Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model.

ABSTRACT:

Aims

Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period.

Methods and results

We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptid

SUBMITTER: Adedinsewo DA 

PROVIDER: S-EPMC8715757 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature