimesulide 1.21 (1.16, 1.27) 1.00 (0.62, 1.60) 0.433 1.31 (1.21, 1.42) 1.17 (1.10, 1.23) 0.023

Recent use of any NSAID 1.01 (0.99, 1.03) 0.97 (0.85, 1.11) 0.557 1.04 (1.01, 1.07) 0.99 (0.96, 1.01) 0.012

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Legend of figures 1

2

Figure 1. Distribution of current use of individual NSAIDs among cases and controls and forest 3

plot of the pooled associations between current use of individual NSAIDs and the risk of 4

hospitalization for heart failure, with past use of any NSAID as reference. Estimates were obtained 5

by pooling individual data from all available databases 6

Footnote. Pooled odds ratio (OR) and 95% confidence intervals (95% CI) were estimated by fitting a conditional 7

logistic regression model after correcting for available covariates (see text) 8

9

Figure 2. Forest plot of the summarized associations between of current use of individual NSAIDs 10

and the risk of hospitalization for heart failure, compared to past use of any NSAID. Estimates were 11

obtained by summarizing DBs specific estimates by the random-effects meta-analytic approach 12

Footnote. Database-specific odds ratios were summarized into a unique odds ratio (ORs) admitted that it was 13

considered from at least two databases. ORs, and 95% confidence intervals (95% CI), were estimated by using a 14

random-effects model. Heterogeneity between DB-specific ORs was assessed by means of Cochran’s Q (and 15

corresponding p-value) and Higgins’ I2 statistics (see text). The number of summarized databases (N) is reported in the 16

last column 17

18

Figure 3. Dose-response relationship between the currently prescribed dose of certain NSAIDs and 19

the risk of heart failure with respect to past use of any NSAID 20

Footnote. Pooled data from the Netherlands (PHARMO) and United Kingdom (THIN) DBs were used for this analysis. 21

The dose currently prescribed of each NSAID was categorized as low (0.8 daily dose equivalents), medium (from 0.9 to 22

1.2), high (from 1.3 to 1.9) and very high dose (≥ 2 defined daily dose equivalents). Odds ratio (OR) and 95% 23

confidence intervals (95% CI) were estimated by fitting a conditional logistic regression model after correcting for 24

available covariates (see text) 25

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Supplementary material 1

2

Figure S1. Flow-chart of inclusion/exclusion criteria 3

4

Table S1. Codes considered to identify HF patients by the included databases 5

6

Table S2. Clinical features and other selected characteristics of case patients hospitalized for heart 7

failure and matched controls included into each database 8

9

Table S3. Database-specific distributions of current and recent NSAIDs users among cases and 10

controls 11

12

Table S4. Database-specific adjusted Odds Ratio (ORs), with 95% Confidence Intervals (95% CI), 13

measuring the association between current use of individual NSAIDs (compared with past use of 14

any NSAID) and risk of a hospitalization for heart failure 15

16

Table S5. Distributions cases and controls along the categories of the current prescribed dose (in 17

DDD equivalents and corresponding daily amount of active principle in mg) of selected individual 18

NSAIDs. Data obtained by pooling PHARMO and THIN databases 19

20

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Figure 1

215x170mm (300 x 300 DPI)

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Figure 2

215x114mm (300 x 300 DPI)

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Figure 3

170x189mm (300 x 300 DPI)

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150x150mm (300 x 300 DPI)

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Table S1. Codes considered to identify HF patients by the included databases.

Coding system Database

(Country) Codes/term

ICD-9-CMa SISR (IT)

OSSIFF (IT)

PHARMO (NL)

398.91, 402.01, 402.11, 402.91, 404.01, 404.03,

404.11, 404.13, 404.91, 404.93, 428, 428.0, 428.1,

428.9

ICD-10-GMb GePaRD (GER) I11.0, I11.00, I11.01, I13.0, I13.00, I13.01, I13.2,

I13.20, I13.21, I50, I50.0, I50.00, I50.01, I50.1,

I50.11, I50.12, I50.13, I50.14, I50.19, I50.9

READc v2 THIN (UK) 14A6.00, G1yz100, G21z100, G232.00, G234.00,

G58..00, G58..11, G580.00, G580.11, G580.12,

G580.13, G580.14, G580000, G580100, G580200,

G580300, G581.00, G581.11, G581.12, G581.13,

G581000, G582.00, G58z.00, G58z.11, G58z.12

a International Classification of Diseases, 9th revision, clinical modification [http://www.who.

int/classifications/icd/en]

b International Classification of Diseases, 10th revision, German modification

c READ clinical classification system [Chisholm J. The Read clinical classification. BMJ 1990;300:1092]

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Table S2. Clinical features and other selected characteristics of case patients hospitalized for

heart failure and matched controls included into each database.

