Rationale and Design of an Artificial Intelligence Model for Diastolic Heart Failure (AID- HF): A Canadian Cardiomyopathy Collaborative (C3) Study
Papaz, T.; Patel, S.; Akilen, R.; Min, S.; Lesurf, R.; Rouleau, J.-L.; Ruiz, M.; Lam, C. Z.; Dragulescu, A.; Friedberg, M. K.; Mertens, L.; Tremblay-Gravel, M.; Krahn, A. D.; Tadros, R.; Mital, S.
Show abstract
Diastolic heart failure (HF) in primary cardiomyopathy is under-recognized and often diagnosed late, particularly in children. While recent studies have advanced understanding of HF with preserved ejection fraction in older adults, the prevalence, outcomes and molecular drivers of diastolic HF in pediatric and young adult cardiomyopathy remain poorly defined, where disease is typically driven by primary myocardial disease rather than acquired co-morbidities. The Canadian Cardiomyopathy Collaborative (C3) was assembled to leverage three of Canadas leading pediatric and adult cardiomyopathy biobank registries. Its flagship initiative, Artificial Intelligence to Model Diastolic Heart Failure (AID-HF), aims to integrate deep phenotyping - including comprehensive diastolic function assessment - with genomics, lipidomics and proteomics and apply machine learning to identify biological and clinical signatures that drive cardiac function and outcomes in cardiomyopathy. Harmonized phenotyping and multiomics protocols across registries will create a uniquely integrated national data resource and enable the goals of AID-HF i.e., earlier diagnosis and new therapeutic targets for diastolic HF in cardiomyopathy.
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