J Clin Endocrinol Metab. 2021 Dec 6:dgab873. doi: 10.1210/clinem/dgab873. Online ahead of print.
ABSTRACT
BACKGROUND: Familial hypercholesterolemia (FH) confers a greatly increased risk for premature cardiovascular disease (CVD), but remains very under-diagnosed and under-treated in primary care populations. We assessed whether using a hybrid model consisting of two existing FH diagnostic criteria coupled with electronic medical record (EMR) data, would accurately identify patients with FH in a midwest US metropolitan healthcare system.
METHODS AND RESULTS: We conducted a retrospective, records-based, cross-sectional study using datasets from unique EMRs of living patients. Using Structured Query Language (SQL) to identify components of two currently approved FH diagnostic criteria, we created a hybrid model to identify individuals with FH. Of 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%) compared with the general population (1.8%) and compared with those having a high CAD risk, but no FH (10%). Although most patients were receiving lipid-lowering therapies (LLT), only 50% were receiving guideline-recommended high-intensity LLT.
CONCLUSION: Using the hybrid model, we identified FH with a higher clinical and genetic detection rate compared with using standard diagnostic criteria, individually. Statin and other LLT use were suboptimal and below guideline recommendations. Because FH under-diagnosis and under-treatment 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.
PMID:34871430 | DOI:10.1210/clinem/dgab873
