ArticleEuropean heart journal. Digital health2021
Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model.
Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 40 citations in OpenAlex.
- Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.Nature medicine · 2024Trial
- Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.Current opinion in anaesthesiology · 2026Review
- The Impact of Artificial Intelligence on Women's Cardiovascular Disease Care.Current cardiology reports · 2025Review
- A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods.American heart journal plus : cardiology research and practice · 2025Article
- Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age.Annals of family medicine · 2025Article
- Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review.Children (Basel, Switzerland) · 2024Review
- Universal Cardiovascular Disease Risk Assessment in Pregnancy: Call to ActionJACC. Advances · 2024Review
- Artificial intelligence-based screening for cardiomyopathy in an obstetric population: A pilot study.Cardiovascular digital health journal · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Adopting artificial intelligence in cardiovascular medicine: a scoping review.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- AI-based preeclampsia detection and prediction with electrocardiogram data.Frontiers in cardiovascular medicine · 2024Article
- Automated electrocardiogram signal quality assessment based on Fourier analysis and template matching.Journal of clinical monitoring and computing · 2023Article
- Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.Circulation research · 2022Review
- A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning.Biophysical reviews · 2022Review
- Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects.Vascular health and risk management · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 3 institutions in 1 country.
Funding
Abstract
aimsCardiovascular 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
resultsWe 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.
conclusionsAn 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.