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A Scoping Review of Electronic Health Records-Based Screening Algorithms for Familial Hypercholesterolemia
Jeffery Osei1,2, Alexander C Razavi3,4, Baffour Otchere1
1Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
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.
