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.

研究使用大型医院拥有的心电图数据库来建模和预测患者死亡率，通过与两种神经网络架构比较四种深度存活建模方法以及在一到十年内的绩效评估，结果表明 AUROC 和协调性得分与以往工作相当（约 0.8），考虑到与死亡率相关的心电图样本的比例（MIMIC-IV：27％，Code-15：4％），AUPRC 得分合理（MIMIC-IV：0.4-0.5，Code-15：0.05-0.13）。