Bayesian Methods affirm the use of percutaneous coronary intervention to improve survival in patients with unprotected left main coronary artery disease

Background: Several randomized clinical trials support the use of coronary artery bypass grafting (CABG) for patients with unprotected left main coronary artery disease. Studies suggesting the equivalence of percutaneous coronary intervention (PCI) with CABG for this indication indirectly support the 2011 American College of Cardiology Foundation/American Heart Association Class IIa recommendation for PCI to improve survival in patients with unprotected left main coronary artery disease. We tested whether bayesian approaches uphold the new recommendation. Methods and Results: We performed a bayesian cross-design and network meta-analysis of 12 studies (4 randomized clinical trials and 8 observational studies) comparing CABG with PCI (n=4574 patients) and of 7 studies (2 randomized clinical trials and 5 observational studies) comparing CABG with medical therapy (n=3224 patients). The odds ratios of 1-year mortality after PCI compared with CABG using bayesian cross-design meta-analysis were not different among randomized clinical trials (odds ratio, 0.99; 95% bayesian credible interval, 0.67-1.43), matched cohort studies (odds ratio, 1.10; 95% bayesian credible interval, 0.76-1.73), and other types of cohort studies (odds ratio, 0.93; 95% bayesian credible interval, 0.58-1.35). A network meta-analysis suggested that medical therapy is associated with higher 1-year mortality than the use of PCI for patients with unprotected left main coronary artery disease (odds ratio, 3.22; 95% bayesian credible interval, 1.96-5.30). Conclusions: Bayesian methods support the current guidelines, which were based on traditional statistical methods and have proposed that PCI, like CABG, improves survival for patients with unprotected left main coronary artery disease compared with medical therapy. An integrated approach using both direct and indirect evidence may yield new insights to enhance the translation of clinical trial data into practice.