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

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

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.

Chagas disease (ChD) is caused by the protozoan parasite Trypanosoma cruzi and continues to be a health problem despite the control of its transmission. The World Health Organization (WHO) estimates that approximately six million people have been infected, particularly in Latin America, where the disease is endemic [1,2]. Left ventricular systolic dysfunction (LVSD) is the most important predictor of mortality in Chagas Cardiomyopathy (ChCM). The treatment of ChCM is based on relatively low-cost drugs, which can improve symptoms and survival [2]. Unfortunately, the diagnosis of LVSD requires advanced diagnostic testing, such as echocardiography (echo), computer tomography, or magnetic resonance, none of which is readily accessible to most patients with ChD in most endemic areas.

Materials and methods
Study Design

This is a cross-sectional study of ECG obtained from the second wave of a large cohort of patients with Chagas disease from an endemic area, called the SaMi-Trop Study, which is described elsewhere [13]. The SaMi-Trop consists of a network of collaborating scientists in the Brazilian States of Minas Gerais and São Paulo and has been established to develop and conduct research projects on ChD. The SaMi-Trop study is a prospective cohort begun in 2013 which selected patients with ChD in 21 municipalities in the northern part of the state of Minas Gerais, where the prevalence of patients with chronic ChCM was expected to be high [13]. The cohort was established by using patients under the care of the Telehealth Network of Minas Gerais, a program designed to support primary care in Minas Gerais, Brazil. Eligible patients were selected based on the ECG results performed in 2011–2012 by the Telehealth Network, henceforth called index ECG.

Among the 1,304 participants of this study, the median age was 60 (51–69), of which 872 (67%) were women. There were 93 (7.1%) individuals with LVSD. NT-proBNP was high in 148 (11.3%) individuals of the study population. Most of the population presented major ECG abnormalities (59.5%). Baseline characteristics of the study participants are shown in Table 1.

This study describes the diagnostic performance of an AI-enabled ECG algorithm to detect LVSD in patients with ChD. The AI algorithm presented a high-level accuracy to recognize LVSD, together with an excellent negative predictive value, suggesting a potential role to screen (and rule out) for LVSD in this population. The incorporation of readily available information, such as sex and QRS duration, did improve the performance of the algorithm. The use of NT-proBNP level can significantly improve the AUC, the accuracy, and the specificity. The model with the addition of NT-proBNP and QRS duration ≥ 120ms in the evaluation of the same individual proved to be insignificant.

Citation: Brito BOdF, Attia ZI, Martins LNA, Perel P, Nunes MCP, Sabino EC, et al. (2021) Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients–The SaMi-Trop cohort. PLoS Negl Trop Dis 15(12): e0009974. https://doi.org/10.1371/journal.pntd.0009974

Editor: Kathryn Jones, Baylor College of Medicine, UNITED STATES

Received: May 12, 2021; Accepted: November 3, 2021; Published: December 6, 2021

Copyright: © 2021 Brito et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All "Artificial intelligenceand ECG of Chagas disease SaMi-Trop Cohort" data are already available in the following public repository: https://github.com/samitrop/AI-ECG-Chagas.

Funding: The SaMi-Trop study is supported by the National Institute of Health - NIH (www.nih.gov) grant numbers: P50 AI098461-02 and U19AI098461-06. Dr ALPR is supported in part by CNPq (310679/2016-8 and 465518/2014-1) and by FAPEMIG (PPM-00428-17 and RED-00081-16). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and some authors of this manuscript have the following competing interests: Mayo Clinic has licensed the underlying technology to EKO, a maker of digital stethoscopes with embedded ECG electrodes. Mayo Clinic may receive financial benefit from the use of this technology, but at no point will Mayo Clinic benefit financially from its use for the care of patients at Mayo Clinic. F.L.J. and Z.I.A. may also receive financial benefit from this agreement. There is a patent filing covering some of the technology described in this manuscript (USPO application WO2019070978A1).

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