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Published on: May 23, 2021
Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model
Demilade A Adedinsewo1, Patrick W Johnson2, Erika J Douglass1
1Department of Cardiovascular Medicine, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA.
Insights
An electrocardiogram (ECG)-based deep learning model effectively identifies cardiomyopathy in pregnant and postpartum women. This AI tool shows promise as an initial screening method, outperforming traditional tests.
Area of Science:
- Cardiology
- Maternal Health
- Artificial Intelligence in Medicine
Background:
- Cardiovascular disease poses significant risks to maternal health, with cardiomyopathy being a common acquired condition during pregnancy and postpartum.
- Early and accurate identification of cardiomyopathy is crucial for timely intervention and improved maternal outcomes.
Purpose of the Study:
- To evaluate the effectiveness of a deep learning model utilizing electrocardiogram (ECG) data for identifying cardiomyopathy in pregnant and postpartum women.
- To compare the diagnostic performance of the ECG-based deep learning model against established biomarkers and clinical models.
Main Methods:
- A cohort of 1807 pregnant or postpartum women was analyzed using an ECG-based deep learning model.
- Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
- Diagnostic probabilities from the deep learning model were compared with natriuretic peptides and a multivariable clinical model.
Main Results:
- The deep learning model achieved high AUCs for detecting cardiomyopathy: 0.92 (LVEF ≤ 35%), 0.89 (LVEF < 45%), and 0.87 (LVEF < 50%).
- The model demonstrated superior performance compared to natriuretic peptides (AUCs 0.85-0.86) and a multivariable model (AUC 0.72).
- Notably, the model showed higher AUCs in Black (0.95) and Hispanic (0.98) women compared to White (0.91) women for LVEF ≤ 35%.
Conclusions:
- An ECG-based deep learning model is effective in identifying cardiomyopathy during pregnancy and the postpartum period.
- This AI approach surpasses the diagnostic capabilities of natriuretic peptides and traditional clinical parameters.
- The model holds significant potential as a powerful initial screening tool for cardiomyopathy within obstetric care settings.
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 peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, <45%, and <50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF < 45%), and 0.87 (LVEF < 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively.
Conclusions:
An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.
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