ArticleJACC. Advances2026
Validating ECG-Based Artificial Intelligence Models for Cardiomyopathy Detection in Obstetric Patients: A Multisite International Study.
Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
37 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiomyopathy significantly impacts maternal health worldwide. Artificial intelligence-enabled electrocardiograms (AI-ECG) have shown effectiveness in detecting left ventricular systolic dysfunction (LVSD) in adults and limited obstetric cohorts; however, broad validation in diverse obstetric populations remains unexplored.
objectivesOur primary objective was to validate the performance of 2 versions of an AI-ECG model for detecting cardiomyopathy in an international obstetric cohort.
methodsWe conducted a retrospective, multisite study of pregnant or postpartum women (≤12 months) across 11 institutions in 4 countries (Israel, Nigeria, Pakistan, and the United States). Two AI-ECG models (Food and Drug Administration-cleared v2.5.0 and refined v3.0.0) were evaluated against echocardiography. LVSD was defined as left ventricular ejection fraction ≤40% in keeping with the model's training target. Model performance was assessed by area under the receiver-operating characteristic curve (AUROC).
resultsWe included 1,820 women with paired ECG-echocardiogram data, of whom 7.3% had LVSD. Model 1 (v2.5.0) demonstrated strong discriminative performance with an AUROC of 0.865, sensitivity of 46.1%, and negative predictive value of 95.8%. Model 2 (v3.0.0) showed significantly improved performance, with an AUROC of 0.925, sensitivity of 64.1%, and negative predictive value of 97.2% (P < 0.001) for LVSD detection.
conclusionsAI-ECG models demonstrated strong external validity in a global obstetric cohort, with significantly improved performance in the refined model trained on expanded data. These findings support the potential role of AI-ECG for a scalable LVSD screening tool in obstetric populations, while underscoring the importance of population-specific validation prior to clinical implementation.
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.