Integrating large scale genetic and clinical information to predict cases of heart failure.

ABSTRACT:

Background

Heart failure (HF) is a major global cause of death. Early risk prediction and intervention could mitigate disease progression. We aimed to improve HF prediction by integrating genome-wide association studies (GWAS)- and electronic health records (EHR)-derived risk scores.

Methods

We previously performed a large HF GWAS within the Global Biobank Meta-analysis Initiative to create a polygenic risk score (PRS). Three Michigan Medicine (MM) cohorts were used to develop the clinical risk score (ClinRS): 1) Primary Care Provider cohort (MM-PCP; N = 61,849), 2) Heart Failure cohort (MM-HF; N = 53,272), and 3) Michigan Genomics Initiative cohort (MM-MGI; N = 60,215). To extract information from high-dimensional EHR data, we leveraged natural language processing to genera

SUBMITTER: Wu KH 

PROVIDER: S-EPMC12638961 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature