Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy.
JACC Asia
; 4(5): 375-386, 2024 May.
Article
en En
| MEDLINE
| ID: mdl-38765660
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.
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Base de datos:
MEDLINE
Idioma:
En
Revista:
JACC Asia
/
JACC. Asia
Año:
2024
Tipo del documento:
Article