S2.1 THE NETHERLANDS - PHARMO (PHARMO Institute for Drug Outcomes

Research)

Case patients

(N=8,250)

Controls

(N=653,466)

Men 4,646 (60.46%) 426,158 (62.33%)


Comorbidities and other characteristics*

Acute Myocardial Infarction 73 (0.88%) 2130 (0.33%)

Alcohol Abuse 16 (0.19%) 500 (0.08%)

Asthma 9 (0.11%) 199 (0.03%)

Atrial Fibrillation and Flutter 127 (1.54%) 2803 (0.43%)

Chronic Liver Disease 8 (0.10%) 334 (0.05%)

Chronic Respiratory Disease 1949 (23.62%) 86416 (13.22%)

Diabetes 1611 (19.53%) 52329 (8.01%)

Heart Failure 134 (1.62%) 2131 (0.33%)

Hyperlipidemia 1843 (22.34%) 91339 (13.98%)

Hypertension 702 (8.51%) 36948 (5.65%)

Iron Deficiency Anemia 361 (4.38%) 12444 (1.90%)

Ischemic Heart Disease 231 (2.80%) 7376 (1.13%)

Kidney Failure n.a.

Obesity 16 (0.19%) 948 (0.15%)

Osteoarthritis 157 (1.90%) 10831 (1.66%)

Other Cardiovascular Disease 519 (6.29%) 11422 (1.75%)

Rheumatoid Arthritis and Inflammatory Polyarthritis 18 (0.22%) 506 (0.08%)

Smoking 9 (0.11%) 279 (0.04%)

Stroke Confounder 26 (0.32%) 625 (0.10%)

Valvular Disease and Endocarditis 24 (0.29%) 403 (0.06%)

Concomitant use of other drugs**

ACE Inhibitor/AT-II Antagonists 3861 (50.24%) 192401 (28.14%)

Anticoagulants 1909 (24.84%) 44910 (6.57%)

Aspirin 1466 (19.08%) 56614 (8.28%)

Beta Blockers 3854 (50.15%) 201893 (29.53%)

Calcium Channel Blockers 2255 (29.34%) 116932 (17.10%)

Cardiac Glycosides 1193 (15.52%) 23416 (3.43%)

Cyp2C9 Inducers 6 (0.07%) 113 (0.02%)

Cyp2C9 Inhibitors 496 (6.01%) 8132 (1.24%)

Diuretics 4256 (55.38%) 113224 (16.56%)

Glucorticoids 801 (10.42%) 33115 (4.84%)

Nitrates 1163 (15.13%) 33499 (4.90%)

Platelet Aggregation Inhibitor 629 (8.18%) 18744 (2.74%)

Vasodilators 520 (6.77%) 15409 (2.25%)

* Assessed in the 12 months preceding cohort entry. ** Assessed in the 14 days preceding the index HF hospitalization. n.a.: not available

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S2.2 ITALY – SISR (Sistema Informativo Sanitario Regionale)

Case patients

(N=34,773)

Controls

(3,312,454)

Men 14,140 (40.66%) 1,338,754 (40.42%)



Acute Myocardial Infarction 956 (2.75%) 22,591 (0.68%)

Alcohol Abuse 73 (0.21%) 2,704 (0.08%)

Asthma 79 (0.23%) 3,499 (0.11%)

Atrial Fibrillation and Flutter 1,596 (4.59%) 35,072 (1.06%)

Chronic Liver Disease 378 (1.09%) 15,486 (0.47%)

Chronic Respiratory Disease 5,584 (16.06%) 327,712 (9.89%)

Diabetes 8,139 (23.41%) 371,582 (11.22%)

Heart Failure 3,833 (11.02%) 91,385 (2.76%)

Hyperlipidemia 7,334 (21.09%) 526,980 (15.91%)

Hypertension 10,215 (29.38%) 854,865 (25.81%)

Iron Deficiency Anemia n.a.

Ischemic Heart Disease 2,300 (6.61%) 62,802 (1.90%)

Kidney Failure 133 (0.38%) 3,460 (0.10%)

Obesity 438 (1.26%) 9,758 (0.29%)

Osteoarthritis 646 (1.86%) 48,086 (1.45%)

Other Cardiovascular Disease 5,644 (16.23%) 188,134 (5.68%)

Rheumatoid Arthritis and Inflammatory Polyarthritis 157 (0.45%) 7,153 (0.22%)

Smoking n.a.

