Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients–The SaMi-Trop cohort
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...
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| Published in | PLoS neglected tropical diseases Vol. 15; no. 12; p. e0009974 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.12.2021
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1935-2735 1935-2727 1935-2735 |
| DOI | 10.1371/journal.pntd.0009974 |
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| Abstract | 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.
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%.
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.
The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD. |
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| AbstractList | 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 [less than or equal to] 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 [greater than or equal to] 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 [greater than or equal to] 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. 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. 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.BACKGROUNDLeft 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.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%.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%.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.METHODOLOGY/PRINCIPAL FINDINGSThis 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.The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.CONCLUSIONThe AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD. BackgroundLeft 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 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.ConclusionThe AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD. 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. 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. To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram [less than or equal to] 40%. 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 [greater than or equal to] 120ms. The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD. 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. 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%. 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. The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD. Chagas 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. |
| Audience | Academic |
| Author | Attia, Zachi I. Martins, Larissa Natany A. Lopez-Jimenez, Francisco Sabino, Ester Cerdeira Luiz Pinho Ribeiro, Antonio Brito, Bruno Oliveira de Figueiredo Ferreira, Ariela Mota Gomes, Paulo R. Nunes, Maria Carmo P. Perel, Pablo Cardoso, Clareci Silva |
| AuthorAffiliation | 6 Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil 4 Department of Statistics, Instituto de Ciência Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil 7 Federal University of São João del-Rei, Divinópolis, Brazil 8 Graduate Program in Health Sciences, State University of Montes Claros, Montes Claros, Minas Gerais, Brazil 2 Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States of America 5 London School of Hygiene and Tropical Medicine, London, United Kingdom Baylor College of Medicine, UNITED STATES 1 Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil 3 Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil |
| AuthorAffiliation_xml | – name: 2 Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States of America – name: 6 Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil – name: Baylor College of Medicine, UNITED STATES – name: 5 London School of Hygiene and Tropical Medicine, London, United Kingdom – name: 1 Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil – name: 4 Department of Statistics, Instituto de Ciência Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil – name: 7 Federal University of São João del-Rei, Divinópolis, Brazil – name: 3 Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil – name: 8 Graduate Program in Health Sciences, State University of Montes Claros, Montes Claros, Minas Gerais, Brazil |
