PLoS ONE (Jan 2021)

A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

  • Marcelo Dantas Tavares de Melo,
  • Jose de Arimatéia Batista Araujo-Filho,
  • José Raimundo Barbosa,
  • Camila Rocon,
  • Carlos Danilo Miranda Regis,
  • Alex Dos Santos Felix,
  • Roberto Kalil Filho,
  • Edimar Alcides Bocchi,
  • Ludhmila Abrahão Hajjar,
  • Mahdi Tabassian,
  • Jan D'hooge,
  • Vera Maria Cury Salemi

DOI
https://doi.org/10.1371/journal.pone.0260195
Journal volume & issue
Vol. 16, no. 11
p. e0260195

Abstract

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AimsNoncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based model.Methods and resultsForty-nine outpatients with NCC diagnosis by echocardiography and magnetic resonance imaging (21 men, 42.8±14.8 years) were included. A comprehensive echocardiogram was performed. The layer-specific strain was analyzed from the apical two-, three, four-chamber views, short axis, and focused right ventricle views using 2D echocardiography (2DE) software. RBR was present in 44.9% of patients, and this group presented increased LV mass indexed (118±43.4 vs. 94.1±27.1g/m2, P = 0.034), LV end-diastolic and end-systolic volumes (PConclusionIn this study, a random forest algorithm was capable of selecting the best echocardiographic predictors to RBR pattern in NCC patients, which was consistent with worse systolic, diastolic, and myocardium deformation indices. Prospective studies are warranted to evaluate the role of this tool for NCC risk stratification.