Dataset related to the article "Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques"

This record contains raw data related to the article "Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques"

Background: Whereas transcatheter aortic valve implantation (TAVI) has become the gold standard for aortic valve stenosis treatment in high-risk patients, it has recently been extended to include intermediate risk patients. However, the mortality rate at 5 years is still elevated. The aim of the present study was to develop a novel machine learning (ML) approach able to identify the best predictors of 5-year mortality after TAVI among several clinical and echocardiographic variables, which may improve the long-term prognosis. Methods: We retrospectively enrolled 471 patients undergoing TAVI. More than 80 pre-TAVI variables were collected and analyzed through different feature selection processes, which allowed for the identification of several variables with the highest predictive value of mortality. Different ML models were compared. Results: Multilayer perceptron resulted in the best performance in predicting mortality at 5 years after TAVI, with an area under the curve, positive predictive value, and sensitivity of 0.79, 0.73, and 0.71, respectively. Conclusions: We presented an ML approach for the assessment of risk factors for long-term mortality after TAVI to improve clinical prognosis. Fourteen potential predictors were identified with the organic mitral regurgitation (myxomatous or calcific degeneration of the leaflets and/or annulus) which showed the highest impact on 5 years mortality.

本数据集包含与题为《利用机器学习技术预测经导管主动脉瓣植入术患者长期死亡率》的学术文章相关的原始数据。

背景：经导管主动脉瓣植入术（transcatheter aortic valve implantation, TAVI）现已成为高危患者主动脉瓣狭窄治疗的金标准，近年来其应用范围已拓展至中危患者群体。但该术后5年死亡率仍处于较高水平。本研究旨在开发一种新型机器学习（machine learning, ML）方法，从多项临床与超声心动图变量中筛选出TAVI术后5年死亡率的最优预测因子，以改善患者长期预后。

方法：本研究回顾性纳入471例接受TAVI的患者。收集了80余项术前TAVI相关变量，并通过多种特征选择流程进行分析，最终筛选出若干对死亡率预测价值最高的变量。同时对多种机器学习模型进行了对比评估。

结果：多层感知机（Multilayer perceptron）在预测TAVI术后5年死亡率方面表现最优，其曲线下面积、阳性预测值及灵敏度分别为0.79、0.73与0.71。

结论：本研究提出了一种用于评估TAVI术后长期死亡率危险因素的机器学习方法，以优化临床预后。本研究共筛选出14项潜在预测因子，其中器质性二尖瓣反流（瓣叶及/或瓣环的黏液瘤样或钙化变性）对5年死亡率的影响最为显著。