Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy

Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear.This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy.This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software version 1.00.00 [AiTiALVSD], which was developed to screen for left ventricular systolic dysfunction in the general population), was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. Results were obtained under a 95% confidence interval and considered significant when p < 0.05.Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead ECG was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000) and area under the precision recovery curve, sensitivity, specificity, positive predictive value, and negative predictive value of 0.715 (0.499-0.951), 0.917 (0.760-1.000), 0.927 (0.890-0.964), 0.440 (0.245-0.635), and 0.994 (0.983-1.000), respectively. 1-Lead (lead I) artificial intelligence/machine learning algorithm also showed excellent performance; the area under the receiver operating characteristic, area under the precision recovery curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (0.895-0.993), 0.520 (0.319-0.801), 0.833 (0.622-1.000), 0.880 (0.834-0.926), 0.303 (0.146-0.460), and 0.988 (0.972-1.000), respectively.The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-Lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.