Otomo Shunsaku, Hosaka Itaru, Tanaka Marenao, Murakami Naoto, Kokubu Nobuaki, Muranaka Atsuko, Nishikawa Ryo, Hachiro Naoki, Kawamura Ryota, Nakata Jun, Nagano Nobutaka, Akiyama Yukinori, Sato Tatsuya, Iba Yutaka, Yano Toshiyuki, Kawaharada Nobuyoshi, Furuhashi Masato
Department of Cardiovascular, Renal and Metabolic Medicine, Sapporo Medical University School of Medicine Sapporo Japan.
Department of Cardiovascular Surgery, Sapporo Medical University School of Medicine Sapporo Japan.
Circ Rep. 2025 Mar 4;7(4):293-302. doi: 10.1253/circrep.CR-24-0182. eCollection 2025 Apr 10.
Prognostic models for cardiovascular death, but not all-cause death, after transcatheter aortic valve implantation (TAVI) have not been established yet.
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.
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.
经导管主动脉瓣植入术（TAVI）后心血管死亡的预后模型已经建立，但全因死亡的预后模型尚未建立。
在252例接受TAVI的主动脉瓣狭窄（AS）患者中（男性/女性83/169；平均年龄85岁），我们使用62个候选指标通过机器学习探索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算子（LASSO）特征选择确定了8个特征为重要候选指标，包括陈旧性心肌梗死、甘油三酯/高密度脂蛋白胆固醇（TG/HDL-C）比值、胸外科医师协会预测的死亡风险评分（STS-PROM）、脉搏率、左心房容积指数、每搏输出量指数、估计肾小球滤过率和白蛋白。对年龄和性别进行调整的Cox回归分析表明，陈旧性心肌梗死、高TG/HDL-C水平、STS-PROM和脉搏率，以及低肾小球滤过率和白蛋白水平是心血管死亡的独立危险因素。使用LASSO选择的特征（除STS-PROM外）建立的逻辑回归（LR）和随机生存森林（RSF）模型，与仅使用STS-PROM的LR分析相比，显著提高了心血管死亡的预测能力。
在接受TAVI的严重AS患者中，使用LASSO选择的特征建立的LR和RSF心血管死亡预测机器学习模型优于仅使用STS-PROM的LR模型。
