ArticleJournal of clinical medicine2022
Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An Updated Review of Novel Triglyceride-Lowering Therapies in Adults with Familial Chylomicronemia Syndrome.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Clinical considerations for the treatment of patients with familial chylomicronemia syndrome using a hepatic-targetedAmerican journal of preventive cardiology · 2025Review
- Anti-apoC-III Therapies and Implications for Treatment of Pancreatitis and Cardiovascular Disease.Current atherosclerosis reports · 2025Review
- Smart medical report: efficient detection of common and rare diseases on common blood tests.Frontiers in digital health · 2024Article
- Assessment of Hypertensive Patients' Complex Metabolic Status Using Data Mining Methods.Journal of cardiovascular development and disease · 2023Article
- Determining the prevalence of childhood hypertension and its concomitant metabolic abnormalities using data mining methods in the Northeastern region of Hungary.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThere are no exact data about the prevalence of familial chylomicronemia syndrome (FCS) in Central Europe. We aimed to identify FCS patients using either the FCS score proposed by Moulin et al. or with data mining, and assessed the diagnostic applicability of the FCS score.
methodsAnalyzing medical records of 1,342,124 patients, the FCS score of each patient was calculated. Based on the data of previously diagnosed FCS patients, we trained machine learning models to identify other features that may improve FCS score calculation.
resultsWe identified 26 patients with an FCS score of ≥10. From the trained models, boosting tree models and support vector machines performed the best for patient recognition with overall AUC above 0.95, while artificial neural networks accomplished above 0.8, indicating less efficacy. We identified laboratory features that can be considered as additions to the FCS score calculation.
conclusionsThe estimated prevalence of FCS was 19.4 per million in our region, which exceeds the prevalence data of other European countries. Analysis of larger regional and country-wide data might increase the number of FCS cases. Although FCS score is an excellent tool in identifying potential FCS patients, consideration of some other features may improve its accuracy.
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