The value of machine learning approach based on echocardiography and CMR for diagnosing hypertrophic cardiomyopathy with HFpEF

Motivation: About 43.5% of hypertrophic cardiomyopathy (HCM) can progress to heart failure with preserved ejection fraction (HFpEF). However, it remains lacking standardized method for diagnosing HCM with HFpEF (HCN-HFpEF) in clinic. Goal(s): Investigating the feasibility of identifying HCM-HFpEF by machine learning (ML) approaches based on echocardiography and cardiac magnetic resonance imaging (CMR). Approach: 162 HCM patients were enrolled. Echocardiography and CMR features of cardio were extracted and applied to detect HCM-HFpEF by using ML models, including logistic regression (LR), decision tree (DT), and stochastic gradient descent (SGD). Results: LR and SGD can robustly identify HCM-HFpEF in both training and validation cohorts. Impact: HCM patients with HFpEF demonstrated a markedly reduced survival rate compared to non-HFpEF. Our results suggested machine learning approaches based on echocardiography and CMR can be used to identify HFpEF which would be beneficial for the management of HCM patients.