Bayesian Methods Affirm the Use of Percutaneous Coronary Intervention to Improve Survival in Patients With Unprotected Left Main Coronary Artery Disease

Abstract

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.

Response to Letters Regarding Article, “Bayesian Methods Affirm the Use of Percutaneous Coronary Intervention to Improve Survival in Patients With Unprotected Left Main Coronary Artery Disease”

“…1 He raises a specific concern about the width of the 95% Bayesian credible interval of the odds ratio, ranging from 0.68 to 1.45, and questions whether the approach that we used actually affirms that percutaneous coronary intervention (PCI) should be considered as an alternative to coronary artery bypass (CABG) surgery for selected patients with unprotected left main coronary artery disease, as suggested by the Class IIa recommendation from the American Heart Association Foundation/ American Heart Association. 2 The Bayesian credible intervals in our analysis were similar to the classical confidence intervals and included the value 1.0, suggesting that the 1-year mortality after PCI or CABG was similar.…”

“…The seemingly wide Bayesian credible intervals were the consequences of sample size. To illustrate this, we note from the data that the 1-year mortality rate after CABG was 6.56% 1 and estimate that a sample size of 90 000 per treatment arm would be required to detect a relative mortality difference of 5% in the PCI arm (alpha = 0.05, power = 0.80). If the relative difference in mortality between CABG and PCI were 10%, we would still need more >20 000 patients in each arm.…”

Bayesian Methods for Evidence Evaluation

“…It is helpful to use the thoughtful and thorough Bayesian exercise of Bittl et al 3 to reflect on what Bayesian approaches give us, and what they do not.…”

“…The latter is what Bittl et al do in their Bayesian metaanalysis of the evidence supporting the relative benefits of treatments for unprotected left main coronary artery disease. 3 Although the notion of epistemic probability might seem far afield from practice guidelines, in fact, such guidelines have their own language of epistemic uncertainty. Instead of stating that the proposition that PCI is comparable to CABG, has, say, an 80% chance of being true, the level of evidence for this statement is classified as B.…”

“…Less obvious, but no less important, is the value of such models for modeling the effects of different qualitative judgments and allowing guideline panels to navigate more adeptly in the world of intermediate certainty. This analysis by Bittl et al 3 can serve as a nice starting point for future Bayesian evidence analyses, whose value may be more apparent when the outcomes of the summaries are less predictable, qualitative disagreements among experts about the evidence are more difficult to reconcile, and the full range of Bayesian measures is used.…”