Sistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSPBRITO, Bruno Oliveira de FigueiredoATTIA, Zachi I.MARTINS, Larissa Natany A.PEREL, PabloNUNES, Maria Carmo P.SABINO, Ester CerdeiraCARDOSO, Clareci SilvaFERREIRA, Ariela MotaGOMES, Paulo R.RIBEIRO, Antonio Luiz PinhoLOPEZ-JIMENEZ, Francisco2022-02-242022-02-242021PLOS NEGLECTED TROPICAL DISEASES, v.15, n.12, article ID e0009974, 16p, 20211935-2735https://observatorio.fm.usp.br/handle/OPI/44453BackgroundLeft 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. ObjectiveTo 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 findingsThis is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named Sao 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. ConclusionThe AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD. Author summaryChagas disease (ChD) is caused by the protozoan parasite Trypanosoma cruzi and continues to be a health problem despite the control of its transmission. ChD is a heterogeneous condition with a wide variation in its clinical course and prognosis. The majority (60%-70%) of infected individuals remain asymptomatic throughout life. Although some develop only conduction defects and mild segmental wall motion abnormalities, others develop severe symptoms of heart failure (HF), thromboembolic phenomena, and life threatening ventricular arrhythmias. HF is one of major causes of the death of patients with ChD. There is some evidence on effective drugs against the parasite in the chronic form of the disease capable of preventing long-term adverse outcomes, but it is still limited. However low-cost medications are able to reduce mortality and improve the quality of life of patients with HF. Because of the lack of tertiary care facilities outside urban centers, an automatic diagnostic tool based on the ECG, which is a relatively simple exam without requiring human interpretation, would improve the capacity to recognize HF. Recently, digital signals of the electrocardiogram were recognized by Artificial Intelligence (AI) and associated with an excellent accuracy for HF in the general population. Our results demonstrate that AI-ECG could ensure a rapid recognition of HF in patients who require a referral to a cardiologist and the use of disease-modifying drugs. AI can be used as a powerful public heath tool, it can transform the lives of 6 million patients with ChD worldwide, and it may well have a formidable impact on patient management and prognosis.engopenAccessbrain natriuretic peptident-probnpassociationmanagementdiagnosismortalityqrsarticleCopyright PUBLIC LIBRARY SCIENCE10.1371/journal.pntd.0009974Infectious DiseasesParasitologyTropical Medicine