ABSTRACT
Background:
Current
risk stratification
strategies for
patients with
hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies.
Objectives:
The authors aimed to establish
machine learning (ML)-based models to discriminate major cardiovascular events in
patients with HCM.
Methods:
We enrolled consecutive HCM
patients from 2
tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (
MACE), including all-cause
death, admission for
heart failure (HF-adm), and
stroke. The best model was selected for each outcome using the area under the
receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP)
method.
Results:
In total, 2,111
patients with HCM (age 61.4 ± 13.6 years; 67.6%
men) were analyzed. During the median 4.0 years of follow-up,
MACE occurred in 341
patients (16.2%). Among the 4 ML models, the
logistic regression model achieved the best AUROC of 0.800 (95% CI 0.760-0.841) for
MACE, 0.789 (95% CI 0.736-0.841) for all-cause
death, 0.798 (95% CI 0.736-0.860) for HF-adm, and 0.807 (95% CI 0.754-0.859) for
stroke. The discriminant
ability of the
logistic regression model remained excellent when applied to the external validation cohort for
MACE (AUROC = 0.768), all-cause
death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP
analysis identified left atrial diameter and
hypertension as important variables for all outcomes of interest.
Conclusions:
The proposed ML models incorporating various
phenotypes from
patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.
