Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?

Autor: Stevens CAT; Department of Primary Care and Public Health School of Public Health, Imperial College London London United Kingdom., Vallejo-Vaz AJ; Department of Primary Care and Public Health School of Public Health, Imperial College London London United Kingdom.; Department of Medicine, Faculty of Medicine Universidad de Sevilla Sevilla Spain.; Clinical Epidemiology and Vascular Risk Instituto de Biomedicina de Sevilla (IBiS), IBiS/Hospital Universitario Virgen del Rocío/Universidad de Sevilla/CSIC Sevilla Spain.; Centro de Investigación Biomédica en Red (CIBER) de Epidemiología y Salud Pública Instituto de Salud Carlos III Madrid Spain., Chora JR; Nacional Institute of Health Dr. Ricardo Jorge Lisbon Portugal.; BioISI-Biosystems and Integrative Sciences Institute University of Lisbon Portugal., Barkas F; Department of Primary Care and Public Health School of Public Health, Imperial College London London United Kingdom.; Department of Internal Medicine, Faculty of Medicine, School of Health Sciences University of Ioannina Greece., Brandts J; Department of Primary Care and Public Health School of Public Health, Imperial College London London United Kingdom.; Department of Medicine I University Hospital Aachen Aachen Germany., Mahani A; Quantitative Research Davidson Kempner Capital Management New York NY., Abar L; National Institute of Cancer National Institute of Health Rockville MD., Sharabiani MTA; Department of Primary Care and Public Health School of Public Health, Imperial College London London United Kingdom., Ray KK; Department of Primary Care and Public Health School of Public Health, Imperial College London London United Kingdom.
Jazyk: angličtina
Zdroj: Journal of the American Heart Association [J Am Heart Assoc] 2024 Jun 18; Vol. 13 (12), pp. e034434. Date of Electronic Publication: 2024 Jun 15.
DOI: 10.1161/JAHA.123.034434
Abstrakt: Background: Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiagnosed monogenic disorder. Improved detection could reduce the large number of cardiovascular events attributable to poor case finding. We aimed to assess whether machine learning algorithms outperform clinical diagnostic criteria (signs, history, and biomarkers) and the recommended screening criteria in the United Kingdom in identifying individuals with FH-causing variants, presenting a scalable screening criteria for general populations.
Methods and Results: Analysis included UK Biobank participants with whole exome sequencing, classifying them as having FH when (likely) pathogenic variants were detected in their LDLR , APOB , or PCSK9 genes. Data were stratified into 3 data sets for (1) feature importance analysis; (2) deriving state-of-the-art statistical and machine learning models; (3) evaluating models' predictive performance against clinical diagnostic and screening criteria: Dutch Lipid Clinic Network, Simon Broome, Make Early Diagnosis to Prevent Early Death, and Familial Case Ascertainment Tool. One thousand and three of 454 710 participants were classified as having FH. A Stacking Ensemble model yielded the best predictive performance (sensitivity, 74.93%; precision, 0.61%; accuracy, 72.80%, area under the receiver operating characteristic curve, 79.12%) and outperformed clinical diagnostic criteria and the recommended screening criteria in identifying FH variant carriers within the validation data set (figures for Familial Case Ascertainment Tool, the best baseline model, were 69.55%, 0.44%, 65.43%, and 71.12%, respectively). Our model decreased the number needed to screen compared with the Familial Case Ascertainment Tool (164 versus 227).
Conclusions: Our machine learning-derived model provides a higher pretest probability of identifying individuals with a molecular diagnosis of FH compared with current approaches. This provides a promising, cost-effective scalable tool for implementation into electronic health records to prioritize potential FH cases for genetic confirmation.
Databáze: MEDLINE
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