MARCELO DANTAS TAVARES DE MELO

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Assunto: cardiac magnetic-resonance ×

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  • article 1 Citação(ões) na Scopus
    Position Statement on the Use of Myocardial Strain in Cardiology Routines by the Brazilian Society of Cardiology's Department Of Cardiovascular Imaging-2023
    (2023) ALMEIDA, Andre Luiz Cerqueira; MELO, Marcelo Dantas Tavares de; BIHAN, David Costa de Souza Le; VIEIRA, Marcelo Luiz Campos; PENA, Jose Luiz Barros; CASTILLO, Jose Maria Del; ABENSUR, Henry; HORTEGAL, Renato de Aguiar; OTTO, Maria Estefania Bosco; PIVETA, Rafael Bonafim; DANTAS, Maria Rosa; ASSEF, Jorge Eduardo; BECK, Adenalva Lima de Souza; SANTO, Thais Harada Campos Espirito; SILVA, Tonnison de Oliveira; SALEMI, Vera Maria Cury; ROCON, Camila; LIMA, Marcio Silva Miguel; BARBERATO, Silvio Henrique; RODRIGUES, Ana Clara; RABSCHKOWISKY, Arnaldo; FROTA, Daniela do Carmo Rassi; GRIPP, Eliza de Almeida; BARRETTO, Rodrigo Bellio de Mattos; SILVA, Sandra Marques e; CAUDURO, Sanderson Antonio; PINHEIRO, Aurelio Carvalho; ARAUJO, Salustiano Pereira de; TRESSINO, Cintia Galhardo; SILVA, Carlos Eduardo Suaide; MONACO, Claudia Gianini; PAIVA, Marcelo Goulart; FISHER, Claudio Henrique; ALVES, Marco Stephan Lofrano; GRAU, Claudia R. Pinheiro de Castro; SANTOS, Maria Veronica Camara dos; GUIMARAES, Isabel Cristina Britto; MORHY, Samira Saady; LEAL, Gabriela Nunes; SOARES, Andressa Mussi; CRUZ, Cecilia Beatriz Bittencourt Viana; GUIMARAES FILHO, Fabio Villaca; ASSUNCAO, Bruna Morhy Borges Leal; FERNANDES, Rafael Modesto; SARAIVA, Roberto Magalhaes; TSUTSUI, Jeane Mike; SOARES, Fabio Luis de Jesus; FALCAO, Sandra Nivea dos Reis Saraiva; HOTTA, Viviane Tiemi; ARMSTRONG, Anderson da Costa; HYGIDIO, Daniel de Andrade; MIGLIORANZA, Marcelo Haertel; CAMAROZANO, Ana Cristina; LOPES, Marly Maria Uellendahl; CERCI, Rodrigo Julio; SIQUEIRA, Maria Eduarda Menezes de; TORREAO, Jorge Andion; ROCHITTE, Carlos Eduardo; FELIX, Alex
  • article 9 Citação(ões) na Scopus
    Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study
    (2020) ROCON, Camila; TABASSIAN, Mahdi; MELO, Marcelo Dantas Tavares de; ARAUJO FILHO, Jose Arimateia de; GRUPI, Cesar Jose; PARGA FILHO, Jose Rodrigues; BOCCHI, Edimar Alcides; D'HOOGE, Jan; SALEMI, Vera Maria Cury
    Aims Left ventricular non-compaction cardiomyopathy (LVNC) is a genetic heart disease, with heart failure, arrhythmias, and embolic events as main clinical manifestations. The goal of this study was to analyse a large set of echocardiographic (echo) and cardiac magnetic resonance imaging (CMRI) parameters using machine learning (ML) techniques to find imaging predictors of clinical outcomes in a long-term follow-up of LVNC patients. Methods and results Patients with echo and/or CMRI criteria of LVNC, followed from January 2011 to December 2017 in the heart failure section of a tertiary referral cardiologic hospital, were enrolled in a retrospective study. Two-dimensional colour Doppler echocardiography and subsequent CMRI were carried out. Twenty-four hour Holter monitoring was also performed in all patients. Death, cardiac transplantation, heart failure hospitalization, aborted sudden cardiac death, complex ventricular arrhythmias (sustained and non-sustained ventricular tachycardia), and embolisms (i.e. stroke, pulmonary thromboembolism and/or peripheral arterial embolism) were registered and were referred to as major adverse cardiovascular events (MACEs) in this study. Recruited for the study were 108 LVNC patients, aged 38.3 +/- 15.5 years, 48.1% men, diagnosed by echo and CMRI criteria. They were followed for 5.8 +/- 3.9 years, and MACEs were registered. CMRI and echo parameters were analysed via a supervised ML methodology. Forty-seven (43.5%) patients had at least one MACE. The best performance of imaging variables was achieved by combining four parameters: left ventricular (LV) ejection fraction (by CMRI), right ventricular (RV) end-systolic volume (by CMRI), RV systolic dysfunction (by echo), and RV lower diameter (by CMRI) with accuracy, sensitivity, and specificity rates of 75.5%, 77%, 75%, respectively. Conclusions Our findings show the importance of biventricular assessment to detect the severity of this cardiomyopathy and to plan for early clinical intervention. In addition, this study shows that even patients with normal LV function and negative late gadolinium enhancement had MACE. ML is a promising tool for analysing a large set of parameters to stratify and predict prognosis in LVNC patients.