A Scoping Review of Electronic Health Records–Based Screening Algorithms for Familial Hypercholesterolemia
Jeffery Osei, Alexander C. Razavi, Baffour Otchere, Gracelove Bonful, Natalie Akoto, Ralph Kwame Akyea, Nadeem Qureshi, Fátima Coronado, Ramal Moonesinghe, Katherine Kolor, George A. Mensah, Laurence Sperling, Muin J. Khoury
Abstract
Background: Familial hypercholesterolemia (FH) is a common genetic disorder that is strongly associated with premature cardiovascular disease. Effective diagnosis and appropriate treatment of FH can reduce cardiovascular disease risk; however, FH is underdiagnosed. Electronic health record (EHR)-based FH screening tools have been previously described to enhance the detection of FH. Objectives: This scoping review explored the available literature on the performance and utility of existing EHR-based FH screening algorithms or tools. Methods: We searched PubMed, CINAHL, and Embase from inception to October 2023 for relevant literature on the performance, utility, and/or implementation of EHR-based screening algorithms for FH. Results: Of 14 screening algorithms and/or tools identified in the 27 studies included in this review, Familial Hypercholesterolemia Case Ascertainment Tool (1, 2, and ML), FIND FH algorithm, Mayo SEARCH, and TARB-Ex demonstrated the highest performance metrics for identifying patients with FH. Conclusions: EHR-based screening tools hold great potential for improving population-level FH detection. Lack of established diagnostic criteria that can be applied across diverse populations and the lack of information about the performance, utility, and implementation of current EHR-based screening tools across diverse populations limit the current use of these tools.