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Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy
Young Mi Jung1, Sora Kang2, Jeong Min Son2
1Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee); Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea (Drs Jung and S Lee); Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee).
Insights
Artificial intelligence algorithms using 12-lead and 1-lead electrocardiography show high effectiveness in screening for peripartum cardiomyopathy. These noninvasive tools can aid in the early detection of this critical condition in pregnant women.
Area of Science:
- Cardiology
- Medical Technology
- Artificial Intelligence
Background:
- Peripartum cardiomyopathy is a life-threatening condition causing heart failure in pregnant women.
- Electrocardiography abnormalities can indicate left ventricular dysfunction, but its role in peripartum cardiomyopathy screening is unclear.
Purpose of the Study:
- To evaluate the effectiveness of AI/ML-based software using 12-lead ECG for screening peripartum cardiomyopathy.
- To assess a 1-lead ECG AI/ML algorithm for detecting left ventricular systolic dysfunction in peripartum cardiomyopathy.
Main Methods:
- Retrospective cohort study of pregnant women undergoing echocardiography and ECG.
- Evaluation of AiTiALVSD (12-lead ECG) and a 1-lead ECG AI/ML algorithm for peripartum cardiomyopathy detection.
- Analysis of performance metrics including AUC, sensitivity, specificity, and predictive values.
Main Results:
- The 12-lead ECG AI/ML software (AiTiALVSD) demonstrated high effectiveness (AUC 0.979) in identifying peripartum cardiomyopathy.
- A 1-lead ECG AI/ML algorithm also showed excellent performance (AUC 0.944) in detecting the condition.
- Both methods exhibited high sensitivity and specificity for screening peripartum cardiomyopathy.
Conclusions:
- AI/ML analysis of 12-lead and 1-lead ECGs are effective, noninvasive screening tools for peripartum cardiomyopathy.
- These algorithms show potential for widespread use in identifying peripartum cardiomyopathy during the peripartum period.
Background:
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.
Objective:
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.
Study Design:
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, 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. The results were obtained under a 95% confidence interval and considered significant when P<.05.
Results:
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 electrocardiography 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), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively.
Conclusion:
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.
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