PURPOSE OF REVIEW: Diagnostic scoring for familial hypercholesterolaemia (FH) can be used either to screen for possible FH or guide the selection of patients for genetic (DNA) testing. We review the published diagnostic criteria and discuss the options for future development. RECENT FINDINGS: Scoring systems have been developed internationally based on lipid values and various combinations of clinical signs and cardiovascular history. The predictive value varies according to the test population, be it lipid clinic referrals, general population, or relatives of patients with FH. Also, there is increasing recognition of genetic heterogeneity in FH so that criteria are of differing predictive value depending on the genetic variant of FH. SUMMARY: These clinical scoring systems are increasingly used to guide selection of patients for FH genetic testing but no single approach has yet emerged as the system of choice. Further refinement of these scoring tools using more sophisticated calculators are superseding the more manual approaches. These are well suited to web-based tools or smartphone applications.
PURPOSE OF REVIEW: Diagnostic scoring for familial hypercholesterolaemia (FH) can be used either to screen for possible FH or guide the selection of patients for genetic (DNA) testing. We review the published diagnostic criteria and discuss the options for future development. RECENT FINDINGS: Scoring systems have been developed internationally based on lipid values and various combinations of clinical signs and cardiovascular history. The predictive value varies according to the test population, be it lipid clinic referrals, general population, or relatives of patients with FH. Also, there is increasing recognition of genetic heterogeneity in FH so that criteria are of differing predictive value depending on the genetic variant of FH. SUMMARY: These clinical scoring systems are increasingly used to guide selection of patients for FH genetic testing but no single approach has yet emerged as the system of choice. Further refinement of these scoring tools using more sophisticated calculators are superseding the more manual approaches. These are well suited to web-based tools or smartphone applications.
Authors: Roopa Mehta; Rafael Zubirán; Alexandro J Martagón; Alejandra Vazquez-Cárdenas; Yayoi Segura-Kato; María Teresa Tusié-Luna; Carlos A Aguilar-Salinas Journal: J Lipid Res Date: 2016-10-24 Impact factor: 5.922
Authors: Gerald F Watts; Samuel S Gidding; Pedro Mata; Jing Pang; David R Sullivan; Shizuya Yamashita; Frederick J Raal; Raul D Santos; Kausik K Ray Journal: Nat Rev Cardiol Date: 2020-01-23 Impact factor: 32.419
Authors: Maria Mytilinaiou; Ioannis Kyrou; Mike Khan; Dimitris K Grammatopoulos; Harpal S Randeva Journal: Front Pharmacol Date: 2018-07-12 Impact factor: 5.810
Authors: Paul Crosland; Ross Maconachie; Sara Buckner; Hugh McGuire; Steve E Humphries; Nadeem Qureshi Journal: Atherosclerosis Date: 2018-05-17 Impact factor: 5.162