Stroke Confounder 414 (1.19%) 21,469 (0.65%)

Valvular Disease and Endocarditis 510 (1.47%) 10,053 (0.30%)


ACE Inhibitor/AT-II Antagonists 16,991 (48.86%) 974,943 (29.43%)

Anticoagulants 6,999 (20.13%) 189,656 (5.73%)

Aspirin 12,786 (36.77%) 759,562 (22.93%)

Beta Blockers 7,627 (21.93%) 435,123 (13.14%)

Calcium Channel Blockers 12,617 (36.28%) 861,069 (25.99%)

Cardiac Glycosides 5,470 (15.73%) 144,214 (4.35%)

Cyp2C9 Inducers n.a.

Cyp2C9 Inhibitors 3,999 (11.50%) 90,114 (2.72%)

Diuretics 17,507 (50.35%) 556,149 (16.79%)

Glucorticoids 3,062 (8.81%) 130,582 (3.94%)

Nitrates 10,567 (30.39%) 338,230 (10.21%)

Platelet Aggregation Inhibitor 4,033 (11.60%) 189,748 (5.73%)

Vasodilators n.a.


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S2.3 ITALY – OSSIFF (Osservatorio Interaziendale per la Farmacoepidemiologia e

la Farmacoeconomia)

Case patients

(N=23,753)

Controls

(N=2,159,548)

Men 9,924 (41.78%) 884,886 (40.98%)




Alcohol Abuse 58 (0.24%) 2,334 (0.11%)

Asthma 61 (0.26%) 2,396 (0.11%)

Atrial Fibrillation and Flutter 859 (3.62%) 19,174 (0.89%)

Chronic Liver Disease 233 (0.98%) 9,287 (0.43%)


Diabetes 3,899 (16.41%) 153,790 (7.12%)

Heart Failure 1,504 (6.33%) 36,585 (1.69%)

Hyperlipidemia 2,864 (12.06%) 193,743 (8.97%)

Hypertension 5,241 (22.06%) 408,379 (18.91%)

Iron Deficiency Anemia 625 (2.63%) 28,750 (1.33%)


Kidney Failure 46 (0.19%) 913 (0.04%)

Obesity 130 (0.55%) 2120 (0.10%)

Osteoarthritis 521 (2.19%) 31,274 (1.45%)



Smoking n.a.





Anticoagulants 4,520 (19.03%) 121,319 (5.62%)

Aspirin 7,275 (30.63%) 398,196 (18.44%)

Beta Blockers 3,223 (13.57%) 183,367 (8.49%)



Cyp2C9 Inducers 18 (0.08%) 673 (0.03%)

Cyp2C9 Inhibitors 2,567 (10.81%) 56,793 (2.63%)

Diuretics 11,539 (48.58%) 339,377 (15.72%)

Glucorticoids 1,895 (7.98%) 77,641 (3.60%)

Nitrates 6,850 (28.84%) 205,666 (9.52%)


Vasodilators n.a.


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S2.4 GERMANY – GePaRD (German Pharmacoepidemiological Research

Database)

Case patients

(N=7,685)

Controls

(N=683,663)

Men 3,937 (47.72%) 303,716 (46.48%)



Acute Myocardial Infarction 1,239 (16.12%) 37,070 (5.42%)

Alcohol Abuse 229 (2.98%) 7,702 (1.13%)

Asthma 610 (7.94%) 36,198 (5.29%)

Atrial Fibrillation and Flutter 1,735 (22.58%) 45,724 (6.69%)

Chronic Liver Disease 1,166 (15.17%) 72,751 (10.64%)


Diabetes 1,944 (25.30%) 71,867 (10.51%)

Heart Failure 2,767 (36.01%) 77,676 (11.36%)

Hyperlipidemia 2,506 (32.61%) 149,564 (21.88%)

Hypertension 2,439 (31.74%) 150,496 (22.01%)



Kidney Failure 1,176 (15.30%) 34,817 (5.09%)

Obesity 1,737 (22.60%) 76,194 (11.14%)

Osteoarthritis 2,233 (29.06%) 187,447 (27.42%)



Smoking n.a.


