How well can familial hypercholesterolemia be identified in an electronic health record database?

Katherine E Mues1, Alina N Bogdanov2, Keri L Monda1

  • 1Center for Observational Research, Amgen Inc., Thousand Oaks, CA, USA, kmues@amgen.com.

Clinical Epidemiology
|December 12, 2018
PubMed

Insights

Identifying familial hypercholesterolemia (FH) using SNOMED codes in EHRs shows high accuracy but low positive predictive value compared to clinical criteria. This highlights challenges in FH patient identification within large electronic health record databases.

Area of Science:

  • Cardiovascular Medicine
  • Genetics
  • Health Informatics

Background:

  • Familial hypercholesterolemia (FH) is a genetic condition causing high cholesterol and increased coronary heart disease (CHD) risk, often undiagnosed.
  • The Dutch Lipid Network Criteria (DLNC) aid FH diagnosis using clinical and genetic factors.
  • Electronic Health Records (EHRs) utilize Systematized Nomenclature of Medicine (SNOMED) codes for diagnoses, including FH.

Purpose of the Study:

  • To assess the agreement between FH identification using SNOMED and ICD-10 CM codes versus the DLNC within an EHR database.
  • To evaluate the diagnostic performance of SNOMED codes for FH in a real-world clinical setting.

Main Methods:

  • A cohort of patients with hypercholesterolemia was analyzed from the Practice Fusion EHR database.
  • Sensitivity, specificity, and predictive values were calculated comparing SNOMED/ICD-10 CM codes to DLNC criteria (scores ≥6 and >8).

Main Results:

  • Among over 900,000 patients, 2,180 FH cases were identified via SNOMED codes; zero via ICD-10 CM.
  • Compared to DLNC score >8, SNOMED codes showed 84.4% sensitivity, 99.4% specificity, and 6.4% PPV.
  • Compared to DLNC score ≥6, SNOMED codes demonstrated 36.8% sensitivity, 99.5% specificity, and 18.7% PPV.

Conclusions:

  • SNOMED codes offer high sensitivity and specificity for identifying FH patients compared to clinical criteria.
  • The low positive predictive value (PPV) of SNOMED codes indicates challenges in accurately identifying FH patients in EHRs.
  • Discordance between coding systems and clinical criteria underscores difficulties in FH detection within large health databases.
Abstract

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