University of Pittsburgh, Pittsburgh, PA.
Northeast Georgia Health Systems, GA.
Ann Emerg Med. 2023 Jan;81(1):57-69. doi: 10.1016/j.annemergmed.2022.08.005. Epub 2022 Oct 15.
Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis.
This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis.
Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation.
In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.
疑似非 ST 段抬高型（NSTE）急性冠状动脉综合征患者的缺血性心电图（ECG）变化细微且短暂。然而，在急诊科后续评估中，通常不使用院外 ECG。因此，我们旨在比较院外和急诊科 ECG 的诊断性能，并评估人工智能增强 ECG 分析的增量增益。
本前瞻性观察队列研究招募了院外胸痛患者。我们检索了护理人员在现场获得的院外 ECG 和护士在住院评估期间获得的第一份急诊科 ECG。两位独立且盲目的审查员按照实践建议混合顺序解释 ECG 对。使用 179 种形态 ECG 特征，我们训练、交叉验证和测试了一个随机森林分类器，以增强非 ST 段抬高型急性冠状动脉综合征（NSTE-ACS）的诊断。
我们的样本包括 2122 名患者（年龄 59[16]；53%为女性；44%为黑人，13.5%为确诊的急性冠状动脉综合征）。院外-ECG 的 ST 抬高和 ST 压低诊断率分别为 5.9%和 16.2%，急诊科 ECG 的诊断率分别为 6.1%和 12.4%，约 40%的变化在院外-ECG 上持续存在，约 60%的变化得到解决。仅使用专家对院外-ECG 的解释作为基线，其性能较差，接受者操作特征（ROC）曲线下面积（AUC）、敏感性和阴性预测值分别为 0.69、0.50 和 0.92。使用专家对连续 ECG 变化的解释可以提高该性能（AUC 0.80、敏感性 0.61 和特异性 0.93）。有趣的是，仅使用人工智能算法增强院外-ECG 就可以提高其性能（AUC 0.83、敏感性 0.75 和特异性 0.95），与专家 ECG 解释相比，净重新分类改善率为 29.5%。
在这项研究中，60%的诊断性 ST 变化在到达医院之前得到解决，这使得急诊科 ECG 不适合 NSTE-ACS 的住院评估。使用连续 ECG 变化或纳入人工智能增强分析可以正确重新分类四分之一的疑似 NSTE-ACS 患者。