Valvular Disease and Endocarditis 1,500 (19.52%) 52,696 (7.71%)



Anticoagulants 1,909 (24.84%) 44,910 (6.57%)

Aspirin 1,466 (19.08%) 56,614 (8.28%)

Beta Blockers 3,854 (50.15%) 201,893 (29.53%)



Cyp2C9 Inducers 5 (0.07%) 64 (0.01%)

Cyp2C9 Inhibitors 496 (6.01%) 8,132 (1.24%)

Diuretics 4,256 (55.38%) 113,224 (16.56%)

Glucorticoids 801 (10.42%) 33,115 (4.84%)

Nitrates 1,163 (15.13%) 33,499 (4.90%)

Platelet Aggregation Inhibitor 629 (8.18%) 18,744 (2.74%)

Vasodilators 520 (6.77%) 15,409 (2.25%)


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S2.5 UNITED KINGDOM – THIN (The Health Improvement Network)

Case patients

(N=17,702)

Controls

(N=1,437.272)

Men 9,005 (50.87%) 718,051 (49.96%)




Alcohol Abuse 1,566 (8.85%) 115,631 (8.05%)

Asthma 272 (1.54%) 14,787 (1.03%)

Atrial Fibrillation and Flutter 289 (1.63%) 7,444 (0.52%)

Chronic Liver Disease 30 (0.17%) 904 (0.06%)


Diabetes 2,295 (12.96%) 75,752 (5.27%)

Heart Failure 115 (0.65%) 1,348 (0.09%)

Hyperlipidemia 4,246 (23.99%) 198,906 (13.84%)

Hypertension 1,308 (7.39%) 64,314 (4.47%)



Kidney Failure 87 (0.49%) 1,849 (0.13%)

Obesity 2,234 (12.62%) 92,084 (6.41%)

Osteoarthritis 3,359 (18.98%) 206,083 (14.34%)

Other Cardiovascular Disease 738 (4.17%) 18,969 (1.32%)


Smoking 155 (0.88%) 7,876 (0.55%)



Concomitant use of other drugs*


Anticoagulants 2,252 (12.72%) 41,931 (2.92%)

Aspirin 8,665 (48.95%) 398,457 (27.72%)

Beta Blockers 3,948 (22.30%) 231,473 (16.11%)



Cyp2C9 Inducers 9 (0.05%) 299 (0.02%)

Cyp2C9 Inhibitors 731 (4.13%) 11082 (0.77%)

Diuretics 11,433 (64.59%) 414,726 (28.86%)

Glucorticoids 2,077 (11.73%) 74,559 (5.19%)

Nitrates 4,286 (24.21%) 106,775 (7.43%)


Vasodilators 614 (3.47%) 14,098 (0.98%)


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Table S3. Database-specific distributions of NSAIDs use status among case and control.

S3.1 THE NETHERLANDS - PHARMO (PHARMO Institute for Drug Outcomes

Research)

NSAIDs exposure Case patients

(N=8,250)

Controls

(N=653,466)

Current Use of:

Aceclofenac 3 (0.04%) 261 (0.04%)

Acemetacin n.a.

Azapropazone 2 (0.02%) 58 (0.01%)

Celecoxib 47 (0.57%) 2,825 (0.43%)

Dexibuprofen 2 (0.02%) 90 (0.01%)

Dexketoprofen 0 (0.00%) 7 (0.00%)

Diclofenac 380 (4.61%) 21,456 (3.28%)

Diclofenac, combinations 144 (1.75%) 8,273 (1.27%)

Etodolac n.a.

Etoricoxib 69 (0.84%) 2,363 (0.36%)

Fenbufen n.a.

Fenoprofen n.a.

Fentiazac n.a.

Flurbiprofen 0 (0.00%) 87 (0.01%)

Ibuprofen 171 (2.07%) 10,700 (1.64%)

Ibuprofen, combinations n.a.

Indometacin 32 (0.39%) 992 (0.15%)

Ketoprofen 6 (0.07%) 441 (0.07%)

Ketoprofen, combinations n.a.

Ketorolac n.a.

Lonazolac n.a.

Lornoxicam n.a.

Lumiracoxib n.a.

Meclofenamic acid n.a.

Mefenamic acid n.a.

Meloxicam 93 (1.13%) 5,574 (0.85%)

Mofebutazone n.a.

Morniflumate n.a.

Nabumetone 17 (0.21%) 919 (0.14%)

Naproxen 137 (1.66%) 7,303 (1.12%)

Naproxen and esomeprazole n.a.

Niflumic acid n.a.

Nimesulide n.a.

Oxaprozin n.a.

Parecoxib n.a.

Phenylbutazone 1 (0.01%) 13 (0.00%)

Prioxicam 18 (0.22%) 873 (0.13%)

Proglumetacin n.a.

