Citations

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the 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. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, 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 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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 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.

e13002 Background: Despite improvement in survival in breast cancer patients from advanced therapies, rates of chemotherapy-induced cardiotoxicity manifesting as a decline in left ventricular ejection fraction (LVEF) have offset some of these benefits in patients receiving anthracycline-based chemotherapy followed by trastuzumab. Results suggesting clinical biomarkers predict cardiotoxicity are inconsistent. Recently, expression of angiotensin II type 1 receptor (ATR1) and endothelin 1 (ET1) has shown to play a role in breast tumor growth. We sought to evaluate whether the presence of ATR1 and ET1 in breast cancer tissue using immunohistochemistry (IHC) and qRT-PCR can predict chemotherapy-induced cardiotoxicity. Methods: We hypothesized that elevated expression of ATR1 and ET1 in tumor tissue is associated with chemotherapy-induced cardiotoxicity (LVEF < 50%) compared to tissue from controls. Preliminarily, 34 paraffin-embedded breast tissue specimens from women with breast cancer treated with anthracycline-based chemotherapy and trastuzumab were retrieved for analysis. IHC and qRT-PCR for expression of ATR1 and ET1 was performed on all specimens. Results: We found that ET1 expression was significantly increased in patients with an LVEF < 50%. In addition, we noted the lower the LVEF, the higher the ET1 expression (p = 0.03). We also found patients with a change in LVEF of greater than 10% had more ET1 expression compared to those without a change in LVEF (p = 0.05). We did not find a significant relationship between ATR1 and LVEF. Conclusions: Increased ET1 expression in breast tumor tissue may predict cardiotoxicity. Our findings may aid in future research using novel biomarkers to predict breast cancer patients at risk for developing chemotherapy-induced cardiotoxicity. In addition, targeted therapies to these biomarkers may potentially help prevent the development of cardiotoxicity.