At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice.

在急诊科 (ED)，快速准确地确定哪些患者可能发生重大不良心脏事件 (MACE) 非常重要。机器学习 (ML) 模型可用于帮助医生检测 MACE，而提高此类模型的性能是一个活跃的研究领域。在这项研究中，我们试图确定是否可以通过纳入每位患者之前的心电图 (ECG) 来改进 ML 模型。为此，我们使用 2017 年至 2018 年期间从瑞典南部 5 个急诊室连续 19,499 名胸痛患者收集的数据，训练了多个模型来预测 30 天内的 MACE，无论是否有既往心电图。我们的结果表明与之前的心电图相比，AUC 没有改善。对于不同的患者亚组和不同的结果子集，无论是否有额外的临床输入变量，这在各个模型中都是一致的。虽然与当前手动心电图分析的最佳实践相矛盾，但结果是积极的，因为输入较少的 ML 模型在实践中更容易、更广泛地应用。