ArticleEuropean heart journal. Digital health2025
Prognostic stratification of familial hypercholesterolaemia patients using AI algorithms: a gender-specific approach.
Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Prevalence, Clinical Characteristics, and Therapeutic Underachievement in Familial Hypercholesterolemia: Results from the GRegistry-FH Population-Based Study in Greece.Journal of clinical medicine · 2026Article
- Long noncoding RNAs in familial hypercholesterolemia: biomarkers, therapeutics, and AI in precision medicine.Lipids in health and disease · 2025Review
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Authors and funding
10 authors.
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Abstract
Aims: Familial hypercholesterolaemia (FH) is the most prevalent autosomal dominant disorder, affecting about 1 in 200-250 individuals. It is the leading cause of early and aggressive coronary artery disease. Methods and results: We analysed patients with genetically confirmed FH or a score >8 on the Dutch Lipid Clinics Network criteria from the National Registry of the Spanish Atherosclerosis Society, including individuals enrolled from January 2010 to December 2017. The model utilized a dataset incorporating family history, clinical characteristics, laboratory results, genetic data, imaging studies, and lipid-lowering treatment details. Eighty per cent of the population was allocated for training the AI algorithm and 20% was used for testing. A Histogram-based Gradient Boosting Classification Tree was used. The stability of the AI system was assessed using Conclusion: Artificial intelligence-machine learning algorithms are promising tools for enhancing vascular risk stratification, revealing critical sex-based differences.
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