Comparing the performance of the novel FAMCAT algorithms and established case-finding criteria for familial hypercholesterolaemia in primary care

Abstract

ObjectiveFamilial hypercholesterolaemia (FH) is a common inherited disorder causing premature coronary heart disease (CHD) and death. We have developed the novel Familial Hypercholesterolaemia Case Ascertainment Tool (FAMCAT 1) case-finding algorithm for application in primary care, to improve detection of FH. The performance of this algorithm was further improved by including personal history of premature CHD (FAMCAT 2 algorithm). This study has evaluated their performance, at 95% specificity, to detect genetically confirmed FH in the general population. We also compared these algorithms to established clinical case-finding criteria.MethodsProspective validation study, in 14 general practices, recruiting participants from the general adult population with cholesterol documented. For 260 participants with available health records, we determined possible FH cases based on FAMCAT thresholds, Dutch Lipid Clinic Network (DLCN) score, Simon-Broome criteria and recommended cholesterol thresholds (total cholesterol >9.0 mmol/L if ≥30 years or >7.5 mmol/L if <30 years), using clinical data from electronic and manual extraction of patient records and family history questionnaires. The reference standard was genetic testing. We examined detection rate (DR), sensitivity and specificity for each case-finding criteria.ResultsAt 95% specificity, FAMCAT 1 had a DR of 27.8% (95% CI 12.5% to 50.9%) with sensitivity of 31.2% (95% CI 11.0% to 58.7%); while FAMCAT 2 had a DR of 45.8% (95% CI 27.9% to 64.9%) with sensitivity of 68.8% (95% CI 41.3% to 89.0%). DLCN score ≥6 points yielded a DR of 35.3% (95% CI 17.3% to 58.7%) and sensitivity of 37.5% (95% CI 15.2% to 64.6%). Using recommended cholesterol thresholds resulted in DR of 28.0% (95% CI 14.3% to 47.6%) with sensitivity of 43.8% (95% CI 19.8% to 70.1%). Simon-Broome criteria had lower DR 11.3% (95% CI 6.0% to 20.0%) and specificity 70.9% (95% CI 64.8% to 76.5%) but higher sensitivity of 56.3% (95% CI 29.9% to 80.2%).ConclusionsIn primary care, in patients with cholesterol documented, FAMCAT 2 performs better than other case-finding criteria for detecting genetically confirmed FH, with no prior clinical review required for case finding.Trial registration numberNCT03934320.

Cost-Effectiveness of Screening Algorithms for Familial Hypercholesterolaemia in Primary Care

“…The omission of the impacts of FH and the benefits of successful treatment suggests that we are conservatively estimating the cost-effectiveness of the different screening algorithms. To model long-term cost-effectiveness would require additional data on quality of life, impact of treatment, and uptake of treatment, none of which were captured as part of the original study [ 12 , 17 ]. However, it has been estimated that over 80% of the lifetime treatment costs associated with FH are accrued within the first year of diagnosis [ 26 ]; therefore, there would be little impact on healthcare resources beyond a time horizon of 1 year.…”

“…The comparator was chosen because, although screening within primary care is recommended [ 4 ], expert consensus from six clinical members of the FAMCAT feasibility study steering committee suggested that there is little evidence that any form of FH screening is actually carried out in primary care. Three case-finding criteria were selected because they represent the current recommended approaches across several countries (cholesterol, Dutch Lipid Clinic Network (DLCN), and Simon Broome (SB)) [ 4 , 18 ], along with two newly developed algorithms which have demonstrated increased sensitivity and specificity from database studies (FAMCAT 1 and 2) [ 12 , 19 ].…”

“…Tool version 2 (FAMCAT2) 1 [12] An updated FAMCAT1 algorithm, with re-estimated regression equations using continuous variables for total cholesterol, LDL-cholesterol, triglycerides, and age. Algorithm identifies patients at increased risk of FH.…”

“…The omission of the impacts of FH and the benefits of successful treatment suggests that we are conservatively estimating the cost-effectiveness of the different screening algorithms. To model long-term costeffectiveness would require additional data on quality of life, impact of treatment, and uptake of treatment, none of which were captured as part of the original study [12,17].…”

“…While screening for FH within primary care has been demonstrated to be cost-effective [ 10 ], there are varying approaches with little evidence as to which offers best value for money [ 4 , 11 ]. Furthermore, there has been recent development of various algorithms which can screen a patient’s electronic health record (EHR) and assess their risk of having FH [ 12 ].…”