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. 2016 Sep-Oct;10(5):1230-9.
doi: 10.1016/j.jacl.2016.08.001. Epub 2016 Aug 6.

Rapid identification of familial hypercholesterolemia from electronic health records: The SEARCH study

Maya S Safarova  1 , Hongfang Liu  2 , Iftikhar J Kullo  3
Affiliations

Rapid identification of familial hypercholesterolemia from electronic health records: The SEARCH study

Maya S Safarova et al. J Clin Lipidol. 2016 Sep-Oct.

Abstract

Background: Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States.

Objective: To address these knowledge gaps, we developed an ePhenotyping algorithm for rapid identification of FH in electronic health records (EHRs) and deployed it in the Screening Employees And Residents in the Community for Hypercholesterolemia (SEARCH) study.

Methods: We queried a database of 131,000 individuals seen between 1993 and 2014 in primary care practice to identify 5992 (mean age 52 ± 13 years, 42% men) patients with low-density lipoprotein cholesterol (LDL-C) ≥190 mg/dL, triglycerides <400 mg/dL and without secondary causes of hyperlipidemia.

Results: Our EHR-based algorithm ascertained the Dutch Lipid Clinic Network criteria for FH using structured data sets and natural language processing for family history and presence of FH stigmata on physical examination. Blinded expert review revealed positive and negative predictive values for the SEARCH algorithm at 94% and 97%, respectively. The algorithm identified 32 definite and 391 probable cases with an overall FH prevalence of 0.32% (1:310). Only 55% of the FH cases had a diagnosis code relevant to FH. Mean LDL-C at the time of FH ascertainment was 237 mg/dL; at follow-up, 70% (298 of 423) of patients were on lipid-lowering treatment with 80% achieving an LDL-C ≤100 mg/dL. Of treated FH patients with premature CHD, only 22% (48 of 221) achieved an LDL-C ≤70 mg/dL.

Conclusions: In a primary care setting, we found the prevalence of FH to be 1:310 with low awareness and control. Further studies are needed to assess whether automated detection of FH in EHR improves patient outcomes.

Keywords: Awareness; Control; Electronic health records; Electronic phenotyping; Familial hypercholesterolemia; Hypercholesterolemia; Informatics; Prevalence; Screening; eEpidemiology.

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Figures

Figure 1
Figure 1
Data elements required for the Mayo SEARCH Algorithm. ASCVD, atherosclerotic cardiovascular disease; EHR, electronic health record; NLP, natural language processing; PPI, patient provided information; LOINC, Logical Observation Identifiers Names and Codes; ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; CPT, current procedural terminology codes.
Figure 2
Figure 2
The Mayo SEARCH study flow chart and stepwise illustration of the approach for identifying FH from electronic health records. Individuals were classified as definite (>8 points), probable (6–7), and possible (3–5) FH.
See this image and copyright information in PMC

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