Why the study?
Identifying pediatric dilated cardiomyopathy patients at high risk for death or heart transplant and delineating associated mechanisms could facilitate more precise prognostication and treatment.
Does interpretable machine-learning identify pediatric DCM patients at high risk for death or heart transplant?
Does interpretable machine-learning identify pediatric DCM patients at high risk for death or heart transplant?
Interpretable machine learning models incorporating cardiac mechanics and clinical parameters may improve risk stratification for death or heart transplant in pediatric dilated cardiomyopathy.
Interpretable machine-learning, using full cardiac-cycle systolic and diastolic data, mechanics and clinical parameters, can potentially identify pediatric DCM patients at high-risk for DoT, and delineate mechanisms associated with risk. This may facilitate more precise prognostication and treatment of pediatric DCM.
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García‐Canadilla et al. (2021) studied this question.
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