Rofecoxib 111 (1.35%) 5,234 (0.80%)

Sulindac 13 (0.16%) 331 (0.05%)

Tenoxicam 0 (0.00%) 15 (0.00%)

Tiaprofenic acid 2 (0.02%) 219 (0.03%)

Tolfenamic acid 0 (0.00%) 7 (0.00%)

Valdecoxib 0 (0.00%) 31 (0.00%)

Recent Use of any NSAID 1,725 (20.91%) 142,047 (21.74%)

n.a.: not available

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S3.2 ITALY – SISR (Sistema Informativo Sanitario Regionale)


(N=34,773)

Controls

(3,312,454)

Current Use of:

Aceclofenac 224 (0.64%) 21,005 (0.63%)

Acemetacin 0 (0.00%) 10 (0.00%)

Azapropazone n.a.

Celecoxib 532 (1.53%) 59,505 (1.80%)

Dexibuprofen 32 (0.09%) 2,294 (0.07%)

Dexketoprofen 0 (0.00%) 1 (0.00%)

Diclofenac 1,010 (2.90%) 74,937 (2.26%)


Etodolac n.a.

Etoricoxib 411 (1.18%) 30,116 (0.91%)

Fenbufen n.a.

Fenoprofen n.a.

Fentiazac 1 (0.00%) 47 (0.00%)

Flurbiprofen 13 (0.04%) 1224 (0.04%)

Ibuprofen 514 (1.48%) 31,834 (0.96%)

Ibuprofen, combinations n.a.

Indometacin 95 (0.27%) 5262 (0.16%)

Ketoprofen 483 (1.39%) 44,291 (1.34%)

Ketoprofen, combinations 0 (0.00%) 1 (0.00%)

Ketorolac 239 (0.69%) 9,993 (0.30%)

Lonazolac n.a.

Lornoxicam 34 (0.10%) 3,046 (0.09%)

Lumiracoxib n.a.

Meclofenamic acid 0 (0.00%) 13 (0.00%)

Mefenamic acid 0 (0.00%) 84 (0.00%)

Meloxicam 214 (0.62%) 21,162 (0.64%)

Mofebutazone n.a.

Morniflumate 1 (0.00%) 10 (0.00%)

Nabumetone 18 (0.05%) 1,967 (0.06%)

Naproxen 121 (0.35%) 11,748 (0.35%)


Niflumic acid 0 (0.00%) 1 (0.00%)

Nimesulide 1,770 (5.09%) 128,443 (3.88%)

Oxaprozin 16 (0.05%) 2,440 (0.07%)

Parecoxib n.a.

Phenylbutazone n.a.

Prioxicam 531 (1.53%) 41,483 (1.25%)

Proglumetacin 8 (0.02%) 873 (0.03%)

Rofecoxib 394 (1.13%) 27,740 (0.84%)

Sulindac 1 (0.00%) 132 (0.00%)

Tenoxicam 32 (0.09%) 2,907 (0.09%)


Tolfenamic acid n.a.

Valdecoxib 23 (0.07%) 1,787 (0.05%)

Recent Use of any NSAID 12,916 (37.14%) 1,213,352 (36.63%)

n.a.: not available

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S3.3 ITALY – OSSIFF (Osservatorio Interaziendale per la Farmacoepidemiologia e

la Farmacoeconomia)


(N=23,753)

Controls

(N=2,159,548)

Current Use of:

Aceclofenac 62 (0.26%) 6,757 (0.31%)

Acemetacin 0 (0.00%) 21 (0.00%)

Azapropazone n.a.

Celecoxib 374 (1.57%) 33,872 (1.57%)

Dexibuprofen 9 (0.04%) 876 (0.04%)

Dexketoprofen n.a

Diclofenac 592 (2.49%) 42,466 (1.97%)


Etodolac n.a.

