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

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.

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 generate 350 latent phenotypes representing EHR codes and used coefficients from LASSO regression on these phenotypes in a training set as weights to calculate ClinRS in a validation set. Using logistic regression, model performances were compared between baseline model and models with risk scores added: 1) PRS, 2) ClinRS, and 3) ClinRS+PRS. We further compared the proposed models with Atherosclerosis Risk in Communities (ARIC) HF risk score.

PRS and ClinRS each predict HF outcomes significantly better than the baseline model, up to eight years prior to HF diagnosis. Including both PRS and ClinRS further improves prediction performance up to ten years prior to diagnosis, two years earlier than either score alone. Additionally, ClinRS significantly outperforms the ARIC model one year prior.

We demonstrate the additive power of integrating GWAS- and EHR-derived risk scores to predict HF cases prior to diagnosis. This standardizable and scalable risk predictor may enable physicians to provide earlier interventions to improve patient outcomes.