Opportunistic Screening for Chagas Disease Using an Artificial Intelligence-Enabled ECG: Prospective Evaluation of Feasibility and Diagnostic Accuracy.

Résumé fourni par la source

BACKGROUND: Chagas disease (ChD), a neglected cardiovascular condition, affects 7.5 to 10.5 million people worldwide. Opportunistic screening during routine electrocardiography may allow earlier detection of unrecognized infections, enabling timely antiparasitic therapy or optimized cardiac care. This study prospectively evaluated the feasibility and diagnostic accuracy of an artificial intelligence (AI)-enabled ECG model enriched with 3 epidemiological questions (AI-ECG-EPI model) as an opportunistic screening for ChD in endemic regions of Brazil. METHODS: We conducted a prospective study embedded in the Telehealth Network of Minas Gerais, Brazil, across 107 municipalities in hyperendemic and endemic regions. From October 2023 to September 2024, adults undergoing routine tele-ECGs were eligible. The primary exposure was the output of the AI-ECG-EPI model: all positive individuals and a 3:1 sample of negative individuals were invited for serology (index test-dependent sampling), the reference standard. Weighted analyses (inverse probability) accounted for this sampling design. Diagnostic metrics included sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristics curve, and area under the precision recall curve. The AI-ECG-EPI model was compared with both an epidemiological questions model and an AI ECG-only model. RESULTS: <0.001). Precision recall curves showed higher precision across most recall levels. Exploratory analysis suggested improved risk reclassification (net reclassification improvement, 0.503). Performance was higher in women and in individuals with Chagas cardiomyopathy. CONCLUSIONS: In this community-based study, an AI-ECG-EPI model integrated into a public telecardiology network enabled feasible opportunistic screening for ChD in primary care. It showed acceptable diagnostic accuracy, with higher discrimination than epidemiological questions and AI ECG-only models, and identified many previously undiagnosed patients in endemic regions, supporting its potential role in scalable screening strategies. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02646943.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.