Data_Sheet_1_Sex-Specific Patterns of Mortality Predictors Among Patients Undergoing Cardiac Resynchronization Therapy: A Machine Learning Approach.docx

Background: The relative importance of variables explaining sex-related differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in CRT patients. We also aimed to assess the sex-specific differences in predictors of mortality utilizing ML.Methods: Using a retrospective registry of 2,191 CRT patients, ML models were implemented in 6 partially overlapping patient subsets (all patients, females, or males with 1- or 3-year follow-up). Each cohort was randomly split into training (80%) and test sets (20%). After hyperparameter tuning in the training sets, the best performing algorithm was evaluated in the test sets. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC). The most important predictors were identified using the permutation feature importances method.Results: Conditional inference random forest exhibited the best performance with AUCs of 0.728 (0.645–0.802) and 0.732 (0.681–0.784) for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction, and QRS morphology had higher predictive power, whereas hemoglobin was less important in females compared to males. The importance of atrial fibrillation and age increased, while the importance of serum creatinine decreased from 1- to 3-year follow-up in both sexes.Conclusions: Using ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in CRT patients. Sex-specific patterns of predictors were identified, showing a dynamic variation over time.

背景：在实施心脏再同步治疗（CRT）的患者中，解释性别相关差异的变量相对重要性鲜有深入研究。本研究旨在实施并评估机器学习（ML）算法，以预测CRT患者1年和3年的全因死亡率。此外，我们还旨在利用ML技术评估死亡率预测因子的性别特异性差异。方法：利用2,191名CRT患者的回顾性登记数据，我们在6个部分重叠的患者子集（所有患者、女性或男性，具有1年或3年随访）中实施了ML模型。每个队列随机分为训练集（80%）和测试集（20%）。在训练集中进行超参数调整后，在测试集中评估了表现最佳算法。使用受试者工作特征曲线下面积（AUC）量化模型的判别能力。利用排列特征重要性方法确定了最重要的预测因子。结果：条件推断随机森林在预测1年和3年死亡率方面表现出最佳性能，AUC分别为0.728（0.645–0.802）和0.732（0.681–0.784）。心衰病因、纽约心脏协会（NYHA）分级、左心室射血分数和QRS形态具有较高的预测能力，而与男性相比，血红蛋白在女性中的重要性较低。心房颤动和年龄的重要性增加，而血清肌酐的重要性在1年至3年随访期间在两性中均有所降低。结论：结合易于获取的临床特征和机器学习技术，我们的模型有效地预测了CRT患者1年和3年的全因死亡率。识别出预测因子的性别特异性模式，显示出随时间动态变化的特点。