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AI stethoscope doubles detection of pregnancy heart failure
![Summary of study design. This figure summarizes the study design, interventions, study-related visits and key study procedures. Participants could enter the study at any time point during pregnancy or postpartum (up to 12 months). As such, each individual participant could have up to seven visits if they enter the study in the first trimester of pregnancy and fewer depending on the time of study entry. AI-based screening was performed up to seven times during the study period, including during each trimester of pregnancy (first trimester, <14 weeks; second trimester, 14 to <28 weeks; and third trimester, 28 to <42 weeks and post-term) (up to three ECGs), between delivery and 6 weeks, between 6 weeks and 3 months, between 3 and 5 months, and between 5 and 12 months postpartum (up to four ECGs). Only participants in the intervention arm had a baseline echocardiogram as well as a simultaneous portable ECG recorded at each time point. AI-based prediction for LVSD using the digital stethoscope was available in real time at the point of care and 12-lead AI-ECG predictions for LVSD were provided asynchronously, usually within 1 week of ECG acquisition. *, visit 1 can vary for each participant depending on the time point at study entry in relation to delivery. Credit: Nature Medicine (2024). DOI: 10.1038/s41591-024-03243-9 AI stethoscope doubles detection of pregnancy heart failure](https://scx1.b-cdn.net/csz/news/800a/2024/ai-stethoscope-doubles.jpg)
Heart failure during pregnancy is a dangerous and often under-detected condition because common symptoms—shortness of breath, extreme fatigue and trouble breathing while lying down—are easily mistaken for typical pregnancy discomforts. Late-breaking research presented at the European Society of Cardiology Congress on a Mayo Clinic study showed an artificial intelligence (AI)-enabled digital stethoscope helped doctors identify twice as many cases of heart failure compared to a control group that received usual obstetric care and screening. Full study findings are published in Nature Medicine.
The trial was conducted in Nigeria, where more women experience pregnancy-related heart failure than anywhere in the world. The results also indicate that screening including the AI-enabled digital stethoscope was 12-times more likely than traditional screening to flag heart pump weakness when evaluated at an ejection fraction threshold lower than 45%, which is the cutoff indicating a specific type of heart failure called peripartum cardiomyopathy.
"Recognizing this type of heart failure early is important to the mother's health and well-being," says Demilade Adedinsewo, M.D., a cardiologist at Mayo Clinic and lead investigator of the study.
"The symptoms of peripartum cardiomyopathy can get progressively worse as pregnancy advances, or more commonly following childbirth, and can endanger the mother's life if her heart becomes too weak. Medicines can help when the condition is identified but severe cases may require intensive care, a mechanical heart pump, or sometimes a heart transplant, if not controlled with medical therapy."
The randomized, controlled, open-label clinical trial included nearly 1,200 participants who were screened for heart conditions through typical obstetric care or AI-enhanced solutions. Mayo Clinic researchers previously developed a foundational 12-lead AI-electrocardiogram (ECG) algorithm to predict a weak heart pump, clinically known as low ejection fraction. A version of this algorithm was further enhanced by Eko Health for its point-of-care digital stethoscope, which is U.S. Food and Drug Administration (FDA)-cleared to detect heart failure with low ejection fraction.
The researchers found that doctors using AI-based screening with the digital stethoscope and 12-lead ECG detected weak heart function with high accuracy. Within the study cohort, the digital stethoscope helped flag twice as many cases of low ejection fraction <50% and doctors using it were 12 times more likely to identify an ejection fraction <45% as compared to usual care.
The AI-supported tools were evaluated at three different levels of ejection fraction used in clinical diagnosis. Less than 45% is the cut point for diagnosing peripartum cardiomyopathy. Less than 40% indicates heart failure with reduced ejection fraction and presents strong evidence in favor of specific medications to reduce symptoms and the risk of death.
An ejection fraction of less than 35% signals severely low heart pump function that often requires more intense management, including advanced heart failure therapies and an implantable defibrillator if pump function does not recover. Patients in the intervention group each had an echocardiogram at study entry to provide confirmation of the AI-predictions.
"This study provides evidence that we can better detect peripartum cardiomyopathy among women in Nigeria. However, there are more questions to be answered," says Dr. Adedinsewo. "Our next steps would be to evaluate usability and adoption of this tool by Nigerian health care providers (including doctors and nurses) and importantly, its impact on patient care. Peripartum cardiomyopathy affects approximately 1 in 2,000 women within the U.S. and as many as 1 in 700 African American women. Evaluating this AI tool in the U.S. will further test its abilities in varied populations and health care settings."
More information: Demilade A. Adedinsewo et al, Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial, Nature Medicine (2024). DOI: 10.1038/s41591-024-03243-9