Development and Validation of an Electrocardiographic Artificial Intelligence Model for Detection of Peripartum Cardiomyopathy.

BACKGROUND: This study used electrocardiogram (ECG) data in conjunction with artificial intelligence (AI) methods as a non-invasive tool for detecting peripartum cardiomyopathy (PPCM).

OBJECTIVE: The primary objective was to assess the efficacy of a heart failure detection model for detecting peripartum cardiomyopathy detection using an AI deep learning model called a 1-dimensional convolutional neural network.

STUDY DESIGN: We first built a deep learning model for heart failure detection using retrospective data at University of Tennessee Health Science Center (UTHSC). Cases were adult and non-pregnant females with a heart failure diagnosis; controls were adult non-pregnant females without heart failure. The model was then tested on an independent cohort of pregnant women at UTHSC who either did or did not have peripartum cardiomyopathy. We also tested the model in an external cohort of pregnant women at Atrium Health Wake Forest Baptist (AHWFB). Key outcomes were assessed using the area under the receiver operating characteristic curve (AUC). We also repeated our analysis using only lead I ECG as an input to assess feasibility of remote monitoring via wearables that can capture single-lead ECG data.

RESULTS: The UTHSC heart failure cohort comprised 346,339 ECGs from 142,601 patients. In this cohort, 60% were Black and 37% were white, with an average age (SD) of 53 (19). The heart failure detection model achieved an AUC of 0.92 on the holdout set. We then tested the ability of the heart failure model to detect peripartum cardiomyopathy in an independent cohort of pregnant women from UTHSC and an external cohort of pregnant women from AWFBH. The independent UTHSC cohort included 158 ECGs from 115 patients; our deep learning model achieved an AUC of 0.83 [0.77-0.89] for this dataset. The external AHWFB cohort involved 80 ECGs from 43 patients; our deep learning model achieved an AUC of 0.83 [0.77-0.89] AUC of 0.94 [0.91-0.98] for this dataset. For identifying peripartum cardiomyopathy diagnosed 10 or more days post-delivery, the model achieved an AUC of 0.88 [0.81-0.94] for the UTHSC cohort and an AUC of 0.96 [0.93-0.99] for the AHWFB cohort. When we repeated our analysis by building a heart failure detection model using only lead I ECGs, we obtained similarly high detection accuracies, with AUCs of 0.73 and 0.93 for the UTHSC and AHWFB cohorts, respectively.

CONCLUSIONS: AI can accurately detect peripartum cardiomyopathy from ECG alone. A simple ECG-AI-based peripartum screening could result in a more timely diagnosis. Since results with 1-lead ECG data were similar to those obtained using all 12 leads, future studies will focus on remote screening for peripartum cardiomyopathy using smartwatches that can capture single-lead ECG data.