Literature DB >> 30306861

Prevalence, Identification, and Scouting for Familial Hypercholesterolaemia Including Registries.

Matilda Florentin1, Michael S Kostapanos2, Moses S Elisaf1, Evangelos N Liberopoulos1.   

Abstract

BACKGROUND: Familial Hypercholesterolaemia (FH) is the most common metabolic genetic disorder, with around 13 million people worldwide having the disease. However, FH is globally underdiagnosed and undertreated, while the vast majority of those treated do not achieve treatment goals.
OBJECTIVE: This review aims to clarify how to identify patients with FH.
METHODS: We performed a comprehensive search of the literature to identify available data.
RESULTS: Patients with FH are at high risk for cardiovascular events and death at an early age. Therefore, prompt detection of individuals with FH is of pivotal importance in order to implement appropriate preventive measures at a young age. Patient registries are a powerful tool for recording and monitoring a disease and encouraging clinical practices, subsequently improving outcomes and reducing healthcare costs. National FH registries are successfully applied in several countries (e.g. Spain, Denmark, UK, USA and the Netherlands). Importantly, in the last few years, the European Atherosclerosis Society (EAS) launched a global FH network aiming to collect data from specialized FH centres from different countries and establish a worldwide, standardised registry of patients with FH.
CONCLUSION: It appears that the establishment and proper function of such registries will improve FH diagnosis, as well as preventive measures and management of FH patients. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  Cascade screening; Dutch Score; Familial Hypercholesterolaemia (FH); Simon Broome; genetic testing; registries.

Mesh:

Year:  2018        PMID: 30306861     DOI: 10.2174/1381612824666181009103440

Source DB:  PubMed          Journal:  Curr Pharm Des        ISSN: 1381-6128            Impact factor:   3.116


  1 in total

1.  Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients.

Authors:  Lei Wang; Jian Guo; Zhuang Tian; Samuel Seery; Ye Jin; Shuyang Zhang
Journal:  Front Cardiovasc Med       Date:  2022-08-03
  1 in total

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