Abstract
Background Familial hypercholesterolemia ( FH ), is a historically underdiagnosed, undertreated, high-risk condition that is associated with a high burden of cardiovascular morbidity and mortality. In this study, we use a population-based approach using electronic health record ( EHR )-based algorithms to identify FH . We report the major adverse cardiovascular events, mortality, and cost of medical care associated with this diagnosis. Methods and Results In our 1.18 million EHR- eligible cohort, International Classification of Diseases, Ninth Revision ( ICD -9) code-defined hyperlipidemia was categorized into FH and non- FH groups using an EHR algorithm designed using the modified Dutch Lipid Clinic Network criteria. Major adverse cardiovascular events, mortality, and cost of medical care were analyzed. A priori associated variables/confounders were used for multivariate analyses using binary logistic regression and linear regression with propensity score-based weighted methods as appropriate. EHR FH was identified in 32 613 individuals, which was 2.7% of the 1.18 million EHR cohort and 13.7% of 237 903 patients with hyperlipidemia. FH had higher rates of myocardial infarction (14.77% versus 8.33%; P<0.0001), heart failure (11.82% versus 10.50%; P<0.0001), and, after adjusting for traditional risk factors, significantly correlated to a composite major adverse cardiovascular events variable (odds ratio, 4.02; 95% CI, 3.88-4.16; P<0.0001), mortality (odds ratio, 1.20; CI, 1.15-1.26; P<0.0001), and higher total revenue per-year (incidence rate ratio, 1.30; 95% CI, 1.28-1.33; P<0.0001). Conclusions EHR -based algorithms discovered a disproportionately high prevalence of FH in our medical cohort, which was associated with worse outcomes and higher costs of medical care. This data-driven approach allows for a more precise method to identify traditionally high-risk groups within large populations allowing for targeted prevention and therapeutic strategies.