Etoricoxib 194 (0.82%) 10,370 (0.48%)

Fenbufen n.a

Fenoprofen n.a

Fentiazac 0 (0.00%) 23 (0.00%)

Flurbiprofen 15 (0.06%) 1,117 (0.05%)

Ibuprofen 186 (0.78%) 11,194 (0.52%)

Ibuprofen, combinations 0 (0.00%) 1 (0.00%)

Indometacin 53 (0.22%) 2,942 (0.14%)

Ketoprofen 251 (1.06%) 21,191 (0.98%)

Ketoprofen, combinations n.a

Ketorolac 210 (0.88%) 7,462 (0.35%)

Lonazolac n.a

Lornoxicam 15 (0.06%) 1,200 (0.06%)

Lumiracoxib n.a

Meclofenamic acid n.a

Mefenamic acid 0 (0.00%) 23 (0.00%)

Meloxicam 121 (0.51%) 11,721 (0.54%)

Mofebutazone n.a

Morniflumate 0 (0.00%) 31 (0.00%)

Nabumetone 8 (0.03%) 1,173 (0.05%)

Naproxen 96 (0.40%) 7,413 (0.34%)

Naproxen and esomeprazole n.a

Niflumic acid 0 (0.00%) 38 (0.00%)

Nimesulide 947 (3.99%) 68,944 (3.19%)

Oxaprozin 13 (0.05%) 1207 (0.06%)

Parecoxib n.a

Phenylbutazone n.a

Prioxicam 376 (1.58%) 28,903 (1.34%)

Proglumetacin 7 (0.03%) 431 (0.02%)

Rofecoxib 412 (1.73%) 29,140 (1.35%)

Sulindac 2 (0.01%) 26 (0.00%)

Tenoxicam 18 (0.08%) 1,655 (0.08%)


Tolfenamic acid n.a

Valdecoxib 12 (0.05%) 617 (0.03%)


n.a.: not available

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S3.4 GERMANY – GePaRD (German Pharmacoepidemiological Research

Database)


(N=7,685)

Controls

(N=683,663)

Current Use of:

Aceclofenac 1 (0.01%) 262 (0.04%)

Acemetacin 14 (0.18%) 818 (0.12%)

Azapropazone n.a.

Celecoxib 20 (0.26%) 1,268 (0.19%)

Dexibuprofen 3 (0.04%) 328 (0.05%)

Dexketoprofen 6 (0.08%) 425 (0.06%)

Diclofenac 692 (9.00%) 48,954 (7.16%)

Diclofenac, combinations 13 (0.17%) 994 (0.15%)

Etodolac n.a.

Etoricoxib 80 (1.04%) 2,733 (0.40%)

Fenbufen n.a.

Fenoprofen n.a.

Fentiazac n.a.

Flurbiprofen n.a.

Ibuprofen 507 (6.60%) 28,337 (4.14%)

Ibuprofen, combinations

Indometacin 21 (0.27%) 996 (0.15%)

Ketoprofen 2 (0.03%) 206 (0.03%)

Ketoprofen, combinations n.a.

Ketorolac n.a.

Lonazolac 0 (0.00%) 5 (0.00%)

Lornoxicam 1 (0.01%) 78 (0.01%)

Lumiracoxib 0 (0.00%) 87 (0.01%)


Mefenamic acid n.a.

Meloxicam 25 (0.33%) 1,249 (0.18%)

Mofebutazone 0 (0.00%) 3 (0.00%)

Morniflumate n.a.

Nabumetone 1 (0.01%) 55 (0.01%)

Naproxen 19 (0.25%) 898 (0.13%)


Niflumic acid n.a.

Nimesulide n.a.

Oxaprozin n.a.

Parecoxib 0 (0.00%) 10 (0.00%)

Phenylbutazone 1 (0.01%) 113 (0.02%)

Prioxicam 25 (0.33%) 1,301 (0.19%)

Proglumetacin 1 (0.01%) 97 (0.01%)

Rofecoxib n.a.

Sulindac n.a.

Tenoxicam n.a.


Tolfenamic acid n.a.

Valdecoxib 0 (0.00%) 62 (0.01%)


n.a.: not available

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S3.5 UNITED KINGDOM – THIN (The Health Improvement Network)


(N=17,702)

Controls

(N=1,437,272)

Current Use of:

Aceclofenac 6 (0.03%) 473 (0.03%)

Acemetacin 2 (0.01%) 130 (0.01%)

Azapropazone 1 (0.01%) 213 (0.01%)

Celecoxib 280 (1.58%) 21,455 (1.49%)

Dexibuprofen 1 (0.01%) 80 (0.01%)

Dexketoprofen 2 (0.01%) 95 (0.01%)

Diclofenac 554 (3.13%) 53,979 (3.76%)


Etodolac 40 (0.23%) 3,578 (0.25%)

Etoricoxib 81 (0.46%) 4,457 (0.31%)

Fenbufen 3 (0.02%) 214 (0.01%)

Fenoprofen 0 (0.00%) 35 (0.00%)

Fentiazac n.a.

