Key result
Machine learning predicts rigid body rotation in noncompaction cardiomyopathy with ~93% accuracy using LV mechanics.
Why the study?
Noncompaction cardiomyopathy has unknown pathophysiological mechanisms, prompting evaluation of echocardiographic predictors for rigid body rotation using a machine learning model.
Can a machine learning model identify echocardiographic predictors of rigid body rotation in patients with noncompaction cardiomyopathy?
Observational (n=49)
Can a machine learning model identify echocardiographic predictors of rigid body rotation in patients with noncompaction cardiomyopathy?
A machine learning framework using echocardiographic parameters can accurately predict rigid body rotation in patients with noncompaction cardiomyopathy, a pattern associated with worse systolic and diastolic function.
Answers are grounded in this paper and cite their sources.
May aid RBR identification in noncompaction cardiomyopathy; hypothesis-generating and requires prospective validation before practice change.
AIMS: 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/m2, 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.
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Melo et al. (2021) conducted an observational in Noncompaction cardiomyopathy (n=49). Machine learning model (random forest and neural network) was evaluated on Predictors of rigid body rotation (RBR) (Accuracy 0.93, AUC 0.88). A machine learning model identified twist, NC/C, torsion, LV ejection fraction, and diastolic dysfunction as strong predictors of rigid body rotation in noncompaction cardiomyopathy (AUC 0.88).
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