Several recent high-impact studies leverage large hospital-owned electrocardiographic (ECG) databases to model and predict patient mortality. MIMIC-IV, released September 2023, is the first comparable public dataset and includes 800,000 ECGs from a U.S. hospital system. Previously, the largest public ECG dataset was Code-15, containing 345,000 ECGs collected during routine care in Brazil. These datasets now provide an excellent resource for a broader audience to explore ECG survival modeling. Here, we benchmark survival model performance on Code-15 and MIMIC-IV with two neural network architectures, compare four deep survival modeling approaches to Cox regressions trained on classifier outputs, and evaluate performance at one to ten years. Our results yield AUROC and concordance scores comparable to past work (circa 0.8) and reasonable AUPRC scores (MIMIC-IV: 0.4-0.5, Code-15: 0.05-0.13) considering the fraction of ECG samples linked to a mortality (MIMIC-IV: 27\%, Code-15: 4\%). When evaluating models on the opposite dataset, AUROC and concordance values drop by 0.1-0.15, which may be due to cohort differences. All code and results are made public.

翻译：近期多项高影响力研究利用医院持有的大型心电图（ECG）数据库建模并预测患者死亡率。2023年9月发布的MIMIC-IV是首个可比的公开数据集，包含来自美国医院系统的80万份心电图。此前最大的公开心电图数据集Code-15收录了巴西常规诊疗中收集的34.5万份心电图。这些数据集为更广泛的研究者探索心电图生存建模提供了优质资源。本研究采用两种神经网络架构，在Code-15和MIMIC-IV数据集上对生存模型性能进行基准测试，比较了四种深度生存建模方法与基于分类器输出训练的Cox回归模型，并评估了一至十年时间范围内的预测性能。考虑到与死亡事件关联的心电图样本比例（MIMIC-IV：27%，Code-15：4%），本研究获得的AUROC和一致性指数与既往研究相当（约0.8），AUPRC分数处于合理范围（MIMIC-IV：0.4-0.5，Code-15：0.05-0.13）。当在相反数据集上评估模型时，AUROC和一致性指数下降0.1-0.15，这可能源于队列差异。所有代码与结果均已公开。