Flurbiprofen 2 (0.01%) 353 (0.02%)

Ibuprofen 634 (3.58%) 53,880 (3.75%)

Ibuprofen, combinations 1 (0.01%) 171 (0.01%)

Indometacin 66 (0.37%) 3364 (0.23%)

Ketoprofen 7 (0.04%) 821 (0.06%)

Ketoprofen, combinations 0 (0.00%) 4 (0.00%)

Ketorolac n.a.

Lonazolac n.a.

Lornoxicam 0 (0.00%) 31 (0.00%)

Lumiracoxib n.a.


Mefenamic acid 4 (0.02%) 802 (0.06%)

Meloxicam 176 (0.99%) 14,785 (1.03%)

Mofebutazone n.a.

Morniflumate n.a.

Nabumetone 22 (0.12%) 1,184 (0.08%)

Naproxen 217 (1.23%) 15,035 (1.05%)

Naproxen and esomeprazole 0 (0.00%) 1 (0.00%)

Niflumic acid n.a.

Nimesulide n.a.

Oxaprozin n.a.

Parecoxib n.a.

Phenylbutazone 0 (0.00%) 1 (0.00%)

Prioxicam 24 (0.14%) 1,862 (0.13%)

Proglumetacin n.a.

Rofecoxib 296 (1.67%) 16,816 (1.17%)

Sulindac 0 (0.00%) 150 (0.01%)

Tenoxicam 1 (0.01%) 139 (0.01%)


Tolfenamic acid 0 (0.00%) 21 (0.00%)

Valdecoxib 3 (0.02%) 304 (0.02%)


n.a.: not available

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Confidential: For Review Only12

Table S4. Database-specific adjusted Odds Ratio (ORs), with 95% Confidence Intervals (95% CI), measuring the association between current use

of individual NSAIDs (compared with past use of any NSAID) and risk of a hospitalization for heart failure.

PHARMO (NL) SISR (IT) OSSIFF (IT) GEPARD (GER) THIN (UK)

OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI

Aceclofenac n.a. 1.10 (0.96, 1.25) 0.82 (0.64, 1.06) n.a. n.a.

Acemethacin n.a. n.a. n.a. 1.27 (0.73, 2.20) n.a.

Celecoxib 1.13 (0.84, 1.52) 0.90 (0.83, 0.98) 0.99 (0.89, 1.10) 1.20 (0.76, 1.90) 0.98 (0.87, 1.11)

Diclofenac 1.35 (1.21, 1.50) 1.27 (1.19, 1.35) 1.25 (1.14, 1.36) 1.33 (1.21, 1.45) 0.92 (0.84, 1.01)

Diclofenac, combinations 1.34 (1.12, 1.59) 0.92 (0.72, 1.17) 0.94 (0.71, 1.26) 0.93 (0.53, 1.64) 0.93 (0.80, 1.08)

Etodolac n.a. n.a. n.a. n.a. 0.85 (0.62, 1.17)

Etoricoxib 2.23 (1.73, 2.88) 1.34 (1.21, 1.48) 1.62 (1.40, 1.88) 2.04 (1.61, 2.59) 1.41 (1.13, 1.77)

Ibuprofen 1.36 (1.16, 1.60) 1.20 (1.09, 1.31) 1.23 (1.06, 1.43) 1.46 (1.32, 1.61) 1.01 (0.93, 1.10)

Indomethacin 2.28 (1.58, 3.28) 1.40 (1.13, 1.72) 1.33 (1.01, 1.76) 1.67 (1.07, 2.62) 1.54 (1.20, 1.98)

Ketoprofen n.a. 1.05 (0.96, 1.15) 1.01 (0.89, 1.15) n.a. n.a.

Ketorolac n.a. 1.74 (1.52, 1.98) 1.99 (1.72, 2.30) n.a. n.a.

Meloxicam 1.15 (0.93, 1.42) 1.02 (0.89, 1.17) 1.00 (0.83, 1.20) 1.54 (1.02, 2.32) 0.92 (0.79, 1.07)

Nabumetone 1.46 (0.88, 2.40) n.a. n.a. n.a. 1.50 (0.97, 2.31)

Naproxen 1.39 (1.16, 1.66) 0.90 (0.75, 1.07) 1.15 (0.94, 1.41) 1.51 (0.94, 2.42) 1.23 (1.07, 1.42)

Nimesulide n.a. 1.20 (1.14, 1.26) 1.17 (1.09, 1.25) n.a. n.a.

