Background: Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in hypertrophic cardiomyopathy (HCM) typically represents myocardial fibrosis and may lead to fatal ventricular arrhythmias. However, CMR is resource-intensive and sometimes contraindicated. Thus, in patients with HCM, we aimed to detect LGE on CMR by applying machine learning (ML) algorithm to clinical parameters.
Methods and results: In this trans-Pacific multicenter study of HCM, a ML model was developed to distinguish the presence or absence of LGE on CMR by ridge classification method using 22 clinical parameters including 9 echocardiographic data. Among 742 patients in this cohort, the ML model was constructed in 2 institutions in the United States (training set, n = 554) and tested using data from an institution in Japan (test set, n = 188). LGE was detected in 299 patients (54%) in the training set and 76 patients (40%) in the test set. In the test set, the area under the receiver-operating-characteristic curve (AUC) of the ML model derived from the training set was 0.77 (95% confidence interval [CI] 0.70-0.84). When compared with a reference model constructed with 3 conventional risk factors for LGE on CMR (AUC 0.69 [95% CI 0.61-0.77]), the ML model outperformed the reference model (DeLong's test P = 0.01).
Conclusions: This trans-Pacific study demonstrates that ML analysis of clinical parameters can distinguish the presence of LGE on CMR in patients with HCM. Our ML model would help physicians identify patients with HCM in whom the pre-test probability of LGE is high, and therefore CMR will have higher utility.
Keywords: Cardiac magnetic resonance imaging; Hypertrophic cardiomyopathy; Late gadolinium enhancement; Machine learning.
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