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Morales-Lara AC, Johnson PW, Douglass EJ, O'Sullivan S, Yamani MH, Noseworthy PA, Carter RE, Adedinsewo DA. Artificial intelligence-based risk stratification of atrial fibrillation among women with peripartum cardiomyopathy compared to other cardiomyopathies. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.2503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Atrial fibrillation (AF) is diagnosed in up to 50% of patients with heart failure. However, the prevalence of AF among patients with peripartum cardiomyopathy (PPCM) ranges from only 2–10%, with the lowest rates in Black women. An artificial intelligence enhanced electrocardiogram (AI-ECG) has previously been shown to be effective in detecting AF while in sinus rhythm, and for AF risk prediction in a population-based study.
Purpose
Our objective was to evaluate the use of an AI-ECG for AF risk stratification among women of reproductive age (18 to 49 years) with PPCM compared to other forms of cardiomyopathy.
Methods
We identified 59 reproductive age women with a diagnosis of PPCM between January 2007, and October 2018 and included matched controls in a 3:1 fashion. Matching was performed based on sex, age, race, and left ventricular ejection fraction. We excluded patients with a diagnosis of AF prior to cardiomyopathy diagnosis date. AI-ECG prediction probabilities were generated for ECGs performed within a 30-day window prior to the patient's first cardiomyopathy diagnosis date for the entire study cohort.
Results
A total of 236 patients were included in the final analysis (59 cases, 177 controls). Overall, the median age at cardiomyopathy diagnosis was 31.7 years (IQR: 18.5, 49.4), 76.3% were White, 8.5% were Black, and 15.3% represented other or unknown race. Over the period studied, 3.4% of women with PPCM developed AF compared to 5.6% of women with other cardiomyopathies. The frequency of positive AI-ECG predictions for AF was more common among women with other cardiomyopathies (40.7%) compared to women with PPCM (20.3%). The predicted odds ratio for AF development following a cardiomyopathy diagnosis based on AI-ECG results was 0.37 (95% CI: 0.18, 0.73) for PPCM compared to other cardiomyopathies (p=0.006).
Conclusion
We demonstrated that an AI-ECG model for AF prediction may play a potential role in arrhythmia risk stratification/prediction among young women with PPCM who have a demonstrable lower risk for AF compared to women with other cardiomyopathies. Mechanisms for lower AF risk among patients with PPCM remain unknown. Further studies evaluating mechanistic pathways will be essential.
Funding Acknowledgement
Type of funding sources: None.
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Affiliation(s)
- A C Morales-Lara
- Mayo Clinic, Cardiovascular Medicine , Jacksonville , United States of America
| | - P W Johnson
- Mayo Clinic, Quantitative Health Sciences , Jacksonville , United States of America
| | - E J Douglass
- Mayo Clinic, Cardiovascular Medicine , Jacksonville , United States of America
| | - S O'Sullivan
- Mayo Clinic, Cardiovascular Medicine , Jacksonville , United States of America
| | - M H Yamani
- Mayo Clinic, Cardiovascular Medicine , Jacksonville , United States of America
| | - P A Noseworthy
- Mayo Clinic, Cardiovascular Medicine , Rochester , United States of America
| | - R E Carter
- Mayo Clinic, Quantitative Health Sciences , Jacksonville , United States of America
| | - D A Adedinsewo
- Mayo Clinic, Cardiovascular Medicine , Jacksonville , United States of America
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Adedinsewo D, Johnson PW, Douglass EJ, Attia ZI, Phillips SD, Goswami RM, Yamani MH, Connolly HM, Rose CH, Sharpe EE, Lopez-Jimenez F, Friedman PA, Carter RE, Noseworthy PA. Detecting cardiomyopathies in pregnancy and the postpartum period using ECG. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.3062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
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.
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Affiliation(s)
- D Adedinsewo
- Mayo Clinic, Jacksonville, United States of America
| | - P W Johnson
- Mayo Clinic, Jacksonville, United States of America
| | - E J Douglass
- Mayo Clinic, Jacksonville, United States of America
| | - Z I Attia
- Mayo Clinic, Cardiovascular Medicine, Rochester, United States of America
| | - S D Phillips
- Mayo Clinic, Jacksonville, United States of America
| | - R M Goswami
- Mayo Clinic, Jacksonville, United States of America
| | - M H Yamani
- Mayo Clinic, Jacksonville, United States of America
| | - H M Connolly
- Mayo Clinic, Cardiovascular Medicine, Rochester, United States of America
| | - C H Rose
- Mayo Clinic, Obstetrics and Gynecology, Rochester, United States of America
| | - E E Sharpe
- Mayo Clinic, Anesthesia and Perioperative Medicine, Rochester, United States of America
| | - F Lopez-Jimenez
- Mayo Clinic, Cardiovascular Medicine, Rochester, United States of America
| | - P A Friedman
- Mayo Clinic, Cardiovascular Medicine, Rochester, United States of America
| | - R E Carter
- Mayo Clinic, Jacksonville, United States of America
| | - P A Noseworthy
- Mayo Clinic, Cardiovascular Medicine, Rochester, United States of America
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