Abstract 16554: Rapid Identification of Familial Hypercholesterolemia From Electronic Health Records
Background: Familial hypercholesterolemia (FH) is an important public health burden as it is a relatively common Mendelian genetic disorder which is associated with dramatically increased lifetime risk for premature atherosclerotic cardiovascular disease (ASCVD) due to elevated plasma low-density lipoprotein cholesterol (LDL-C) levels. Little is known about prevalence and control of FH in the US.
Objective: To develop an electronic phenotyping algorithm for rapid identification of FH in electronic health records (EHRs) and thereby address knowledge gaps in prevalence and control of FH.
Methods: We identified patients in the Mayo Employee and Community Health (ECH) system who met the following inclusion criteria: 1) gave research authorization, 2) any LDL-C in EHR ≥190 mg/dL; 2) triglycerides <400 mg/dL; 3) absence of secondary causes of hyperlipidemia (hypothyroidism, cholestatic liver diseases, nephrotic syndrome, renal failure, and pregnancy). Next we applied the Dutch Lipid Clinic Network (DLCN) criteria to ascertain FH cases. ASCVD-related diagnosis codes, laboratory data, and medications were obtained from structured EHR datasets. We used the Mayo natural language processing (NLP) system to ascertain presence of family and personal history of premature ASCVD, xanthomas and corneal arcus from clinical text notes.
Results: Of a total of 131,000 patients seen in ECH clinics between July 1993 and December 2014, 6018 met the inclusion criteria. Implementing the electronic phenotyping algorithm for FH in these patients identified 178 definite and 369 probable cases (DLCN score >8 and 6-8 points, respectively) with an overall FH prevalence of 0.4% (1:240). Blinded expert review of 160 randomly chosen patients showed positive and negative predictive values for electronic phenotyping algorithm at 85% and 90%, respectively. Only 40% of these patients achieved LDL-C ≤100 mg/dL on treatment.
Conclusions: 1) An EHR-based phenotyping algorithm that included NLP had reasonable accuracy in ascertaining FH cases. 2) Implementing this algorithm revealed the prevalence of FH in the study cohort to be nearly twice the current estimate of 1:500 in the general population. 3) Less than half of the FH patients had optimal LDL-C levels on treatment.
Author Disclosures: M.S. Safarova: None. H. Liu: None. I.J. Kullo: None.
- © 2015 by American Heart Association, Inc.