ArticleThe Journal of clinical endocrinology and metabolism2022
Improving Familial Hypercholesterolemia Diagnosis Using an EMR-based Hybrid Diagnostic Model.
Article in The Journal of clinical endocrinology and metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Electronic health records-based algorithms to screen for U.S. Centers for Disease Control and Prevention tier 1 genetic diseases: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Increasing provider awareness of Lp(a) testing for patients at risk for cardiovascular disease: A comparative study.American journal of preventive cardiology · 2025Article
- Genetic Testing for Familial Hypercholesterolemia in Clinical Practice.Current atherosclerosis reports · 2023Review
- Leveraging Healthcare System Data to Identify High-Risk Dyslipidemia Patients.Current cardiology reports · 2022Review
- Improving Familial Hypercholesterolemia Diagnosis Using an EMR-based Hybrid Diagnostic Model.The Journal of clinical endocrinology and metabolism · 2022Article
- Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextFamilial hypercholesterolemia (FH) confers a greatly increased risk for premature cardiovascular disease, but remains very underdiagnosed and undertreated in primary care populations.
objectiveWe assessed whether using a hybrid model consisting of 2 existing FH diagnostic criteria coupled with electronic medical record (EMR) data would accurately identify patients with FH in a Midwest US metropolitan healthcare system.
methodsWe conducted a retrospective, records-based, cross-sectional study using datasets from unique EMRs of living patients. Using Structured Query Language to identify components of 2 currently approved FH diagnostic criteria, we created a hybrid model to identify individuals with FH.
resultsOf 264 264 records analyzed, between 794 and 1571 patients were identified as having FH based on the hybrid diagnostic model, with a prevalence of 1:300 to 1:160. These patients had a higher prevalence of premature coronary artery disease (CAD) (38-58%) than the general population (1.8%) and higher than those having a high CAD risk but no FH (10%). Although most patients were receiving lipid-lowering therapies (LLTs), only 50% were receiving guideline-recommended high-intensity LLT.
conclusionUsing the hybrid model, we identified FH with a higher clinical and genetic detection rate than using standard diagnostic criteria individually. Statin and other LLT use were suboptimal and below guideline recommendations. Because FH underdiagnosis and undertreatment are due partially to the challenges of implementing existing diagnostic criteria in a primary care setting, this hybrid model potentially can improve FH diagnosis and subsequent early access to appropriate treatment.
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