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: Brito, Bruno Oliveira de Figueiredo, Attia, Zachi I, Martins, Larissa Natany A, Perel, Pablo, Nunes, Maria Carmo P, Sabino, Ester Cerdeira, Cardoso, Clareci Silva, Ferreira, Ariela Mota, Gomes, Paulo R, Luiz Pinho Ribeiro, Antonio, Lopez-Jimenez, Francisco
• Format: Article in Journal/Newspaper
• Language: English
• Published: PUBLIC LIBRARY SCIENCE 2021
• Subjects: sami
• Online Access: https://researchonline.lshtm.ac.uk/id/eprint/4664392/; https://researchonline.lshtm.ac.uk/id/eprint/4664392/1/Left%20ventricular%20systolic%20dysfunction%20predicted%20by%20artificial%20intelligence%20using%20the%20electrocardiogram%20in%20Chagas%20disease%20pat.pdf

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.