Construction of Predictive Models for Cardiovascular Mortality by Machine Learning Approaches in Patients Who Underwent Transcatheter Aortic Valve Implantation.

Circulation reports Pub Date : 2025-03-04 eCollection Date: 2025-04-10 DOI:10.1253/circrep.CR-24-0182
Shunsaku Otomo, Itaru Hosaka, Marenao Tanaka, Naoto Murakami, Nobuaki Kokubu, Atsuko Muranaka, Ryo Nishikawa, Naoki Hachiro, Ryota Kawamura, Jun Nakata, Nobutaka Nagano, Yukinori Akiyama, Tatsuya Sato, Yutaka Iba, Toshiyuki Yano, Nobuyoshi Kawaharada, Masato Furuhashi
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Abstract

Background: Prognostic models for cardiovascular death, but not all-cause death, after transcatheter aortic valve implantation (TAVI) have not been established yet.

Methods and results: In 252 patients with aortic stenosis (AS) who underwent TAVI (men/women 83/169; mean age 85 years), we explored predictive models by machine learning for cardiovascular death using 62 candidates. During the follow-up period (mean 1,135 days), 13 (5.2%) patients died of cardiovascular disease. The least absolute shrinkage and selection operator (LASSO) feature selection identified 8 features as important candidates, including old myocardial infarction, triglycerides/high-density lipoprotein cholesterol (TG/HDL-C) ratio, Society of Thoracic Surgeons predicted risk of mortality score (STS-PROM), pulse rate, left atrium volume index, stroke volume index, estimated glomerular filtration rate, and albumin. Cox regression analyses with adjustment for age and sex showed that old myocardial infarction, high levels of TG/HDL-C, STS-PROM, and pulse rate, as well as low levels of glomerular filtration rate and albumin, were independent risk factors for cardiovascular death. Models of logistic regression (LR) and random survival forest (RSF) using the LASSO-selected features, except for STS-PROM, significantly improved predictive abilities for cardiovascular death compared with LR analysis using STS-PROM alone.

Conclusions: Machine learning models of prediction for cardiovascular death of LR and RSF using the LASSO-selected features are superior to a LR model using STS-PROM alone in patients with severe AS who underwent TAVI.

应用机器学习方法构建经导管主动脉瓣植入术患者心血管死亡率预测模型。
背景:经导管主动脉瓣植入术(TAVI)后心血管死亡(而非全因死亡)的预后模型尚未建立。方法与结果:252例主动脉瓣狭窄(AS)患者行TAVI(男/女83/169;平均年龄85岁),我们使用62名候选人通过机器学习探索了心血管死亡的预测模型。在随访期间(平均1135天),13例(5.2%)患者死于心血管疾病。最小绝对收缩和选择操作(LASSO)特征选择确定了8个重要的候选特征,包括老年性心肌梗死、甘油三酯/高密度脂蛋白胆固醇(TG/HDL-C)比率、胸外科学会预测死亡风险评分(STS-PROM)、脉搏率、左心房容积指数、卒中容积指数、估计肾小球滤过率和白蛋白。校正年龄和性别的Cox回归分析显示,老年性心肌梗死、高水平的TG/HDL-C、STS-PROM和脉搏率,以及低水平的肾小球滤过率和白蛋白是心血管死亡的独立危险因素。除STS-PROM外,使用lasso选择特征的逻辑回归(LR)和随机生存森林(RSF)模型与单独使用STS-PROM的LR分析相比,显著提高了心血管死亡的预测能力。结论:在接受TAVI的严重AS患者中,使用lasso选择的特征预测LR和RSF心血管死亡的机器学习模型优于单独使用STS-PROM的LR模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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