An artificial intelligence electrocardiogram analysis for detecting cardiomyopathy in the peripartum period. (1st April 2022)

• Record Type: Journal Article
• Authors: Lee, Yeji; Choi, Byungjin; Lee, Min Sung; Jin, Uram; Yoon, Seokyoung; Jo, Yong-Yeon; Kwon, Joon-myoung
• Is Part Of: International journal of cardiology. Volume 352(2022)
• Page Start: 72
• Page End: 77
• Publication Date: 2022-04-01
• Subjects: Peripartum cardiomyopathy -- Left ventricular dysfunction -- Deep learning -- Electrocardiogram -- Artificial intelligence; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2022.01.064
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: Peripartum cardiomyopathy (PPCM) is a fatal maternal complication, with left ventricular systolic dysfunction (LVSD; Left ventricular ejection fraction 45% or less) occurring at the end of pregnancy or in the months following delivery. The scarcity of screening tools for PPCM leads to a delayed diagnosis and increases its mortality and morbidity. We aim to evaluate an electrocardiogram (ECG)-deep learning model (DLM) for detecting cardiomyopathy in the peripartum period. Methods: For the DLM development and internal performance test for detecting LVSD, we obtained a dataset of 122, 733 ECG-echocardiography pairs from 58, 530 male and female patients from two community hospitals. For the DLM external validation, this study included 271 ECG-echocardiography pairs (157 unique pregnant and postpartum period women) examined in the Ajou University Medical Center (AUMC) between January 2007 and May 2020. All included cases underwent an ECG within two weeks before or after the day of transthoracic echocardiography, which was performed within a month before delivery, or within five months after delivery. Based on the diagnostic criteria of PPCM, we analyzed the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to evaluate the model effectiveness. Results: The ECG-based DLM detected PPCM with an AUROC of 0.877. Moreover, its sensitivity, specificity, PPV, and NPV for the detection of PPCM were 0.877, 0.833, 0.809, 0.352, and 0.975, respectively. Conclusions: An ECG-based DLM non-invasively and effectively detects cardiomyopathies occurring in the peripartum period and could be an ideal screening tool for PPCM. Highlights: Artificial intelligence electrocardiogram analysis is known to efficiently detect left ventricular dysfunction in adults. Our study shows that peripartum cardiomyopathy, a fatal maternal complication with left ventricular systolic dysfunction, is also noninvasively and efficiently predicted by the ECG-based deep learning model in the East Asian racial group.