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

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dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorBRITO, Bruno Oliveira de Figueiredo
dc.contributor.authorATTIA, Zachi I.
dc.contributor.authorMARTINS, Larissa Natany A.
dc.contributor.authorPEREL, Pablo
dc.contributor.authorNUNES, Maria Carmo P.
dc.contributor.authorSABINO, Ester Cerdeira
dc.contributor.authorCARDOSO, Clareci Silva
dc.contributor.authorFERREIRA, Ariela Mota
dc.contributor.authorGOMES, Paulo R.
dc.contributor.authorRIBEIRO, Antonio Luiz Pinho
dc.contributor.authorLOPEZ-JIMENEZ, Francisco
dc.date.accessioned2022-02-24T17:16:06Z
dc.date.available2022-02-24T17:16:06Z
dc.date.issued2021
dc.description.abstractBackgroundLeft 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.eng
dc.description.indexMEDLINEeng
dc.description.sponsorshipNational Institute of Health - NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P50 AI098461-02, U19AI098461-06]
dc.description.sponsorshipCNPqConselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) [310679/2016-8, 465518/2014-1]
dc.description.sponsorshipFAPEMIGFundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) [PPM-00428-17, RED-00081-16]
dc.identifier.citationPLOS NEGLECTED TROPICAL DISEASES, v.15, n.12, article ID e0009974, 16p, 2021
dc.identifier.doi10.1371/journal.pntd.0009974
dc.identifier.issn1935-2735
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/44453
dc.language.isoeng
dc.publisherPUBLIC LIBRARY SCIENCEeng
dc.relation.ispartofPlos Neglected Tropical Diseases
dc.rightsopenAccesseng
dc.rights.holderCopyright PUBLIC LIBRARY SCIENCEeng
dc.subject.otherbrain natriuretic peptideeng
dc.subject.othernt-probnpeng
dc.subject.otherassociationeng
dc.subject.othermanagementeng
dc.subject.otherdiagnosiseng
dc.subject.othermortalityeng
dc.subject.otherqrseng
dc.subject.wosInfectious Diseaseseng
dc.subject.wosParasitologyeng
dc.subject.wosTropical Medicineeng
dc.titleLeft ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohorteng
dc.typearticleeng
dc.type.categoryoriginal articleeng
dc.type.versionpublishedVersioneng
dspace.entity.typePublication
hcfmusp.affiliation.countryEstados Unidos
hcfmusp.affiliation.countryisous
hcfmusp.author.externalBRITO, Bruno Oliveira de Figueiredo:Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
hcfmusp.author.externalATTIA, Zachi I.:Mayo Clin, Dept Cardiovasc Med, Rochester, MN 55905 USA
hcfmusp.author.externalMARTINS, Larissa Natany A.:Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Inst Ciencia Exatas, Dept Stat, Belo Horizonte, MG, Brazil
hcfmusp.author.externalPEREL, Pablo:Mayo Clin, Dept Cardiovasc Med, Rochester, MN 55905 USA
hcfmusp.author.externalNUNES, Maria Carmo P.:Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
hcfmusp.author.externalCARDOSO, Clareci Silva:Univ Fed Sao Joao del Rei, Divinopolis, Brazil
hcfmusp.author.externalFERREIRA, Ariela Mota:Univ Estadual Montes Claros, Grad Program Hlth Sci, Montes Claros, MG, Brazil
hcfmusp.author.externalGOMES, Paulo R.:Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil
hcfmusp.author.externalRIBEIRO, Antonio Luiz Pinho:Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil
hcfmusp.author.externalLOPEZ-JIMENEZ, Francisco:Mayo Clin, Dept Cardiovasc Med, Rochester, MN 55905 USA
hcfmusp.citation.scopus4
hcfmusp.contributor.author-fmusphcESTER CERDEIRA SABINO
hcfmusp.description.articlenumbere0009974
hcfmusp.description.issue12
hcfmusp.description.volume15
hcfmusp.origemWOS
hcfmusp.origem.pubmed34871321
hcfmusp.origem.scopus2-s2.0-85122532262
hcfmusp.origem.wosWOS:000727340800005
hcfmusp.publisher.citySAN FRANCISCOeng
hcfmusp.publisher.countryUSAeng
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