| Author_xml | – sequence: 1 givenname: Bruno Oliveira de Figueiredo orcidid: 0000-0002-3710-006X surname: Brito fullname: Brito, Bruno Oliveira de Figueiredo – sequence: 2 givenname: Zachi I. surname: Attia fullname: Attia, Zachi I. – sequence: 3 givenname: Larissa Natany A. orcidid: 0000-0003-2464-6343 surname: Martins fullname: Martins, Larissa Natany A. – sequence: 4 givenname: Pablo orcidid: 0000-0002-2342-301X surname: Perel fullname: Perel, Pablo – sequence: 5 givenname: Maria Carmo P. surname: Nunes fullname: Nunes, Maria Carmo P. – sequence: 6 givenname: Ester Cerdeira orcidid: 0000-0003-2623-5126 surname: Sabino fullname: Sabino, Ester Cerdeira – sequence: 7 givenname: Clareci Silva orcidid: 0000-0003-0689-1644 surname: Cardoso fullname: Cardoso, Clareci Silva – sequence: 8 givenname: Ariela Mota orcidid: 0000-0002-2315-5318 surname: Ferreira fullname: Ferreira, Ariela Mota – sequence: 9 givenname: Paulo R. orcidid: 0000-0002-7949-1812 surname: Gomes fullname: Gomes, Paulo R. – sequence: 10 givenname: Antonio orcidid: 0000-0002-2740-0042 surname: Luiz Pinho Ribeiro fullname: Luiz Pinho Ribeiro, Antonio – sequence: 11 givenname: Francisco surname: Lopez-Jimenez fullname: Lopez-Jimenez, Francisco |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34871321$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1161/JAHA.119.014176 10.1016/j.ejheart.2008.07.014 10.1016/S0140-6736(02)09638-1 10.1016/j.ijcard.2005.05.048 10.1016/j.jacc.2018.08.1038 10.1056/NEJMoa053241 10.1161/CIRCHEARTFAILURE.117.004361 10.1016/j.jacc.2013.05.046 10.1136/heartjnl-2017-312869 10.1590/0037-8682-0184-2018 10.1016/j.jelectrocard.2011.04.011 10.1038/s41591-018-0306-1 10.1590/S0074-02761922000100001 10.1111/j.1540-8159.2000.tb07076.x 10.1016/j.amjcard.2017.10.020 10.1093/eurheartj/ehw128 10.1056/NEJMoa1507574 10.1161/JAHA.113.000632 10.5334/gh.484 10.1038/nrcardio.2012.109 10.1136/bmjopen-2016-011181 10.1016/j.echo.2017.10.019 10.1016/j.jacc.2017.10.044 10.1371/journal.pntd.0002078 10.1016/j.jelectrocard.2018.08.031 10.1016/j.ahj.2006.12.017 10.1038/s41591-018-0240-2 10.1161/01.CIR.0000079174.13444.9C |
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| Notes | new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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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| References | A Minchole (pntd.0009974.ref009) 2019; 25 H Acquatella (pntd.0009974.ref015) 2018; 31 AL Ribeiro (pntd.0009974.ref024) 2013; 7 AL Ribeiro (pntd.0009974.ref023) 2006; 109 AL Ribeiro (pntd.0009974.ref002) 2012; 9 FA Botoni (pntd.0009974.ref029) 2007; 153 K Thygesen (pntd.0009974.ref026) 2018; 72 AC Diamantino (pntd.0009974.ref031) 2020 A Rassi (pntd.0009974.ref020) 2006; 355 AL Ribeiro (pntd.0009974.ref021) 2000; 23 LE Echeverria (pntd.0009974.ref030) 2020; 15 BR Nascimento (pntd.0009974.ref005) 2012; 45 CA Morillo (pntd.0009974.ref032) 2015; 373 L Shen (pntd.0009974.ref027) 2017; 10 BOdF Brito (pntd.0009974.ref019) 2018; 51 DN Moraes (pntd.0009974.ref007) 2018; 121 C Di Lorenzo Oliveira (pntd.0009974.ref017) 2020; 9 CW Yancy (pntd.0009974.ref011) 2013; 62 CS Cardoso (pntd.0009974.ref013) 2016; 6 ZI Attia (pntd.0009974.ref010) 2019; 25 G Salles (pntd.0009974.ref006) 2003; 108 C Chagas (pntd.0009974.ref003) 1922; 14 MCP Nunes (pntd.0009974.ref018) 2018; 138 AL Ribeiro (pntd.0009974.ref004) 2014; 3 BOF Brito (pntd.0009974.ref008) 2018; 51 AL Ribeiro (pntd.0009974.ref022) 2002; 360 W Nadruz (pntd.0009974.ref028) 2018; 104 P Ponikowski (pntd.0009974.ref012) 2016; 37 A Maisel (pntd.0009974.ref014) 2008; 10 CY Liu (pntd.0009974.ref025) 2017; 70 MC Nunes (pntd.0009974.ref001) 2013; 62 HW. PRCRB (pntd.0009974.ref016) 1982 35613089 - PLoS Negl Trop Dis. 2022 May 25;16(5):e0010488 |
| References_xml | – volume-title: The Minnesota Code Manual of Electrocardiographic Findings: Standards and Procedures for Measurement and Classification.: year: 1982 ident: pntd.0009974.ref016 – volume: 9 start-page: e014176 issue: 6 year: 2020 ident: pntd.0009974.ref017 article-title: Risk Score for Predicting 2-Year Mortality in Patients With Chagas Cardiomyopathy From Endemic Areas: SaMi-Trop Cohort Study. publication-title: Journal of the American Heart Association doi: 10.1161/JAHA.119.014176 – volume: 10 start-page: 824 issue: 9 year: 2008 ident: pntd.0009974.ref014 article-title: State of the art: using natriuretic peptide levels in clinical practice. publication-title: European journal of heart failure doi: 10.1016/j.ejheart.2008.07.014 – volume: 360 start-page: 461 issue: 9331 year: 2002 ident: pntd.0009974.ref022 article-title: Brain natriuretic peptide and left ventricular dysfunction in Chagas’ disease publication-title: Lancet doi: 10.1016/S0140-6736(02)09638-1 – volume: 109 start-page: 34 issue: 1 year: 2006 ident: pntd.0009974.ref023 article-title: Brain natriuretic peptide based strategy to detect left ventricular dysfunction in Chagas disease: a comparison with the conventional approach. publication-title: International journal of cardiology doi: 