COVID-19 Vaccination and Cardiovascular Events: A Systematic Review and Bayesian Multivariate Meta-Analysis of Preventive Benefits and Risks

Abstract

BACKGROUND: To provide a detailed understanding and apply a comprehensive strategy, this study examines the association between COVID-19 vaccination and cardiovascular events. We conducted a Bayesian multivariate meta-analysis using summary data across multiple outcomes including myocardial infarction, stroke, arrhythmia, and CAD, considering potential dependencies in the data. Markov chain Monte Carlo (MCMC) methods were detected for easy implementation of the Bayesian approach. Also, the sensitivity analysis of the model was done by using different priors. METHODS: Fifteen studies were included in the systematic review, with eleven studies comparing the results between the vaccine group and the unvaccinated group. Additionally, six studies were used for further analysis to compare mRNA COVID-19 Vaccines (Pfizer-BioNTech and Moderna). RESULTS: Bayesian meta-analysis revealed a link between vaccines and CAD risk (OR, 1.70; 95 CrI: 1.11-2.57), particularly after BNT162b2 (OR, 1.64; 95 CrI: 1.06-2.55) and second dose (OR, 3.44; 95 CrI: 1.99-5.98). No increased risk of heart attack, arrhythmia, or stroke was observed post-COVID-19 vaccination. As the only noteworthy point, a protective effect on stroke (OR, 0.19; 95 CrI: 0.10-0.39) and myocardial infarction (OR, 0.003; 95 CrI: 0.001-0.006) was observed after the third dose of the vaccine. CONCLUSIONS: Secondary analysis showed no notable disparity in cardiovascular outcomes between BNT162b2 and mRNA vaccines. The association of COVID-19 vaccination with the risk of coronary artery disease should be considered in future vaccine technologies for the next pandemic.