Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan; Division of Cardiovascular Medicine, Asia University Hospital, Taichung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, Taiwan.
Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan.
Mayo Clin Proc. 2022 Dec;97(12):2291-2303. doi: 10.1016/j.mayocp.2022.05.014. Epub 2022 Nov 3.
To implement an all-day artificial intelligence (AI)-based system to facilitate chest pain triage in the emergency department.
The AI-based triage system encompasses an AI model combining a convolutional neural network and long short-term memory to detect ST-elevation myocardial infarction (STEMI) on electrocardiography (ECG) and a clinical risk score (ASAP) to prioritize patients for ECG examination. The AI model was developed on 2907 twelve-lead ECGs: 882 STEMI and 2025 non-STEMI ECGs.
Between November 1, 2019, and October 31, 2020, we enrolled 154 consecutive patients with STEMI: 68 during the AI-based triage period and 86 during the conventional triage period. The mean ± SD door-to-balloon (D2B) time was significantly shortened from 64.5±35.3 minutes to 53.2±12.7 minutes (P=.007), with 98.5% vs 87.2% (P=.009) of D2B times being less than 90 minutes in the AI group vs the conventional group. Among patients with an ASAP score of 3 or higher, the median door-to-ECG time decreased from 30 minutes (interquartile range [IQR], 7-59 minutes) to 6 minutes (IQR, 4-30 minutes) (P<.001). The overall performances of the AI model in identifying STEMI from 21,035 ECGs assessed by accuracy, precision, recall, area under the receiver operating characteristic curve, F1 score, and specificity were 0.997, 0.802, 0.977, 0.999, 0.881, and 0.998, respectively.
Implementation of an all-day AI-based triage system significantly reduced the D2B time, with a corresponding increase in the percentage of D2B times less than 90 minutes in the emergency department. This system may help minimize preventable delays in D2B times for patients with STEMI undergoing primary percutaneous coronary intervention.
实施一个全天人工智能（AI）系统，以方便急诊科胸痛分诊。
基于 AI 的分诊系统包括一个 AI 模型，该模型结合卷积神经网络和长短时记忆来检测心电图（ECG）上的 ST 段抬高型心肌梗死（STEMI）和临床风险评分（ASAP），以优先安排患者进行 ECG 检查。该 AI 模型是在 2907 份 12 导联 ECG 上开发的：882 份 STEMI 和 2025 份非 STEMI ECG。
在 2019 年 11 月 1 日至 2020 年 10 月 31 日期间，我们连续收治了 154 例 STEMI 患者：86 例在常规分诊期间和 68 例在基于 AI 的分诊期间。门球时间（D2B）明显缩短，从 64.5±35.3 分钟缩短至 53.2±12.7 分钟（P=.007），AI 组和常规组的 D2B 时间均小于 90 分钟的比例分别为 98.5%和 87.2%（P=.009）。在 ASAP 评分≥3 的患者中，中位门到 ECG 时间从 30 分钟（四分位距[IQR]，7-59 分钟）缩短至 6 分钟（IQR，4-30 分钟）（P<.001）。AI 模型在识别 21035 份 ECG 中的 STEMI 的整体性能，通过准确性、精密度、召回率、接收者操作特征曲线下面积、F1 评分和特异性来评估，分别为 0.997、0.802、0.977、0.999、0.881 和 0.998。
实施全天基于 AI 的分诊系统可显著缩短 D2B 时间，并相应增加 D2B 时间在 90 分钟内的比例。该系统有助于减少接受直接经皮冠状动脉介入治疗的 STEMI 患者的可预防的 D2B 时间延迟。
