Improving Familial Hypercholesterolemia Diagnosis Using an EMR-based Hybrid Diagnostic Model

Area of Science:

• Cardiology
• Genetics
• Public Health

Background:

• Familial Hypercholesterolemia (FH) significantly elevates the risk of premature cardiovascular disease.
• Despite the risks, FH remains underdiagnosed and undertreated in primary care settings.
• Current diagnostic criteria can be challenging to implement in routine clinical practice.

Purpose of the Study:

• To assess a hybrid diagnostic model combining existing FH criteria with electronic medical record (EMR) data.
• To accurately identify patients with FH within a large healthcare system.
• To evaluate the prevalence and treatment patterns of FH in the identified patient cohort.

Main Methods:

• A retrospective, cross-sectional study utilizing EMR data from a Midwest US metropolitan healthcare system.
• Development of a hybrid FH diagnostic model using Structured Query Language to integrate two existing FH diagnostic criteria.
• Analysis of records to identify individuals meeting the hybrid model's criteria for FH.

Main Results:

• The hybrid model identified a FH prevalence ranging from 1:300 to 1:160, significantly higher than previously estimated.
• Patients identified with FH exhibited a substantially higher prevalence of premature coronary artery disease (CAD) compared to the general population and those at high CAD risk without FH.
• While most FH patients received lipid-lowering therapies (LLTs), only 50% were on guideline-recommended high-intensity treatment.

Conclusions:

• The hybrid FH diagnostic model demonstrated a higher clinical and genetic detection rate compared to individual criteria.
• Lipid-lowering therapy use in FH patients was suboptimal, falling below recommended guidelines.
• This hybrid model offers a promising strategy to improve FH diagnosis and facilitate timely, appropriate treatment, addressing current underdiagnosis challenges.