Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.
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DOI：
10.1161/circulationaha.120.047829
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发表时间：
2021-03-30
期刊：
影响因子：
37.8
通讯作者：
Haggerty CM
中科院分区：
文献类型：
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作者：
Raghunath S;Pfeifer JM;Ulloa-Cerna AE;Nemani A;Carbonati T;Jing L;vanMaanen DP;Hartzel DN;Ruhl JA;Lagerman BF;Rocha DB;Stoudt NJ;Schneider G;Johnson KW;Zimmerman N;Leader JB;Kirchner HL;Griessenauer CJ;Hafez A;Good CW;Fornwalt BK;Haggerty CM
补充数字内容可在文本中找到。心房颤动（AF）与大量发病率相关，特别是当它未被发现时。如果新发房颤可以预测，可以使用有针对性的筛查来早期发现它。我们假设深度神经网络可以通过静息12导联心电图预测新发房颤，这种预测可能有助于识别房颤相关卒中的风险。我们使用了1984年至2019年收集的43万例患者的1.6 M静息12导联数字ECG迹。训练深度神经网络来预测无房颤病史患者的新发房颤（1年内）。使用受试者工作特征曲线和精确召回曲线下的面积来评估表现。根据模型预测对心电图进行分层后，我们进行了30年的无发病率生存分析。为了模拟真实世界的部署，我们使用2010年之前的所有心电图训练了一个单独的模型，并在2010年至2014年与卒中注册表相关的心电图测试集上评估了模型的性能。我们在不同的预测阈值下，从模型预测的房颤高风险患者中识别出房颤相关卒中的高危患者。预测心电图1年内新发房颤的受试者工作特征曲线下面积和精确召回曲线下面积分别为0.85和0.22。在30年的时间跨度内，预测的高风险组与低风险组的风险比为7.2 （95% CI, 6.9-7.6）。在模拟部署场景中，该模型预测1年后新发房颤的敏感性为69%，特异性为81%。发现1例新发房颤需要筛查的人数为9人。该模型预测，在心电图指数3年内发生房颤相关卒中的所有患者中，有62%的患者具有新发房颤的高风险。对于无房颤病史的患者，深度学习可以通过12导联心电图预测新发房颤。这种预测可能有助于识别有房颤相关卒中风险的患者。
Supplemental Digital Content is available in the text. Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9–7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.