A roadmap for familial hypercholesterolaemia control
Authors: Alexandre C Pereira
Journal: The Lancet Digital Health (2019)

Familial hypercholesterolaemia is the most common genetic lipid disorder and early identification of carriers is highly cost-effective for societies. Its prevalence reaches one in every 250 individuals in the global general population. The disease exponentially increases the risk of early coronary artery disease and even patients who do not present with early disease have, if left untreated, more than 50% chance of having a coronary event by their sixties1Miname MH Bittencourt MS Moraes SR et al.Coronary artery calcium and cardiovascular events in patients with familial hypercholesterolemia receiving standard lipid-lowering therapy.JACC Cardiovasc Imaging. 2019; 12: 1797-1804Crossref PubMed Scopus (81) Google Scholar and a higher than 80% risk of coronary disease after their seventies.2Silva PR Jannes CE Marsiglia JD Krieger JE Santos RD Pereira AC Predictors of cardiovascular events after one year of molecular screening for Familial hypercholesterolemia.Atherosclerosis. 2016; 250: 144-150Summary Full Text Full Text PDF PubMed Scopus (17) Google Scholar The disease is treatable in most cases and new drugs have brought hope even to those patients not responding to already very good first-line treatment alternatives. Additionally, because of its autosomal dominant mode of inheritance, familial hypercholesterolaemia is a paradigm of familial cascade screening approaches, as 50% of first-degree relatives are expected to inherit the condition. Cascade screening has been shown to be highly cost-effective in several very different health systems worldwide.3Kerr M Pears R Miedzybrodzka Z et al.Cost effectiveness of cascade testing for familial hypercholesterolaemia, based on data from familial hypercholesterolaemia services in the UK.Eur Heart J. 2017; 38: 1832-1839Crossref PubMed Scopus (70) Google Scholar, 4Lazaro P Perez de Isla L Watts GF et al.Cost-effectiveness of a cascade screening program for the early detection of familial hypercholesterolemia.J Clin Lipidol. 2017; 11: 260-271Summary Full Text Full Text PDF PubMed Scopus (63) Google Scholar Finally, diagnosis is mainly determined by an objective, and relatively cheap, laboratory test result: LDL-cholesterol levels.5Silva PRS Jannes CE Oliveira TGM et al.Evaluation of clinical and laboratory parameters used in the identification of index cases for genetic screening of familial hypercholesterolemia in Brazil.Atherosclerosis. 2017; 263: 257-262Summary Full Text Full Text PDF PubMed Scopus (17) Google Scholar Nonetheless, despite the overwhelming evidence of the benefits and logistical appropriateness of cascade screening programmes for the early detection of familial hypercholesterolaemia carriers, most societies fall short in identifying numbers approaching even 10% of the expected number of cases,6Santos RD Gidding SS Hegele RA et al.Defining severe familial hypercholesterolaemia and the implications for clinical management: a consensus statement from the International Atherosclerosis Society Severe Familial Hypercholesterolemia Panel.Lancet Diabetes Endocrinol. 2016; 4: 850-861Summary Full Text Full Text PDF PubMed Scopus (267) Google Scholar let alone in targeting these individuals for early treatment and prevention strategies. Even those societies that have been able to promote nationwide programmes, such as the Netherlands,7Louter L Defesche J Roeters van Lennep J Cascade screening for familial hypercholesterolemia: practical consequences.Atheroscler Suppl. 2017; 30: 77-85Summary Full Text Full Text PDF PubMed Scopus (42) Google Scholar have struggled to keep these systems successful. Several reasons are used to explain the relatively low numbers of identified individuals with familial hypercholesterolaemia: little awareness of disease prevalence and its associated morbidity and mortality; health systems incapable of delivering a centralised enterprise for disease diagnosis; unavailability, until recently, of therapies for refractory cases; few incentives for early diagnosis; absence of large-scale availability of molecular genetic tests; issues associated with privacy and consent of family members; no reimbursement for cascade screening; and a restricted time for physicians to conduct family history or follow-up with patients for the contact of first-degree family relatives of affected individuals. None of these reasons justify the inaction. In an era of personalised medicine, familial hypercholesterolaemia is potentially one of the most tractable conditions to deliver the promised society-wide benefits of the implementation of this paradigm. Controlling the disease, however, requires a multipronged approach and the orchestrated participation of several different stakeholders. In The Lancet Digital Health, Kelly Myers and colleagues provide a new contribution to the toolbox of those fighting under-diagnosis of the disease.8Myers K Knowles JW Staszak D et al.Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.Lancet Digital Health. 2019; (published online Oct 21)https://doi.org/10.1016/S2589-7500(19)30150-5Google Scholar Through the training of a machine learning classifier using health-care encounter data, the authors were able to flag with relatively high precision individuals from two different health systems: a national dataset and one from an integrated health-care delivery system. Upon review, approximately 80% of flagged individuals were considered deserving of more comprehensive diagnostic work-ups for familial hypercholesterolaemia. Interestingly, features outside of the lipids realm were also incorporated in the model and found to significantly add information content to the classifier, hinting that higher order models might, indeed, provide a measurable gain in efficiency in this scenario and that one should not be left solely with the values of LDL cholesterol or lipid medications to do the job of finding these high-risk individuals in the system. Another feature of the trained model is to be able to deliver the same precision using two rather different datasets. This should be commended and paves the way for broader use of the tool. The ability to build machine learning classifiers is becoming more available to the health-care enterprise.9Ngiam KY Khor IW Big data and machine learning algorithms for health-care delivery.Lancet Oncol. 2019; 20: e262-e273Summary Full Text Full Text PDF PubMed Scopus (384) Google Scholar Myers and colleagues' example was entirely developed using open-source software. Implementation of similar approaches in different levels of the health ecosystem is not only warranted but deeply desired. Fortunately, different approaches for derivation and deployment of these mechanisms are being tested worldwide.10Crosland P Maconachie R Buckner S McGuire H Humphries SE Qureshi N Cost-utility analysis of searching electronic health records and cascade testing to identify and diagnose familial hypercholesterolaemia in England and Wales.Atherosclerosis. 2018; 275: 80-87Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar Nonetheless, it remains to be seen whether flagging algorithms that are native to electronic health systems will lead to real advances in combating the disease. Myers and colleagues' classifier, as it is currently described, will only flag potential patients to physicians who choose to receive the information on their own patients. For the translation of this into better control of diagnosed patients with familial hypercholesterolaemia, or even better results on early diagnosis through cascade screening, a list of further steps should be implemented. Addition of tools that help administrators to monitor the deliverables of this type of system is as important as implementing the system. Datapoints such as the number of flagged individuals, the percentage of flagged individuals at target lipid levels at baseline and after 1 year, the number of first-degree relatives contacted, the number of first-degree relatives of flagged individuals seen by a health-care provider for familial hypercholesterolaemia screening, medication access, and up-titration in refractory cases are all good examples of easily obtainable variables that could work as proxies to implementation hurdles. The result will be the identification of new bottlenecks and a roadmap for the iterative implementation of a comprehensive system able to curb the disease. Hopefully, providers and payers will embrace the challenge and start to use already available technology to bring into fruition the cost savings that large-scale identification of familial hypercholesterolaemia can deliver. These implementations will pave the way to truly transformative care of individuals with familial hypercholesterolaemia. I declare no competing interests. Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter dataThe FIND FH model successfully scans large, diverse, and disparate health-care encounter databases to identify individuals with familial hypercholesterolaemia. Full-Text PDF Open Access