ArticleJACC. Advances2023
A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia.
Article in JACC. Advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Proposal of a familial hypercholesterolemia paediatric diagnostic score (FH-PeDS).European journal of preventive cardiology · 2026Article
- Polygenic Prediction of Nongoal Response to Statin Therapy.Circulation. Genomic and precision medicine · 2026Article
- Integrating New Technologies in Lipidology: A Comprehensive Review.Journal of clinical medicine · 2025Review
- Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?Journal of the American Heart Association · 2024Article
- A machine-learning algorithm using claims data to identify patients with homozygous familial hypercholesterolemia.Scientific reports · 2024Article
- Modelling a two-stage adult population screen for autosomal dominant familial hypercholesterolaemia: cross-sectional analysis within the UK Biobank.BMJ public health · 2023Article
- Is it Time to Rethink Screening for Familial Hypercholesterolemia?JACC. Advances · 2023Article
- Applications of machine learning in familial hypercholesterolemia.Frontiers in cardiovascular medicine · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Background: People with monogenic familial hypercholesterolemia (FH) are at an increased risk of premature coronary heart disease and death. With a prevalence of 1:250, FH is relatively common; but currently there is no population screening strategy in place and most carriers are identified late in life, delaying timely and cost-effective interventions. Objectives: The purpose of this study was to derive an algorithm to identify people with suspected monogenic FH for subsequent confirmatory genomic testing and cascade screening. Methods: A least absolute shrinkage and selection operator logistic regression model was used to identify predictors that accurately identified people with FH in 139,779 unrelated participants of the UK Biobank. Candidate predictors included information on medical and family history, anthropometric measures, blood biomarkers, and a low-density lipoprotein cholesterol (LDL-C) polygenic score (PGS). Model derivation and evaluation were performed in independent training and testing data. Results: A total of 488 FH variant carriers were identified using whole-exome sequencing of the Conclusions: Detecting individuals with FH can be improved by considering additional predictors. This would reduce the sequencing burden in a 2-stage population screening strategy for FH.
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