Meta-Analysis of Nonrandomized Studies to Assess the Optimal Timing of Coronary Artery Bypass Grafting After Acute Myocardial Infarction

Am J Cardiol. 2022 Feb 1:164:44-51. doi: 10.1016/j.amjcard.2021.10.033. Epub 2021 Nov 20.

Abstract

The optimal timing of coronary artery bypass grafting (CABG) in patients after an acute myocardial infarction (MI) is unknown. We performed a systematic review and meta-analysis of studies comparing mortality rates in patients who underwent CABG at different time intervals after acute MI. Bias assessments were completed for each study, and summary of proportions of all-cause mortality were calculated based on CABG at various time intervals after MI. A total of 22 retrospective studies, which included a total of 137,373 patients were identified. The average proportion of patients who died when CABG was performed within 6 hours of MI was 12.7%, within 6 to 24 hours of MI was 10.9%, within 1 day of MI was 9.8%, any time after 1 day of MI was 3.0%, within 7 days of MI was 5.9%, and any time after 7 days of MI was 2.7%. Interstudy heterogeneity, assessed using I2 values, showed significant heterogeneity in death rates within subgroups. Only 1 study accounted for immortal time bias, and there was a serious risk of selection bias in all other studies. Confounding was found to be a serious risk for bias in 55% of studies because of a lack of accounting for type of MI, MI severity, or other verified cardiac risk factors. The current publications comparing timing of CABG after MI is at serious risk of bias because of patient selection and confounding, with heterogeneity in both study populations and intervention time intervals.

Publication types

  • Meta-Analysis
  • Systematic Review

MeSH terms

  • Bias
  • Cause of Death
  • Confounding Factors, Epidemiologic
  • Coronary Artery Bypass / methods*
  • Hospital Mortality
  • Humans
  • Mortality*
  • Myocardial Infarction / surgery*
  • Non-Randomized Controlled Trials as Topic
  • Retrospective Studies
  • Time Factors
  • Time-to-Treatment / statistics & numerical data*