Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea
Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea
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DOI：
10.1161/circep.120.008437
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发表时间：
2020-08-01
影响因子：
8.4
通讯作者：
Noseworthy, Peter A.
中科院分区：
文献类型：
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作者：
Adedinsewo, Demilade;Carter, Rickey E.;Noseworthy, Peter A.
背景：在急诊科（艾德）因急性呼吸困难就诊的患者中识别收缩性心力衰竭是一项挑战。呼吸困难的原因往往是多方面的。有针对性的身体评估和诊断测试可能缺乏敏感性和特异性。本研究的目的是评估人工智能ECG识别患有左心室收缩功能障碍（LVSD）的呼吸困难患者的准确性。研究方法：我们回顾性地应用了一种经验证的人工智能ECG算法来识别LVSD（定义为LV射血分数≥ 18岁，在马约诊所的艾德中心接受呼吸困难评估）。如果患者在艾德访视当天至少获得一次标准12导联ECG，并且在就诊后30天内进行了超声心动图检查，则将其纳入研究。排除了既往LVSD患者。我们使用受试者工作特征曲线下面积、准确性、灵敏度和特异性评估模型性能。结果：共纳入1606例患者。从ECG到超声心动图的中位时间为1天（Q1：1，Q3：2）。人工智能ECG算法识别LVSD的受试者工作特征曲线下面积为0.89（95% CI，0.86-0.91），准确度为85.9%。敏感性、特异性、阴性预测值和阳性预测值分别为74%、87%、97%和40%。为了识别射血分数800，识别了LVSD，其具有0.80（95%CI，0.76-0.84）的受试者工作特征曲线下的面积。结论：ECG是一种廉价、普遍、无痛的检查，可在ED中快速获得。当使用人工智能进行分析时，它可有效识别因呼吸困难而到艾德就诊的选定患者的LVSD，并且优于NT-proBNP。
Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction = 18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction 800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76-0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP.