Harrison, JeffAl-Samarrai, Zahra'a Taha2025-01-152025-01-152024https://hdl.handle.net/2292/71052Aim/Background: Familial Hypercholesterolaemia (FH) is one of the most prevalent inherited autosomal dominant disorders, characterised by impaired lipoprotein metabolism which leads to elevated cholesterol levels. This elevation increases the risk of premature coronary heart disease (CHD) and mortality. Early identification and treatment are essential to increase life expectancy, however FH remains underdiagnosed globally. To overcome this hurdle, this project aims to explore the feasibility of applying the Familial Hypercholesterolaemia Case Ascertainment Tool (FAMCAT) to facilitate the identification of FH. Methods : We first reviewed strategies for improving detection of FH both internationally and within New Zealand, along with the accuracy of these screening algorithms. Following this, we retrospectively applied FAMCAT1 and FAMCAT2 regression equations and the Dutch Lipid Network Criteria to a cohort of over two million individuals aged 16 years and older, using routinely collected laboratory data from 2007 to 2020. Findings: The scoping review concluded any form of screening improved FH detection rates, though a number of issues such as inconsistent LDL-C thresholds applied, cohorts varying in their sources, and low participation made the studies difficult to generalise. Incomplete or incorrectly coded patient records were commonly cited issues. Algorithm accuracy results were mixed, with most studies concluding the FAMCAT algorithms were the most effective tools for screening electronic health records to identify high-risk patients. Applied to a New Zealand cohort of 126,747 of patients, assuming a FH population prevalence of 1 in 500, FAMCAT 1 classified 2.9% as likely FH (1 in 35 of cases screened). FAMCAT2 identified 2.7% (1 in 37 of cases screened) who warrant further investigation and potentially genetic testing. Conclusion: We have demonstrated that applying FAMCAT to routinely collected health data is a feasible method for identifying individuals that may be suitable for triage and further screening. Further research is needed to validate FAMCAT2 in a New Zealand population, undertake a feasibility study of targeted screening (with cascade screening for positive cases) and a health economic evaluation.https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htmfamcatfamcat algorithmDLCNCFamilial HypercholesterolemiaFamilial HypercholesterolaemiaThesisCopyright: the authorAttribution-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nd/4.0/