Bittl John A, He Yulei
From the Munroe Regional Medical Center, Ocala, FL (J.A.B.); and Division of Research and Methodology, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD (Y.H.).
Circ Cardiovasc Qual Outcomes. 2017 Aug;10(8). doi: 10.1161/CIRCOUTCOMES.117.003563.
Bayesian analysis is firmly grounded in the science of probability and has been increasingly supplementing or replacing traditional approaches based on values. In this review, we present gradually more complex examples, along with programming code and data sets, to show how Bayesian analysis takes evidence from randomized clinical trials to update what is already known about specific treatments in cardiovascular medicine. In the example of revascularization choices for diabetic patients who have multivessel coronary artery disease, we combine the results of the FREEDOM trial (Future Revascularization Evaluation in Patients with Diabetes Mellitus: Optimal Management of Multivessel Disease) with prior probability distributions to show how strongly we should believe in the new Class I recommendation ("should be done") for a preference of bypass surgery over percutaneous coronary intervention. In the debate about the duration of dual antiplatelet therapy after drug-eluting stent implantation, we avoid a common pitfall in traditional meta-analysis and create a network of randomized clinical trials to compare outcomes after specific treatment durations. Although we find no credible increase in mortality, we affirm the tradeoff between increased bleeding and reduced myocardial infarctions with prolonged dual antiplatelet therapy, findings that support the new Class IIb recommendation ("may be considered") to extend dual antiplatelet therapy after drug-eluting stent implantation. In the decision between culprit artery-only and multivessel percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction, we use hierarchical meta-analysis to analyze evidence from observational studies and randomized clinical trials and find that the probability of all-cause mortality at longest follow-up is similar after both strategies, a finding that challenges the older ban against noninfarct-artery intervention during primary percutaneous coronary intervention. These examples illustrate how Bayesian analysis integrates new trial information with existing knowledge to reduce uncertainty and change attitudes about treatments in cardiovascular medicine.
贝叶斯分析牢固地建立在概率科学基础之上，并且越来越多地补充或取代基于值的传统方法。在本综述中，我们逐步给出更复杂的示例，并附上编程代码和数据集，以展示贝叶斯分析如何从随机临床试验中获取证据，来更新心血管医学中关于特定治疗方法的已知信息。在患有多支冠状动脉疾病的糖尿病患者的血运重建选择示例中，我们将FREEDOM试验（糖尿病患者未来血运重建评估：多支血管疾病的最佳管理）的结果与先验概率分布相结合，以说明我们应该多强烈地相信关于搭桥手术优于经皮冠状动脉介入治疗的新I类推荐（“应该进行”）。在关于药物洗脱支架植入后双联抗血小板治疗持续时间的争论中，我们避免了传统荟萃分析中一个常见的陷阱，并创建了一个随机临床试验网络，以比较特定治疗持续时间后的结果。尽管我们发现死亡率没有可信的增加，但我们肯定了延长双联抗血小板治疗在增加出血和减少心肌梗死之间的权衡，这些发现支持了关于延长药物洗脱支架植入后双联抗血小板治疗的新IIb类推荐（“可以考虑”）。在ST段抬高型心肌梗死患者仅对罪犯血管进行经皮冠状动脉介入治疗和多支血管经皮冠状动脉介入治疗的决策中，我们使用分层荟萃分析来分析观察性研究和随机临床试验的证据，发现两种策略在最长随访期的全因死亡率概率相似，这一发现挑战了在初级经皮冠状动脉介入治疗期间禁止对非梗死动脉进行干预的旧有禁令。这些示例说明了贝叶斯分析如何将新的试验信息与现有知识相结合，以减少不确定性并改变对心血管医学治疗方法的态度。