Piroxicam 1.88 (1.16, 3.04) 1.27 (1.16, 1.39) 1.24 (1.12, 1.38) 1.70 (1.12, 2.57) 1.01 (0.67, 1.52)

Rofecoxib 1.61 (1.31, 1.96) 1.43 (1.29, 1.59) 1.26 (1.13, 1.39) n.a. 1.30 (1.15, 1.46)

Sulindac 1.89 (1.06, 3.38) n.a. n.a. n.a. n.a.

Recent use of any NSAID 0.98 (0.92, 1.04) 0.98 (0.96, 1.01) 0.98 (0.95, 1.01) 1.03 (0.97, 1.09) 1.09 (1.05, 1.14)

Current use of any NSAID 1.41 (1.31, 1.51) 1.19 (1.16, 1.23) 1.22 (1.17, 1.27) 1.41 (1.32, 1.52) 1.02 (0.97, 1.07)

n.a.: not available

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Table S5. Distributions cases and controls along the categories of the current prescribed dose (in

DDD equivalents and corresponding daily amount of active principle in mg) of selected individual

NSAIDs. Data obtained by pooling PHARMO and THIN databases

NSAIDs exposure Daily amount

of active principle

Case patients

(N=25,179)

Controls

(N=2,028,106)

Celecoxib

Low ≤160 mg 25 (9.3%) 1,459 (7.2%)

Medium 161-260 mg 212 (78.8%) 16,735 (82.8%)

High 261-400 mg 7 (2.6%) 403 (2.0%)

Very High ≥401 mg 25 (9.3%) 1,609 (8.0%)

Diclofenac

Low ≤80 mg 59 (8.1%) 3,541 (6.3%)

Medium 81-130 mg 215 (29.3%) 16,904 (30.1%)

High 131-200 mg 408 (55.7%) 34,060 (60.7%)

Very High ≥201 mg 51 (7.0%) 1,613 (2.9%)

Diclofenac, combinations

Low ≤80 mg 16 (5.7 %) 1,032 (5.2%)

Medium 81-130 mg 131 (48.0%) 10,366 (51.8%)

High 131-200 mg 122 (44.7%) 8,493 (42.4%)

Very High ≥201 mg 4 (1.5%) 133 (0.7%)

Etoricoxib

Low ≤48 mg 2 (1.7%) 165 (3.1%)

Medium 49-78 mg 57 (47.5%) 2,646 (49.3%)

High 79-120 mg 40 (33.3%) 1,898 (35.4%)

Very High ≥121 mg 21 (17.5%) 655 (12.2%)

Ibuprofen

Low ≤960 mg 107 (19.6%) 7,659 (17.9%)

Medium 961-1560 mg 383 (70.0%) 28,960 (67.7%)

High 1561-2400 mg 52 (9.5%) 5,901 (13.8%)

Very High ≥2401 mg 5 (0.9%) 250 (0.6%)

Indometacin

Low ≤80 mg 9 (13.4%) 471 (16.0%)

Medium 81-130 mg 35 (52.2%) 1,462 (49.6%)

High 131-200 mg 21 (31.3%) 954 (32.4%)

Very High ≥201 mg 2 (3.0%) 62 (2.1%)

Naproxen

Low ≤400 mg 11 (4.0%) 703 (4.2%)

Medium 401-650 mg 59 (21.7%) 3,677 (21.9%)

High 651-1000 mg 47 (17.3%) 2,707 (16.1%)

Very High ≥1001 mg 155 (57.0%) 9,711 (57.8%)

Piroxicam

Low ≤16 mg 4 (11.1%) 177 (8.1%)

Medium 17-26 mg 28 (77.8%) 1,796 (82.6%)

High 27-40 mg 1 (2.8%) 57 (2.6%)

Very High ≥41 mg 3 (8.3%) 145 (6.7%)

Rofecoxib

Low ≤20 mg 107 (33.8%) 5,963 (36.3%)

Medium 21-33 mg 197 (62.2%) 9,977 (60.7%)

High 34-50 mg 3 (1.0%) 106 (0.7%)

Very High ≥51 mg 10 (3.2%) 381 (2.3%)


The dose currently prescribed dose of each NSAID was categorized as low (≤0.8 daily dose

equivalents), medium (from 0.9 to 1.2), high (from 1.3 to 1.9) and very high dose (≥ 2 defined daily

dose equivalents). Only patients for which the prescribed daily dose of current NSAIDs use was

recorded in the corresponding database of membership were retained in this analysis. Daily amounts

of active principles are based on the DDD/amount conversion factors from www.whocc.no (accessed

December 2015).

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