系统筛选和心脏团队方法有助于揭示复杂冠状动脉疾病血管重建候选者的新风险因素:一种机器学习方法。

IF 3.8 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Shigetaka Kageyama, Kai Ninomiya, Szymon Jonik, Shinichiro Masuda, Pruthvi C Revaiah, Tsung-Ying Tsai, Scot Garg, Yoshinobu Onuma, Patrick W Serruys, Tomasz Mazurek
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引用次数: 0

摘要

导言:由于纳入/排除标准的限制,影响左主干(LM)和/或三血管(3V)冠状动脉疾病(CAD)患者经皮或手术血运重建后死亡率的基线特征在真实世界实践和随机对照试验(RCT)中有所不同:本研究旨在评估系统性筛查是否能识别影响长期死亡率的新的和登记处特异性基线特征:采用 LASSO(最小绝对缩减和选择操作器)回归筛选 SYNTAX 试验和波兰单中心登记的 1035 例连续接受血管重建并随访 5 年的复杂 CAD 患者的 42 个共同基线特征。筛选后,进行了经典的 Cox 回归分析,以检验预测 5 年死亡率的线性模型是否合适,然后将其与使用 SYNTAX 评分 2020(SS2020)预测的同一队列中的死亡率进行比较:结果:登记的五年死亡率为 12.3%,其中肺动脉高压、慢性阻塞性肺病和胰岛素依赖型糖尿病是最强的预测因素。在内部验证中,与 SS2020 相比,LASSO 筛选后构建的线性模型结合经典 Cox 回归分析提高了 5 年死亡率的预测能力(c 指数分别为 0.92 和 0.75):机器学习提高了对所有适合手术或经皮血运重建的患者的注册特异性风险因素的检测能力,这些患者都经过了心脏小组的讨论。从 RCT 中发现的风险因素并不一定与在实际临床实践中进行系统筛查时发现的风险因素相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systematic screening by a heart team and a machine learning approach contribute to unraveling novel risk factors in revascularization candidates with complex coronary artery disease.

Introduction: The baseline characteristics affecting mortality following percutaneous or surgical revascularization in patients with left main and / or 3‑vessel coronary artery disease (CAD) observed in real‑world practice differ from those established in randomized controlled trials (RCTs) due to the constraints of inclusion / exclusion criteria.

Objectives: This study aimed to assess whether systematic screening enables identification of novel and registry‑specific baseline patient characteristics influencing long‑term mortality.

Patient and methods: Least absolute shrinkage and selection operator (LASSO) regression was used to screen 42 baseline patient characteristics shared by the SYNTAX (Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery) trial and a single‑center Polish registry of 1035 consecutive patients with complex CAD who received revascularization and were followed-up for 5 years. After screening, a classic Cox regression analysis was performed to examine the suitability of a linear model for predicting 5‑year mortality, which was then compared with the mortality predicted in the same cohort using the SYNTAX score II 2020 (SS2020).

Results: The 5‑year mortality rate in the registry was 12.3%, and the strongest predictors were pulmonary hypertension, chronic obstructive pulmonary disease, and insulin‑dependent diabetes. In an internal validation, the linear model constructed after LASSO screening and combined with a classic Cox regression analysis improved the prediction of 5‑year mortality, as compared with the SS2020 (concordance index of 0.92 and 0.75, respectively).

Conclusions: A machine learning approach improved the detection of registry‑specific risk factors in all‑comer patients amenable to surgical or percutaneous revascularization who were evaluated by a heart team. The risk factors identified in RCTs are not necessarily the same as those detected in real clinical practice when systematic screening is applied.

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来源期刊
CiteScore
4.50
自引率
0.00%
发文量
176
审稿时长
6-12 weeks
期刊介绍: Polish Archives of Internal Medicine is an international, peer-reviewed periodical issued monthly in English as an official journal of the Polish Society of Internal Medicine. The journal is designed to publish articles related to all aspects of internal medicine, both clinical and basic science, provided they have practical implications. Polish Archives of Internal Medicine appears monthly in both print and online versions.
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