Elsevier

Atherosclerosis

Volume 293, January 2020, Pages 57-61
Atherosclerosis

Reaching detection targets in familial hypercholesterolaemia: Comparison of identification strategies

https://doi.org/10.1016/j.atherosclerosis.2019.11.028Get rights and content

Highlights

  • Familial hypercholesterolaemia (FH) is a preventable cause of coronary heart disease.

  • Only a minority of individuals with FH have so far been identified.

  • Cascade testing, Child-parent Screening and Child-parent Cascade Screening are feasible.

  • Setting FH identification targets is important.

  • Time to target for different strategies in different populations can be estimated.

Abstract

Background and aims

Familial hypercholesterolaemia (FH) is a common and preventable cause of premature heart attack but in most nations only a small proportion of FH-positive individuals have been identified. The aim of this study was to estimate the time to close this FH detection gap.

Methods

We developed a model to estimate the time to identify different proportions of FH in the population for three identification strategies (i) Cascade Testing (FH-mutation testing in relatives of someone with an FH mutation) (ii) Child-parent Screening (testing children for cholesterol and FH mutations during 1-year immunisation and parents of FH-positive children) and (iii) Child-parent Cascade Screening (integrating the first two methods). We used publicly available data to compare the strategies in terms of the time to identify 25%, 50% and 75% of all FH cases in the UK (current target is 25% in 5 years). For Child-parent Cascade Screening, we applied the model to other populations that have reported FH identification levels.

Results

In the UK, 25% of FH individuals would be identified after 47 years for Cascade Testing, 12 years for Child-parent Screening and 8 years for Child-parent Cascade Screening; 50% identification after 146, 33 and 19 years and 75% after 334, 99 and 41 years respectively. For Child-parent Cascade Screening, the times to identify 50% FH were, for Netherlands, Norway, Japan, Canada, USA, Australia/NZ, South Africa and Russia, 0, 5, 13, 15, 16, 18, 21, and 30 years respectively.

Conclusions

Child-parent Cascade Screening is the fastest strategy for identifying FH in the population. The model is applicable to any country to estimate the time to close the FH detection gap (www.screenfh.com).

Introduction

Familial hypercholesterolaemia (FH) is a common and preventable cause of premature ischaemic heart disease (IHD). There are about 260,000 heterozygous affected individuals in the UK (prevalence 1 in 250) who have about a 100-fold excess risk of fatal myocardial infarction between ages 20 and 39 years [1]. A fatal or non-fatal IHD event affects about 50% of FH-positive men before age 50 and about 30% of FH-positive women by age 60 [2]. Preventive treatment with statins is effective in reducing this high risk [3].

Early identification of individuals with FH is therefore a public health priority but in most populations only a small proportion of all cases have been identified [4]. In the UK, an estimated 7% of all cases are known, leaving 93% undetected and at high risk of a premature IHD event [5,6]. This gap in identification was highlighted in a recently published National Health Service (NHS) Plan, and a target was set to increase the 7% to at least 25% in 5 years [6]. How this would be delivered was not specified. Three strategies that have been tested in practice include (i) family-based Cascade Testing, where relatives of individuals with an FH mutation are tested for the same mutation [7] (ii) universal Child-parent Screening, where children aged 1 year old are tested for cholesterol and FH mutations at the time of routine immunisation and the affected parent of positive children identified [8] and (iii) a combination of the two methods, Child-parent Cascade Screening, where Child-parent Screening systematically identifies new unrelated index cases and leads naturally to Cascade Testing of relatives [9].

Here, we develop a model to estimate the time to identify different proportions of FH in the population for the three identification strategies. We use publicly available data to compare the time to reach 25%, 50% and 75% identification for the UK, its home nations and other countries where estimates of FH identification have been recently reported.

Section snippets

Cascade Testing

Supplementary Figure 1 gives the equation used to calculate the number of new FH individuals identified each year by Cascade Testing; the number of new FH relatives identified per known index FH mutation-confirmed case multiplied by the background number of new index FH cases identified each year from opportunistic/targeted testing (eg. high cholesterol level identified in an adult in primary care during a Health Check or in secondary care following a non-fatal cardiac event). We assumed that

Results

Fig. 1 shows plots of FH identification for Cascade Testing, Child-parent Screening and Child-parent Cascade Screening in the UK. The results show that the 25% NHS identification target is reached after 47 years, 12 years and after 8 years, respectively. The plots are curved, because the rate of identification declines with increasing proportions of all cases found for each strategy. Comparable plots for England, Scotland, Wales and Northern Ireland are given in Supplementary Fig. 4.

Table 1

Discussion

The results of this analysis show that the fastest strategy for closing the identification gap in FH is Child-parent Cascade Screening, an integration of universal screening in childhood, based on total cholesterol measurement supported by FH mutation testing during immunisation and subsequent Cascade Testing within mutation-positive families. Nation-wide implementation of this approach in the UK would reach the NHS target of 25% in about 8 years. Cascade testing alone would take 47 years.

The

Author contributions

DW conceived the paper and wrote the first draft. DW and JB developed the methodology, collected, analysed and interpreted the data, revised and finalised the manuscript and approved it for publication.

Declaration of competing interest

The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.

Acknowledgements

We are grateful to Kate Haralambos for providing audit data and to Professors Joan Morris, Nick Wald and Alan Rees for their helpful comments on the paper.

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