Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning. (October 2020)

• Record Type: Journal Article
• Authors: Pina, Ana; Helgadottir, Saga; Mancina, Rosellina Margherita; Pavanello, Chiara; Pirazzi, Carlo; Montalcini, Tiziana; Henriques, Roberto; Calabresi, Laura; Wiklund, Olov; Macedo, M Paula; Valenti, Luca; Volpe, Giovanni; Romeo, Stefano
• Is Part Of: European journal of preventive cardiology. Volume 27:Number 15(2020)
• Page Start: 1639
• Page End: 1646
• Publication Date: 2020-10
• Subjects: Familial hypercholesterolemia -- prediction model -- machine learning -- dyslipidemia -- cardiovascular disease; Cardiovascular system -- Diseases -- Prevention -- Periodicals; Cardiac patients -- Rehabilitation -- Periodicals; 616.12
• DOI: 10.1177/2047487319898951
• Languages: English
• ISSNs: 2047-4873

Abstract

Aims: Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores – for example, the Dutch Lipid Score – are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. The aim of this study is to obtain a more reliable approach to FH diagnosis by a "virtual" genetic test using machine-learning approaches. Methods and results: We used three machine-learning algorithms (a classification tree (CT), a gradient boosting machine (GBM), a neural network (NN)) to predict the presence of FH-causative genetic mutations in two independent FH cohorts: the FH Gothenburg cohort (split into training data ( N = 174) and internal test ( N = 74)) and the FH-CEGP Milan cohort (external test, N = 364). By evaluating their area under the receiver operating characteristic (AUROC) curves, we found that the three machine-learning algorithms performed better (AUROC 0.79 (CT), 0.83 (GBM), and 0.83 (NN) on the Gothenburg cohort, and 0.70 (CT), 0.78 (GBM), and 0.76 (NN) on the Milan cohort) than the clinical Dutch Lipid Score (AUROC 0.68 and 0.64 on the Gothenburg and Milan cohorts, respectively) in predicting carriers of FH-causative mutations. Conclusion: In the diagnosis of FH-causative genetic mutations, all three machine-learning approaches we have tested outperform the Dutch Lipid Score, which is the clinical standard. We expect these machine-learning algorithms to provide the tools to implement a virtual genetic test of FH. These tools might prove particularly important for lipid clinics without access to genetic testing.