Trial reportNature medicine2024
Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.
Trial report in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05438576 (Screening for Peripartum Cardiomyopathies Using Artificial Intelligence), which is not on this map. Cited by 32 papers.
What it found
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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.
Screening for Peripartum Cardiomyopathies Using Artificial Intelligence (SPEC-AI) in Nigeria
Who cites it
32 citing papers in PubMed.
- Structural Heart Interventions in Women with Congenital Heart Disease.Current cardiology reports · 2026Review
- Cardiac Biomarkers and Pregnancy-Associated Cardiovascular Disease.Current cardiology reports · 2026Review
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Human-AI Collaboration in Healthcare: A Scoping Review.NPJ digital medicine · 2026Article
- Smarter FoCUS: AI-guided focused cardiac ultrasound enables novice detection of left ventricular dysfunction.European heart journal. Digital health · 2026Article
- Artificial intelligence electrocardiography for left ventricular systolic dysfunction demonstrates preserved performance across demographic training imbalances.European heart journal. Digital health · 2026Article
- ESC guidelines 2025 on cardiovascular disease and pregnancy: considerations and future perspectives.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Genotype-Phenotype Discordance in Cardiomyopathies: Pathophysiology, Clinical Expression, and Therapeutic Considerations.Health science reports · 2026Article
- Artificial intelligence-enhanced electrocardiogram for the detection of rheumatic heart disease.European heart journal. Digital health · 2026Article
- Federated clinical concept and disease semantic learning for congenital heart disease diagnosis.NPJ digital medicine · 2026Article
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- End-to-End Platform for Electrocardiogram Analysis and Model Fine-Tuning: Development and Validation Study.Journal of medical Internet research · 2026Article
- Clinical Effectiveness of an Artificial Intelligence-Based Prediction Model for Cardiac Arrest in General Ward-Admitted Patients: A Non-Randomized Controlled Trial.Diagnostics (Basel, Switzerland) · 2026Article
- Precision cardiovascular medicine: global imperative for equity through innovation.Frontiers in cardiovascular medicine · 2026Article
- Access to digital health technologies: personalized framework and global perspectives.Nature reviews. Cardiology · 2026Review
- The evolution of sampling in epidemiology: from classical probability models to AI-enhanced recruitment and active learning.Epidemiology and health · 2026Review
- Artificial intelligence-enabled electrocardiography from scientific research to clinical application.EMBO molecular medicine · 2026Review
- Artificial intelligence in early heart failure detection: from algorithms to bedside integration.Frontiers in cardiovascular medicine · 2026Article
- AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions.International journal of general medicine · 2026Review
- An interpretable deep learning model for first-trimester fetal cardiac screening.NPJ digital medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
38 authors.
Funding
Abstract
Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05-4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85-3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576.
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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.