Journal of Zhejiang University

Journal of Zhejiang University SCIENCE B 1998 Vol.-1 No.-1 P.

Machine learning models for predicting thirty-day mortality following TAVR: a national study from the NTCVR cohort

Author(s): Qifeng ZHU^1,^2,^3,^4, Jin LU^1,^2,^3, Jiayuan LI^1,^2,³, Feiyu WU^1,^2,³, Danqing YU1,5, Yihan PAN¹, Qijing ZHOU⁶, Chongzhou ZHENG⁷, Daxin ZHOU^8,⁹, Wenzhi PAN^8,⁹, Xianbao LIU^1,^2,^3,⁴, Jian'an WANG^1,^2,^3,⁴
Affiliation(s): 1. ¹Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China ²State Key Laboratory of Transvascular Implantation Devices, Hangzhou 310009, China ³Heart Regeneration and Repair Key Laboratory of Zhejiang Province, Hangzhou 310009, China ⁴Transvascular Implantation Devices Research Institute, Hangzhou 310053, China ⁵Department of Cardiology, Cixi Integrated Traditional Chinese and Western Medicine Medical and Health Group, Cixi 315300, China ⁶Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China ⁷Department of Structural Heart Disease, Cardiovascular Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China ⁸Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China ⁹National Clinical Research Center for Interventional Medicine, Shanghai 200032, China
Corresponding email(s): Jian'an WANG, email: wangjianan111@zju.edu.cn, Xianbao LIU, email:liuxb@zju.edu.cn
Key Words: Machine Learning, Transcatheter aortic-valve replacement (TAVR), Mortality, Predictive model

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Qifeng ZHU^1,^2,^3,^4*, Jin LU^1,^2,^3*, Jiayuan LI^1,^2,³, Feiyu WU^1,^2,³, Danqing YU1,5, Yihan PAN¹, Qijing ZHOU⁶, Chongzhou ZHENG⁷, Daxin ZHOU^8,⁹, Wenzhi PAN^8,⁹, Xianbao LIU^1,^2,^3,⁴, Jian'an WANG^1,^2,^3,⁴. Machine learning models for predicting thirty-day mortality following TAVR: a national study from the NTCVR cohort[J]. Journal of Zhejiang University Science B, 1998, -1(-1): .

@article{title="Machine learning models for predicting thirty-day mortality following TAVR: a national study from the NTCVR cohort",
author="Qifeng ZHU^1,^2,^3,^4*, Jin LU^1,^2,^3*, Jiayuan LI^1,^2,³, Feiyu WU^1,^2,³, Danqing YU1,5, Yihan PAN¹, Qijing ZHOU⁶, Chongzhou ZHENG⁷, Daxin ZHOU^8,⁹, Wenzhi PAN^8,⁹, Xianbao LIU^1,^2,^3,⁴, Jian'an WANG^1,^2,^3,⁴",
journal="Journal of Zhejiang University Science B",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2500804"
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Background: transcatheter aortic-valve replacement (TAVR) has emerged as the preferred treatment for patients with aortic-valve stenosis (AS). However, risk assessment tools tailored to this patient group remain insufficient. Aims: We aimed to develop a machine-learning-based model for predicting thirty-day mortality risk in TAVR patients, using a national cohort from China. Methods: This multicenter, registry-based study (NTCVR) included 10,799 patients undergoing TAVR at 147 Chinese tertiary hospitals (November 2011 - August 2024). Patients were split into three sets: training (60%), internal validation (20%), and external validation (20%, two separate provinces). Oversampling addressed class imbalance (30-day mortality rate: 2.9%). Extensive feature selection and model development employed 15 feature subsets and 15 ML learners, generating 1,125 candidate models. SHapley Additive exPlanations (SHAP) analysis was used to further assess the influence of selected predictors and machine-learning models. Results: The optimal model combined Double Input Symmetrical Relevance (DISR) feature selection with a Support Vector Machine (SVM) learner. The Area Under the ROC Curve (AUC) of the optimal machine Learning model was significantly higher than that of the Society of Thoracic Surgeons (STS) score in both the internal validation cohort [0.74 (0.67, 0.81) vs. 0.60 (0.53, 0.67), p < 0.05] and the external validation cohort [0.69 (0.62, 0.76) vs. 0.57 (0.51, 0.63), p < 0.05]. SHapley Additive exPlanations (SHAP) analysis of the variables included in the optimal model highlighted the contributions to mortality prediction of baseline alanine aminotransferase, creatinine, NYHA class, need for circulatory support, and non-elective procedure status. Conclusions: A simple and effective machine-learning-based model was developed for predicting thirty-day mortality in TAVR patients, offering a valuable tool for risk stratification in China.

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