Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study

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Arquivos
art_ROCON_Biventricular_imaging_markers_to_predict_outcomes_in_noncompaction_2020.PDF.pdf (2.91 MB)
Citações na Scopus
9
Tipo de produção
article
Data de publicação
2020
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
WILEY PERIODICALS, INC
Autores
TABASSIAN, Mahdi
D'HOOGE, Jan
Citação
ESC HEART FAILURE, v.7, n.5, p.2431-2439, 2020
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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.
Palavras-chave
Cardiomyopathy, Echocardiography, Follow-up, Machine learning, Magnetic resonance imaging, Non-compaction
URI
https://observatorio.fm.usp.br/handle/OPI/38303
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Coleções
Artigos e Materiais de Revistas Científicas - FM/MCP
Artigos e Materiais de Revistas Científicas - HC/InCor
Artigos e Materiais de Revistas Científicas - LIM/11
Artigos e Materiais de Revistas Científicas - LIM/65
Artigos e Materiais de Revistas Científicas - ODS/03