10.1016/j.ijcard.2005.05.048 – volume: 72 start-page: 2231 issue: 18 year: 2018 ident: pntd.0009974.ref026 article-title: Fourth Universal Definition of Myocardial Infarction (2018). publication-title: Journal of the American College of Cardiology doi: 10.1016/j.jacc.2018.08.1038 – volume: 355 start-page: 799 issue: 8 year: 2006 ident: pntd.0009974.ref020 article-title: Development and validation of a risk score for predicting death in Chagas’ heart disease publication-title: The New England journal of medicine doi: 10.1056/NEJMoa053241 – volume: 10 issue: 11 year: 2017 ident: pntd.0009974.ref027 article-title: Contemporary Characteristics and Outcomes in Chagasic Heart Failure Compared With Other Nonischemic and Ischemic Cardiomyopathy. publication-title: Circulation Heart failure doi: 10.1161/CIRCHEARTFAILURE.117.004361 – volume: 62 start-page: 767 issue: 9 year: 2013 ident: pntd.0009974.ref001 article-title: Chagas disease: an overview of clinical and epidemiological aspects publication-title: Journal of the American College of Cardiology doi: 10.1016/j.jacc.2013.05.046 – volume: 104 start-page: 1522 issue: 18 year: 2018 ident: pntd.0009974.ref028 article-title: Temporal trends in the contribution of Chagas cardiomyopathy to mortality among patients with heart failure publication-title: Heart doi: 10.1136/heartjnl-2017-312869 – volume: 51 start-page: 570 issue: 5 year: 2018 ident: pntd.0009974.ref008 article-title: Electrocardiogram in Chagas disease. publication-title: Revista da Sociedade Brasileira de Medicina Tropical doi: 10.1590/0037-8682-0184-2018 – volume: 45 start-page: 43 issue: 1 year: 2012 ident: pntd.0009974.ref005 article-title: The prognostic significance of electrocardiographic changes in Chagas disease publication-title: Journal of electrocardiology doi: 10.1016/j.jelectrocard.2011.04.011 – volume: 25 start-page: 22 issue: 1 year: 2019 ident: pntd.0009974.ref009 article-title: Artificial intelligence for the electrocardiogram publication-title: Nature medicine doi: 10.1038/s41591-018-0306-1 – volume: 14 start-page: 5 issue: 1 year: 1922 ident: pntd.0009974.ref003 article-title: Cardiac form of American Trypanosomiasis publication-title: Memorias do Instituto Oswaldo Cruz doi: 10.1590/S0074-02761922000100001 – volume: 23 start-page: 2014 issue: 11 Pt 2 year: 2000 ident: pntd.0009974.ref021 article-title: A narrow QRS does not predict a normal left ventricular function in Chagas’ disease. publication-title: Pacing and clinical electrophysiology: PACE. doi: 10.1111/j.1540-8159.2000.tb07076.x – volume: 121 start-page: 364 issue: 3 year: 2018 ident: pntd.0009974.ref007 article-title: Value of the Electrocardiographic (P Wave, T Wave, QRS) Axis as a Predictor of Mortality in 14 Years in a Population With a High Prevalence of Chagas Disease from the Bambui Cohort Study of Aging. publication-title: The American journal of cardiology doi: 10.1016/j.amjcard.2017.10.020 – start-page: e13686 year: 2020 ident: pntd.0009974.ref031 article-title: Impact of incorporating echocardiographic screening into a clinical prediction model to optimise utilisation of echocardiography in primary care publication-title: International journal of clinical practice – volume: 37 start-page: 2129 issue: 27 year: 2016 ident: pntd.0009974.ref012 article-title: 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. publication-title: European heart journal doi: 10.1093/eurheartj/ehw128 – volume: 373 start-page: 1295 issue: 14 year: 2015 ident: pntd.0009974.ref032 article-title: Randomized Trial of Benznidazole for Chronic Chagas’ Cardiomyopathy publication-title: The New England journal of medicine doi: 10.1056/NEJMoa1507574 – volume: 3 start-page: e000632 issue: 1 year: 2014 ident: pntd.0009974.ref004 article-title: Electrocardiographic abnormalities in elderly Chagas disease patients: 10-year follow-up of the Bambui Cohort Study of Aging publication-title: Journal of the American Heart Association doi: 10.1161/JAHA.113.000632 – volume: 15 start-page: 26 issue: 1 year: 2020 ident: pntd.0009974.ref030 article-title: WHF IASC Roadmap on Chagas Disease. publication-title: Global heart doi: 10.5334/gh.484 – volume: 9 start-page: 576 issue: 10 year: 2012 ident: pntd.0009974.ref002 article-title: Diagnosis and management of Chagas disease and cardiomyopathy publication-title: Nature reviews Cardiology doi: 10.1038/nrcardio.2012.109 – volume: 6 start-page: e011181 issue: 5 year: 2016 ident: pntd.0009974.ref013 article-title: Longitudinal study of patients with chronic Chagas cardiomyopathy in Brazil (SaMi-Trop project): a cohort profile. publication-title: BMJ open doi: 10.1136/bmjopen-2016-011181 – volume: 31 start-page: 3 issue: 1 year: 2018 ident: pntd.0009974.ref015 article-title: Recommendations for Multimodality Cardiac Imaging in Patients with Chagas Disease: A Report from the American Society of Echocardiography in Collaboration With the InterAmerican Association of Echocardiography (ECOSIAC) and the Cardiovascular Imaging Department of the Brazilian Society of Cardiology (DIC-SBC). publication-title: Journal of the American Society of Echocardiography: official publication of the American Society of Echocardiography doi: 10.1016/j.echo.2017.10.019 – volume: 70 start-page: 3102 issue: 25 year: 2017 ident: pntd.0009974.ref025 article-title: Association of Elevated NT-proBNP With Myocardial Fibrosis in the Multi-Ethnic Study of Atherosclerosis (MESA). publication-title: Journal of the American College of Cardiology doi: 10.1016/j.jacc.2017.10.044 – volume: 138 start-page: e169 issue: 12 year: 2018 ident: pntd.0009974.ref018 article-title: Chagas Cardiomyopathy: An Update of Current Clinical Knowledge and Management: A Scientific Statement From the American Heart Association publication-title: Circulation – volume: 7 start-page: e2078 issue: 2 year: 2013 ident: pntd.0009974.ref024 article-title: Electrocardiographic abnormalities in Trypanosoma cruzi seropositive and seronegative former blood donors publication-title: PLoS neglected tropical diseases doi: 10.1371/journal.pntd.0002078 – volume: 51 start-page: 1039 issue: 6 year: 2018 ident: pntd.0009974.ref019 article-title: Association between typical electrocardiographic abnormalities and NT-proBNP elevation in a large cohort of patients with Chagas disease from endemic area publication-title: Journal of electrocardiology doi: 10.1016/j.jelectrocard.2018.08.031 – volume: 153 start-page: 544 issue: 4 year: 2007 ident: pntd.0009974.ref029 article-title: A randomized trial of carvedilol after renin-angiotensin system inhibition in chronic Chagas cardiomyopathy publication-title: American heart journal doi: 10.1016/j.ahj.2006.12.017 – volume: 25 start-page: 70 issue: 1 year: 2019 ident: pntd.0009974.ref010 article-title: Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram publication-title: Nature medicine doi: 10.1038/s41591-018-0240-2 – volume: 108 start-page: 305 issue: 3 year: 2003 ident: pntd.0009974.ref006 article-title: Prognostic value of QT interval parameters for mortality risk stratification in Chagas’ disease: results of a long-term follow-up study publication-title: Circulation doi: 10.1161/01.CIR.0000079174.13444.9C – volume: 62 start-page: e147 issue: 16 year: 2013 ident: pntd.0009974.ref011 article-title: 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines publication-title: Journal of the American College of Cardiology – reference: 35613089 - PLoS Negl Trop Dis. 2022 May 25;16(5):e0010488 |
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| Snippet | Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and... Background Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve... Chagas 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... BackgroundLeft ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms... Background Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve... |
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| SubjectTerms | Abnormalities Accuracy Aged Algorithms Artificial Intelligence Brazil Chagas disease Chagas Disease - complications Chagas Disease - physiopathology Cohorts Complications and side effects Computer and Information Sciences Cross-Sectional Studies Diagnosis Diseases Echocardiography Ejection fraction EKG Electrocardiogram Electrocardiography Electrocardiography - instrumentation Electrocardiography - methods Enzymes Female Health aspects Heart Heart diseases Heart rate Humans Immunoassay Left ventricular function Male Males Malformations Medical prognosis Medical sciences Medicine Medicine and Health Sciences Middle Aged Patients Physical Sciences Plasma levels Primary care Public health Questionnaires Research and Analysis Methods Research facilities Risk factors Sex Signs and symptoms Specificity Stroke Volume Symptoms Telemedicine Tropical climate Tropical diseases Variables Vector-borne diseases Ventricle Ventricular Dysfunction, Left - diagnosis Ventricular Dysfunction, Left - etiology Ventricular Dysfunction, Left - physiopathology Ventricular Function, Left Women |
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| Title | Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients–The SaMi-Trop cohort |
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