Research Article: Risk of acute myocardial infarction during use of individual NSAIDs: A nested case-control study from the SOS project

Date Published: November 1, 2018

Publisher: Public Library of Science

Author(s): Gwen M. C. Masclee, Huub Straatman, Andrea Arfè, Jordi Castellsague, Edeltraut Garbe, Ron Herings, Bianca Kollhorst, Silvia Lucchi, Susana Perez-Gutthann, Silvana Romio, René Schade, Tania Schink, Martijn J. Schuemie, Lorenza Scotti, Cristina Varas-Lorenzo, Vera E. Valkhoff, Marco Villa, Miriam C. J. M. Sturkenboom, Emilio Russo.


Use of selective COX-2 non-steroidal anti-inflammatory drugs (NSAIDs) (coxibs) has been associated with an increased risk of acute myocardial infarction (AMI). However, the risk of AMI has only been studied for very few NSAIDs that are frequently used.

To estimate the risk of AMI for individual NSAIDs.

A nested case-control study was performed from a cohort of new NSAID users ≥18 years (1999–2011) matching cases to a maximum of 100 controls on database, sex, age, and calendar time. Data were retrieved from six healthcare databases. Adjusted odds ratios (ORs) of current use of individual NSAIDs compared to past use were estimated per database. Pooling was done by two-stage pooling using a random effects model (ORmeta) and by one-stage pooling (ORpool).

Among 8.5 million new NSAID users, 79,553 AMI cases were identified. The risk was elevated for current use of ketorolac (ORmeta 2.06;95%CI 1.83–2.32, ORpool 1.80; 1.49–2.18) followed, in descending order of point estimate, by indometacin, etoricoxib, rofecoxib, diclofenac, fixed combination of diclofenac with misoprostol, piroxicam, ibuprofen, naproxen, celecoxib, meloxicam, nimesulide and ketoprofen (ORmeta 1.12; 1.03–1.22, ORpool 1.00;0.86–1.16). Higher doses showed higher risk estimates than lower doses.

The relative risk estimates of AMI differed slightly between 28 individual NSAIDs. The relative risk was highest for ketorolac and was correlated with COX-2 potency, but not restricted to coxibs.

Partial Text

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used to reduce inflammation and provide pain relief. They act via reversible, competitive inhibition of cyclo-oxygenase (COX) enzymes. As inhibition of the COX-1 enzyme decreases prostaglandins production, gastrointestinal adverse events including ulcerations and bleeding occur often during NSAID use. This led to development of selective COX-2 inhibitors (coxibs) which after successful market introduction [1,2] were considered with cardiovascular safety resulting in the voluntary withdrawal of rofecoxib in 2004.[3] The underlying mechanism of increased cardiovascular events may be related to a dysbalance in COX-1 and COX-2 inhibition properties favoring thrombosis by vasoconstriction and platelet aggregation.[4,5]

The study cohort comprised 8,535,952 new NSAID users (S1 Fig), of whom 101,227 patients developed an AMI after cohort entry. Of these, 79,553 (78.6%) cases could be matched to at least one control. Baseline characteristics of cases and matched controls are shown in Table 1 and exposure in Table 2. If persons used multiple NSAIDs each individual NSAID was considered; 93–97% of current users used only one NSAID. Cases had more often risk factors for AMI such as a prior history of ischemic heart disease, other cardiovascular diseases or use of cardiovascular drugs, than controls but the prevalence of these risk factors did not differ between different NSAID users (S6 Table).

In this unique multinational case-control study nested in a new user NSAID cohort of more than 8.5 million persons, we assessed the association with AMI for 28 individual NSAIDs. Following rofecoxib’s withdrawal many single studies have been conducted using different protocols and definitions.[4,11,37–39] Meta-analyses of these studies identified large variability between studies and several methodological issues such as lack of information on dose effects, and lack of estimates for less frequently used NSAIDs.[12,14,40] The SOS study is a major leap forward from those regular meta-analyses of different studies because it combines patient-level data that have been collected using a common data model, protocol, definitions, data transformation and analysis.




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