Yield of Familial Hypercholesterolemia Genetic and Phenotypic Diagnoses After Electronic Health Record and Genomic Data Screening

Abstract

Background Data mining of electronic health records to identify patients suspected of familial hypercholesterolemia (FH) has been limited by absence of both phenotypic and genomic data in the same cohort. Methods and Results Using the Geisinger MyCode Community Health Initiative cohort (n=130 257), we ran 2 screening algorithms (Mayo Clinic [Mayo] and flag, identify, network, deliver [FIND] FH) to determine FH genetic and phenotypic diagnostic yields. With 29 243 excluded by Mayo (for secondary causes of hypercholesterolemia, no lipid value in electronic health records), 52 034 excluded by FIND FH (insufficient data to run the model), and 187 excluded for prior FH diagnosis, a final cohort of 59 729 participants was created. Genetic diagnosis was based on presence of a pathogenic or likely pathogenic variant in FH genes. Charts from 180 variant‐negative participants (60 controls, 120 identified by FIND FH and Mayo) were reviewed to calculate Dutch Lipid Clinic Network scores; a score ≥5 defined probable phenotypic FH. Mayo flagged 10 415 subjects; 194 (1.9%) had a pathogenic or likely pathogenic FH variant. FIND FH flagged 573; 34 (5.9%) had a pathogenic or likely pathogenic variant, giving a net yield from both of 197 out of 280 (70%). Confirmation of a phenotypic diagnosis was constrained by lack of electronic health record data on physical findings or family history. Phenotypic FH by chart review was present by Mayo and/or FIND FH in 13 out of 120 versus 2 out of 60 not flagged by either (P<0.09). Conclusions Applying 2 recognized FH screening algorithms to the Geisinger MyCode Community Health Initiative identified 70% of those with a pathogenic or likely pathogenic FH variant. Phenotypic diagnosis was rarely achievable due to missing data.

Keywords

• electronic health records
• familial hypercholesterolemia
• genetic testing
• machine learning