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. 2022 Jul 25;11(15):4311.
doi: 10.3390/jcm11154311.

Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods

Ákos Németh  1   2   3 , Mariann Harangi  1 , Bálint Daróczy  4   5 , Lilla Juhász  1 , György Paragh  1 , Péter Fülöp  1
Affiliations

Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods

Ákos Németh et al. J Clin Med. .

Abstract

Background: There 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.

Methods: Analyzing 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.

Results: We 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.

Conclusions: The 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.

Keywords: FCS score; data mining; familial chylomicronemia syndrome; machine learning; screening.

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Conflict of interest statement

Ákos Németh is a co-worker of Aesculab Medical Solutions (Black Horse Group Ltd.), while also on staff at University Debrecen Department of Internal Medicine as a PhD candidate. As stated in the article, the company is a contractual partner of the university who provided cleaned, anonymized data for the research. The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.

Figures

Figure 1
Figure 1
Flowchart of the rapid estimation of familial chylomicronemia (FCS) score.
See this image and copyright information in PMC

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