ArticleJACC. Advances2026
AI-ECG for Detecting Left Ventricular Systolic Dysfunction in Chagas Disease: Diagnostic and Prognostic Value.
Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02646943 (Longitudinal Study of Patients With Chronic Chagas Cardiomyopathy in Brazil), which is not on this map. Not yet cited in PubMed.
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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.
Longitudinal Study of Patients With Chronic Chagas Cardiomyopathy in Brazil (SaMi_Trop Project)
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15 authors.
Funding
Abstract
backgroundLeft ventricular systolic dysfunction (LVSD) is the main predictor of mortality in Chagas disease (ChD). Although LVSD can be treated with affordable medications, its diagnosis relies on cardiac imaging, which is often unavailable in resource-limited settings.
objectivesThe objective of the study was to evaluate an artificial intelligence-enabled electrocardiogram (AI-ECG) for detecting LVSD and predicting mortality and incident LVSD in ChD.
methodsA previously developed AI-ECG LVSD model was fine-tuned in an external ChD sample and applied to SaMi-Trop, a Brazilian prospective ChD cohort. Diagnostic performance for LVSD, confirmed by echocardiography, was compared with N-terminal pro-B-type natriuretic peptide (NT-proBNP). Prognostic performance for all-cause mortality at 2 and 9 years was assessed using Cox models, and incident LVSD over 7 years using log-binomial models.
resultsAmong 1,304 participants, AI-ECG showed high accuracy for LVSD detection (area under the receiver operating characteristic curve: 0.89; 95% CI: 0.85-0.93), similar to NT-proBNP (area under the receiver operating characteristic curve: 0.90; 95% CI: 0.87-0.93; P = 0.52). Among 1,547 patients with Chagas cardiomyopathy, AI-ECG predicted all-cause mortality at 2 and 9 years with performance comparable to NT-proBNP (9-year C-index: 0.78; 95% CI: 0.74-0.82 vs 0.77; 95% CI: 0.74-0.79). AI-ECG could replace NT-proBNP in an established ChD mortality risk score with minor loss of accuracy. Incident LVSD occurred in 8.4% over 7.3 years, and AI-ECG predicted incident LVSD with performance similar to NT-proBNP.
conclusionsA fine-tuned AI-ECG model showed high accuracy for LVSD detection and was independently associated with long-term mortality and incident LVSD. Despite slightly lower performance than NT-proBNP, it may serve as a substitute in settings where NT-proBNP is unavailable. (Longitudinal Study of Patients With Chronic Chagas Cardiomyopathy in Brazil [SaMi_Trop Project] [SaMi-Trop]; NCT02646943).
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