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Abstract #0234

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

Mengyao Hu1, Jiazhao Wang1, Wei Zhu1, Chunhua Yang1, Yinping Leng1, Pei Yang1, Jiankun Dai2, and Lianggeng Gong1
1Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China, 2Clinical and Technical Support, GE Healthcare, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Cardiomyopathy

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.

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