Division of Cardiology, Department of Internal Medicine, Inha University College of Medicine, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, 22332, Republic of Korea.
School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.
Sci Rep. 2023 Sep 13;13(1):15187. doi: 10.1038/s41598-023-42252-5.
Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662-0.807) and 0.734 (95% CI: 0.688-0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830-0.835), precision of 0.764 (95% CI: 0.757-0.771), recall of 0.747 (95% CI: 0.741-0.753), and F1 score of 0.747 (95% CI: 0.741-0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156-3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management.
尽管在严重程度评分系统方面存在挑战，但人工智能增强心电图（AI-ECG）可能有助于早期预测 2019 年冠状病毒病（COVID-19）的严重程度。在 2020 年 3 月至 2022 年 6 月期间，我们招募了 1453 名在我们急诊科接受心电图检查的 COVID-19 患者（平均年龄：59.7±20.1 岁；54.2%为男性），这些患者在严重程度分类之前进行了心电图检查。使用受试者工作特征曲线下面积（AUC）、精度、召回率和 F1 评分，评估 AI-ECG 算法在入院时进行严重程度评估的能力，并与早期预警评分（EWS）进行比较。在内部和外部验证中，AI 算法在预测 COVID-19 严重程度方面表现出合理的结果，AUC 分别为 0.735（95%CI：0.662-0.807）和 0.734（95%CI：0.688-0.781）。与 EWS 相结合，其 AUC 为 0.833（95%CI：0.830-0.835）、精度为 0.764（95%CI：0.757-0.771）、召回率为 0.747（95%CI：0.741-0.753）、F1 评分也为 0.747（95%CI：0.741-0.753），具有可靠的性能。在 Cox 比例风险模型中，即使在调整了相关参数后，AI-ECG 也显示出更高的死亡风险比（HR，2.019；95%CI：1.156-3.525，p=0.014）。因此，AI-ECG 的应用有可能有助于早期 COVID-19 严重程度预测，从而改善患者管理。
