Hidden Burden of Electronic Health Record-Identified Familial Hypercholesterolemia: Clinical Outcomes and Cost of Medical Care
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Background Familial hypercholesterolemia ( FH ), is a historically underdiagnosed, undertreated, high-risk condition that is associated with a high burden of cardiovascular morbidity and mortality. In this study, we use a population-based approach using electronic health record ( EHR )-based algorithms to identify FH . We report the major adverse cardiovascular events, mortality, and cost of medical care associated with this diagnosis. Methods and Results In our 1.18 million EHR- eligible cohort, International Classification of Diseases, Ninth Revision ( ICD -9) code-defined hyperlipidemia was categorized into FH and non- FH groups using an EHR algorithm designed using the modified Dutch Lipid Clinic Network criteria. Major adverse cardiovascular events, mortality, and cost of medical care were analyzed. A priori associated variables/confounders were used for multivariate analyses using binary logistic regression and linear regression with propensity score-based weighted methods as appropriate. EHR FH was identified in 32 613 individuals, which was 2.7% of the 1.18 million EHR cohort and 13.7% of 237 903 patients with hyperlipidemia. FH had higher rates of myocardial infarction (14.77% versus 8.33%; P<0.0001), heart failure (11.82% versus 10.50%; P<0.0001), and, after adjusting for traditional risk factors, significantly correlated to a composite major adverse cardiovascular events variable (odds ratio, 4.02; 95% CI, 3.88-4.16; P<0.0001), mortality (odds ratio, 1.20; CI, 1.15-1.26; P<0.0001), and higher total revenue per-year (incidence rate ratio, 1.30; 95% CI, 1.28-1.33; P<0.0001). Conclusions EHR -based algorithms discovered a disproportionately high prevalence of FH in our medical cohort, which was associated with worse outcomes and higher costs of medical care. This data-driven approach allows for a more precise method to identify traditionally high-risk groups within large populations allowing for targeted prevention and therapeutic strategies.
Kumi D, Narh J, Odoi S, Oduro A, Gajjar R, Gwira-Tamattey E BMJ Open. 2024; 14(5):e077839.
PMID: 38806434 PMC: 11138297. DOI: 10.1136/bmjopen-2023-077839.
Passero L, Roberts M High Blood Press Cardiovasc Prev. 2024; 31(2):215-219.
PMID: 38308804 DOI: 10.1007/s40292-024-00624-6.
Marquina C, Morton J, Lloyd M, Abushanab D, Baek Y, Abebe T Pharmacoeconomics. 2024; 42(4):373-392.
PMID: 38265575 PMC: 10937756. DOI: 10.1007/s40273-023-01347-7.
Saadatagah S, Alhalabi L, Farwati M, Zordok M, Bhat A, Smith C Am J Prev Cardiol. 2022; 12:100393.
PMID: 36204653 PMC: 9530843. DOI: 10.1016/j.ajpc.2022.100393.
Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research.
Gouda P, Ezekowitz J J Cardiovasc Transl Res. 2022; 16(3):546-556.
PMID: 36103036 DOI: 10.1007/s12265-022-10313-1.