Association of clinical trial participation after myocardial infarction with socioeconomic status, clinical characteristics, and outcomes. Issue 2 (11th August 2021) - Ohm, Joel; Jernberg, Tomas; Johansson, David; Warnqvist, Anna; Leosdottir, Margrét; Hambraeus, Kristina; Svensson, Per
Abstract: Aims: To investigate whether participants in clinical trials after myocardial infarction (MI) are representable for the post-MI population concerning characteristics, secondary prevention, and prognosis. Methods and results: Cohort study on 31 792 attendants to 1-year revisits after MI throughout Sweden ( n = 2941 clinical trial participants) between 2008 and 2013 identified in the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART). Individual-level data on socioeconomic status (SES) (disposable income, educational level, and marital status) and outcomes (first recurrent non-fatal MI, coronary heart disease death, fatal or non-fatal stroke until study end 2018) were linked from other national registries. Trial participants were more likely to be men [risk ratio 1.09; 95% confidence interval (CI) 1.07–1.11], and married (1.07; 1.04–1.10), have a highest-quintile income (1.42; 1.36–1.48), and post-secondary education (1.25; 1.18–1.33), while less likely to have a history of MI (0.88; 0.80–0.97), be persistent smokers (0.83; 0.75–0.92) and have left ventricular dysfunction (0.59; 0.44–0.79) compared to non-participants. During a mean 6.7-year follow-up, 5206 outcome events occurred. Risk was lower in trial participants (hazard ratio 0.80; 95% CI 0.72–0.89), also after adjusting for clinical characteristics and post-MI therapies (0.85; 0.77–0.94) and additionally for SES (0.88; 0.79–0.97). Conclusions: Clinical trial participants post-MI are more often male, have higher SES, a more advantageous risk profile, and better prognosis. Additional unmeasured participation bias was implied. Questionable external validity of post-MI trials highlights the importance of complementary studies using real-world data. Graphical Abstract: