Abstract Aims Our aim was to develop a machine learning (ML)-based risk stratification system to predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients undergoing cardiac resynchronization therapy (CRT). Methods and results Multiple ML models were trained on a retrospective database of 1510 patients undergoing CRT implantation to predict 1- to 5-year all-cause mortality. Thirty-three pre-implant clinical features were selected to train the models. The best performing model [SEMMELWEIS-CRT score (perSonalizEd assessMent of estiMatEd risk of mortaLity With machinE learnIng in patientS undergoing CRT implantation)], along with pre-existing scores (Seattle Heart Failure Model, VALID-CRT, EAARN, ScREEN, and CRT-score), was tested on an independent cohort of 158 patients. There were 805 (53%) deaths in the training cohort and 80 (51%) deaths in the test cohort during the 5-year follow-up period. Among the trained classifiers, random forest demonstrated the best performance. For the prediction of 1-, 2-, 3-, 4-, and 5-year mortality, the areas under the receiver operating characteristic curves of the SEMMELWEIS-CRT score were 0.768 (95% CI: 0.674–0.861; P < 0.001), 0.793 (95% CI: 0.718–0.867; P < 0.001), 0.785 (95% CI: 0.711–0.859; P < 0.001), 0.776 (95% CI: 0.703–0.849; P < 0.001), and 0.803 (95% CI: 0.733–0.872; P < 0.001), respectively. The discriminative ability of our model was superior to other evaluated scores. Conclusion The SEMMELWEIS-CRT score (available at semmelweiscrtscore.com) exhibited good discriminative capabilities for the prediction of all-cause death in CRT patients and outperformed the already existing risk scores. By capturing the non-linear association of predictors, the utilization of ML approaches may facilitate optimal candidate selection and prognostication of patients undergoing CRT implantation.

中文翻译：

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基于机器学习的心脏再同步治疗患者死亡率预测：SEMMELWEIS-CRT 评分

摘要目的 我们的目标是开发一种基于机器学习 (ML) 的风险分层系统，以根据接受心脏再同步的患者的植入前参数预测 1、2、3、4 和 5 年全因死亡率治疗（CRT）。方法和结果 在一个包含 1510 名接受 CRT 植入的患者的回顾性数据库中训练多个 ML 模型，以预测 1 至 5 年的全因死亡率。选择了 33 个植入前临床特征来训练模型。表现最佳的模型 [SEMMELWEIS-CRT 评分（通过机器学习对接受 CRT 植入的患者的估计死亡风险进行个性化评估）]，以及预先存在的评分（西雅图心力衰竭模型、VALID-CRT、EAARN、屏幕和 CRT -score），在一个由 158 名患者组成的独立队列中进行了测试。在 5 年随访期间，训练队列中有 805 人（53%）死亡，测试队列中有 80 人（51%）死亡。在经过训练的分类器中，随机森林表现出最好的性能。对于 1 年、2 年、3 年、4 年和 5 年死亡率的预测，SEMMELWEIS-CRT 评分的受试者操作特征曲线下面积为 0.768（95% CI：0.674–0.861；P < 0.001 ), 0.793 (95% CI: 0.718–0.867; P < 0.001), 0.785 (95% CI: 0.711–0.859; P < 0.001), 0.776 (95% CI: 0.703–0.804); 0.804 95% CI：0.733–0.872；P < 0.001)，分别。我们模型的判别能力优于其他评估分数。结论 SEMMELWEIS-CRT 评分（可在 semmelweiscrtscore. com) 在预测 CRT 患者的全因死亡方面表现出良好的判别能力，并且优于现有的风险评分。通过捕捉预测因子的非线性关联，ML 方法的使用可以促进接受 CRT 植入的患者的最佳候选者选择和预后。