Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort.

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Bruno Oliveira de Figueiredo Brito, Zachi I Attia, Larissa Natany A Martins, Pablo Perel, Maria Carmo P Nunes, Ester Cerdeira Sabino, Clareci Silva Cardoso, Ariela Mota Ferreira, Paulo R Gomes, Antonio Luiz Pinho Ribeiro, Francisco Lopez-Jimenez
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2021
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic; sami
• Online Access: https://doi.org/10.1371/journal.pntd.0009974; https://doaj.org/article/4a9e105afca0448e9ce7623766076c1f

Background Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested. Objective To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%. Methodology/principal findings This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms. Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3-128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874. Conclusion The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.