Cardiovascular safety of celecoxib in acute myocardial infarction patients: a nested case-control study

The objective was to measure the impact of exposure to coxibs and non-steroidal antiinflammatory drugs (NSAID) on morbidity and mortality in older patients with acute myocardial infarction (AMI). A nested case-control study was carried out using an exhaustive population-based cohort of patients aged 66 years and older living in Quebec (Canada) who survived a hospitalization for AMI (ICD-9 410) between 1999 and 2002. The main variables were all-cause and cardiovascular (CV) death, subsequent hospital admission for AMI, and a composite end-point including recurrent AMI or CV death. Conditional logistic regressions were used to estimate the risk of mortality and morbidity. A total of 19,823 patients aged 66 years and older survived hospitalization for AMI in the province of Quebec between 1999 and 2002. After controlling for covariables, the risk of subsequent AMI and the risk of composite end-point were increased by the use of rofecoxib. The risk of subsequent AMI was particularly high for new rofecoxib users (HR 2.47, 95% CI 1.57–3.89). No increased risk was observed for celecoxib users. No increased risk of CV death was observed for patients exposed to coxibs or NSAIDs. Patients newly exposed to NSAIDs were at an increased risk of death (HR 2.22, 95% CI 1.30–3.77) and of composite end-point (HR 2.28, 95% CI 1.35–3.84). Users of rofecoxib and NSAIDs, but not celecoxib, were at an increased risk of recurrent AMI and of composite end-point. Surprisingly, no increased risk of CV death was observed. Further studies are needed to better understand these apparently contradictory results

To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies

Covariate adjustment is integral to the validity of observational studies assessing causal effects. It is common practice to adjust for as many variables as possible in observational studies in the hopes of reducing confounding by other variables. However, indiscriminate adjustment for variables using standard regression models may actually lead to biased estimates. In this paper, we differentiate between confounders, mediators, colliders, and effect modifiers. We will discuss that while confounders should be adjusted for in the analysis, one should be wary of adjusting for colliders. Mediators should not be adjusted for when examining the total effect of an exposure on an outcome. Automated statistical programs should not be used to decide which variables to include in causal models. Using a case scenario in cardiology, we will demonstrate how to identify confounders, colliders, mediators and effect modifiers and the implications of adjustment or non-adjustment for each of them. © 2021 Elsevier Inc