In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 11 ( 2021-11-29), p. e0260195-
Abstract:
Noncompaction 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 results Forty-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/m 2 , P = 0.034), LV end-diastolic and end-systolic volumes ( P 〈 0.001), E/e’ (12.2±8.68 vs. 7.69±3.13, P = 0.034), and decreased LV ejection fraction (40.7±8.71 vs. 58.9±8.76%, P 〈 0.001) when compared to patients without RBR. Also, patients with RBR presented a significant decrease of global longitudinal, radial, and circumferential strain. When ML model based on a random forest algorithm and a neural network model was applied, it found that twist, NC/C, torsion, LV ejection fraction, and diastolic dysfunction are the strongest predictors to RBR with accuracy, sensitivity, specificity, area under the curve of 0.93, 0.99, 0.80, and 0.88, respectively. Conclusion In 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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0260195
DOI:
10.1371/journal.pone.0260195.g001
DOI:
10.1371/journal.pone.0260195.g002
DOI:
10.1371/journal.pone.0260195.g003
DOI:
10.1371/journal.pone.0260195.g004
DOI:
10.1371/journal.pone.0260195.g005
DOI:
10.1371/journal.pone.0260195.t001
DOI:
10.1371/journal.pone.0260195.t002
DOI:
10.1371/journal.pone.0260195.s001
DOI:
10.1371/journal.pone.0260195.r001
DOI:
10.1371/journal.pone.0260195.r002
DOI:
10.1371/journal.pone.0260195.r003
DOI:
10.1371/journal.pone.0260195.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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