Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction
Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction
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DOI：
10.1111/jce.13889
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发表时间：
2019-05-01
影响因子：
2.7
通讯作者：
Noseworthy, Peter A.
中科院分区：
文献类型：
--
作者：
Attia, Zachi I.;Kapa, Suraj;Noseworthy, Peter A.
我们试图验证一种深度学习算法，该算法旨在基于大型前瞻性队列中的12导联心电图（ECG）预测射血分数（EF）小于或等于35%。背景：接受常规ECG检查的患者可能存在未被发现的左心室功能障碍，需要进一步超声心动图评估。然而，识别这些患者可能具有挑战性。方法我们将该算法应用于2018年9月由马约诊所ECG实验室解读的所有ECG。该算法的性能在最近进行LV功能超声心动图评估的患者中进行了测试。我们还将该算法应用于近期未进行LV功能超声心动图评估的患者，以确定新的"阳性筛查"率。结果在16056例接受常规ECG检查的成人患者中，8600例（年龄67.1 ± 15.2岁，45.6%男性）接受了经胸超声心动图（TTE）检查，3874例患者的TTE和ECG间隔不到1个月。在这些患者中，该算法能够检测到EF小于或等于35%，特异性为86.8%，灵敏度为82.5%，准确性为86.5%（曲线下面积为0.918）。在474例"假阳性"筛查中，189例（39.8%）的EF为36%至50%。在既往无TTE的患者中，该算法确定了3.5%的疑似EF ≤ 35%的患者。探索性分析表明，在初始"阳性筛选"后评估NT-pro-BNP可减少假阳性。"结论深度学习算法在常规实践中检测到LV功能下降，具有良好的准确性。需要进一步的研究来验证该算法在没有既往超声心动图的患者中的有效性，并评估对超声心动图利用率、成本和临床结局的影响。
Objectives We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort. Background Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging. Methods We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new "positive screens." Results Among 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 +/- 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 "false-positives screens," 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT-pro-BNP after the initial "positive screen." Conclusions A deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes.