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
Abstract: Chagas disease (ChD) is a prevalent health issue caused by the Trypanosoma cruzi parasite, particularly affecting Latin America. Left ventricular systolic dysfunction (LVSD) is a critical predictor of mortality in Chagas Cardiomyopathy (ChCM), and its treatment with low-cost drugs has shown positive outcomes. However, the diagnosis of LVSD typically requires advanced and inaccessible diagnostic testing in endemic areas. This study aimed to evaluate the accuracy of an artificial intelligence (AI)-enabled ECG algorithm in detecting LVSD specifically in ChD patients. The results demonstrate that the AI algorithm exhibits high accuracy and negative predictive value, suggesting its potential as a screening tool for LVSD in this population. Incorporating readily available information such as sex and QRS duration further enhances the algorithm's performance. Additionally, the use of NT-proBNP level significantly improves the accuracy, specificity, and area under the curve. The combination of NT-proBNP and QRS duration ≥ 120ms did not yield significant improvements. This research contributes to the understanding of AI applications in diagnosing LVSD in Chagas disease patients.
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 intelligence and 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).
