Butler Liam, Ivanov Alexander, Celik Turgay, Karabayir Ibrahim, Chinthala Lokesh, Tootooni Mohammad S, Jaeger Byron C, Patterson Luke T, Doerr Adam J, McManus David D, Davis Robert L, Herrington David, Akbilgic Oguz
Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.
Center for Biomedical Informatics, University of Tennessee Health Sciences Center, Memphis, TN 38163, USA.
J Cardiovasc Dev Dis. 2024 Dec 8;11(12):395. doi: 10.3390/jcdd11120395.
: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. : The study aimed to develop ECG-AI models predicting FCHD risk from ECGs. : Data from 10 s 12-lead ECGs and demographic/clinical data from University of Tennessee Health Science Center (UTHSC) were used for model development. Of this dataset, 80% was used for training and 20% as holdout. Data from Atrium Health Wake Forest Baptist (AHWFB) were used for external validation. We developed two separate convolutional neural network models using 12-lead and Lead I ECGs as inputs, and time-dependent Cox proportional hazard models using demographic/clinical data with ECG-AI outputs. Correlation of the predictions from the 12- and 1-lead ECG-AI models was assessed. : The UTHSC cohort included data from 50,132 patients with a mean age (SD) of 62.50 (14.80) years, of whom 53.4% were males and 48.5% African American. The AHWFB cohort included data from 2305 patients with a mean age (SD) of 63.04 (16.89) years, of whom 51.0% were males and 18.8% African American. The 12-lead and Lead I ECG-AI models resulted in validation AUCs of 0.84 and 0.85, respectively. The best overall model was the Cox model using simple demographics with Lead I ECG-AI output (D1-ECG-AI-Cox), with the following results: AUC = 0.87 (0.85-0.89), accuracy = 83%, sensitivity = 69%, specificity = 89%, negative predicted value (NPV) = 92% and positive predicted value (PPV) = 55% on the AHWFB validation cohort. For this, the 2-year FCHD risk prediction accuracy was AUC = 0.91 (0.90-0.92). The 12-lead versus Lead I ECG FCHD risk prediction showed strong correlation (R = 0.74). : The 2-year FCHD risk can be predicted with high accuracy from single-lead ECGs, further improving when combined with demographic information.
在美国，每年约有65万人死于冠心病（FCHD）。心电图人工智能（ECG-AI）模型可以预测不良冠状动脉事件，但其在FCHD中的应用研究不足。
本研究旨在开发从心电图预测FCHD风险的ECG-AI模型。
田纳西大学健康科学中心（UTHSC）的10秒12导联心电图数据和人口统计学/临床数据用于模型开发。在这个数据集中，80%用于训练，20%作为保留数据。心房健康韦克福里斯特浸礼会医院（AHWFB）的数据用于外部验证。我们开发了两个独立的卷积神经网络模型，分别使用12导联和I导联心电图作为输入，并使用人口统计学/临床数据和ECG-AI输出构建时间依赖性Cox比例风险模型。评估了12导联和I导联ECG-AI模型预测结果的相关性。
UTHSC队列包括50132名患者的数据，平均年龄（标准差）为62.50（14.80）岁，其中53.4%为男性，48.5%为非裔美国人。AHWFB队列包括2305名患者的数据，平均年龄（标准差）为63.04（16.89）岁，其中51.0%为男性，18.8%为非裔美国人。12导联和I导联ECG-AI模型的验证AUC分别为0.84和0.85。最佳总体模型是使用简单人口统计学数据和I导联ECG-AI输出的Cox模型（D1-ECG-AI-Cox），在AHWFB验证队列中的结果如下：AUC = 0.87（0.85-0.89），准确率 = 83%，灵敏度 = 69%，特异性 = 89%，阴性预测值（NPV） = 92%，阳性预测值（PPV） = 55%。据此，2年FCHD风险预测准确率为AUC = 0.91（0.90-0.92）。12导联与I导联心电图FCHD风险预测显示出很强的相关性（R = 0.74）。
单导联心电图可以高精度预测2年FCHD风险，与人口统计学信息结合时预测效果进一步提高。
