Department of Cardiology, University of Galway, Galway, Ireland.
Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
J Am Coll Cardiol. 2023 Nov 28;82(22):2113-2124. doi: 10.1016/j.jacc.2023.09.818.
In patients with 3-vessel coronary artery disease (CAD) and/or left main CAD, individual risk prediction plays a key role in deciding between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG).
The aim of this study was to assess whether these individualized revascularization decisions can be improved by applying machine learning (ML) algorithms and integrating clinical, biological, and anatomical factors.
In the SYNTAX (Synergy between PCI with Taxus and Cardiac Surgery) study, ML algorithms (Lasso regression, gradient boosting) were used to develop a prognostic index for 5-year death, which was combined, in the second stage, with assigned treatment (PCI or CABG) and prespecified effect-modifiers: disease type (3-vessel or left main CAD) and anatomical SYNTAX score. The model's discriminative ability to predict the risk of 5-year death and treatment benefit between PCI and CABG was cross-validated in the SYNTAX trial (n = 1,800) and externally validated in the CREDO-Kyoto (Coronary REvascularization Demonstrating Outcome Study in Kyoto) registry (n = 7,362), and then compared with the original SYNTAX score II 2020 (SSII-2020).
The hybrid gradient boosting model performed best for predicting 5-year all-cause death with C-indexes of 0.78 (95% CI: 0.75-0.81) in cross-validation and 0.77 (95% CI: 0.76-0.79) in external validation. The ML models discriminated 5-year mortality better than the SSII-2020 in the external validation cohort and identified heterogeneity in the treatment benefit of CABG vs PCI.
An ML-based approach for identifying individuals who benefit from CABG or PCI is feasible and effective. Implementation of this model in health care systems-trained to collect large numbers of parameters-may harmonize decision making globally. (Synergy Between PCI With TAXUS and Cardiac Surgery: SYNTAX Extended Survival [SYNTAXES]; NCT03417050; SYNTAX Study: TAXUS Drug-Eluting Stent Versus Coronary Artery Bypass Surgery for the Treatment of Narrowed Arteries; NCT00114972).
在三支血管病变(CAD)和/或左主干 CAD 患者中,个体风险预测在经皮冠状动脉介入治疗(PCI)和冠状动脉旁路移植术(CABG)之间的决策中起着关键作用。
本研究旨在评估通过应用机器学习(ML)算法并整合临床、生物学和解剖学因素是否可以改善这些个体化血运重建决策。
在 SYNTAX(紫杉醇与心脏手术的协同作用)研究中,ML 算法(Lasso 回归、梯度提升)用于开发 5 年死亡率的预后指数,然后在第二阶段与指定的治疗(PCI 或 CABG)和预设的效应修饰剂结合:疾病类型(三支血管病变或左主干 CAD)和解剖学 SYNTAX 评分。该模型在 SYNTAX 试验(n=1800)中对预测 5 年死亡风险和 PCI 与 CABG 之间的治疗获益的区分能力进行了交叉验证,并在 CREDO-Kyoto(京都冠状动脉血运重建结果研究)注册中心(n=7362)进行了外部验证,然后与原始 SYNTAX 评分 II 2020(SSII-2020)进行了比较。
在交叉验证中,混合梯度提升模型在预测 5 年全因死亡率方面表现最佳,C 指数为 0.78(95%CI:0.75-0.81),外部验证中为 0.77(95%CI:0.76-0.79)。在外部验证队列中,ML 模型在区分 5 年死亡率方面优于 SSII-2020,并确定了 CABG 与 PCI 治疗获益的异质性。
一种基于机器学习的方法来确定哪些患者从 CABG 或 PCI 中获益是可行且有效的。在接受过收集大量参数培训的医疗保健系统中实施该模型可能会使全球的决策趋于一致。(紫杉醇与心脏手术的协同作用:SYNTAX 扩展生存[SYNTAXES];NCT03417050;SYNTAX 研究:紫杉醇洗脱支架与冠状动脉旁路移植术治疗狭窄动脉;NCT00114972)。