The National Heart, Lung, and Blood Institute (NHLBI) awarded a $756,895 Project Grant to Duke University under the Cardiovascular Diseases Research program (CFDA 93.837). The grant supports the development, validation, and assessment of the clinical utility of a precision genetic testing approach guided by machine learning (ML) algorithms for identifying patients with monogenic cardiovascular disorders, specifically hypertrophic cardiomyopathy (HCM) and transthyretin amyloidosis (ATTR-CM).
The project involves refining and validating imaging-based deep learning algorithms to detect HCM and ATTR-CM from echocardiograms, developing and validating an electronic health record-based computable phenotype for these conditions, and using ML to identify high-evidence genetic variants associated with the diseases. Duke University will serve as the primary site for this research, with sub-awards to Mayo Clinic and the Medical University of South Carolina to perform external validation of the ML-based models. The overall goal is to demonstrate the clinical utility of this integrated precision genetic testing approach to enhance the diagnosis and management of monogenic cardiovascular disorders.
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