Key result
Machine learning models identify familial chylomicronemia syndrome with an AUC above ~0.95.
Why the study?
Exact data regarding the prevalence of familial chylomicronemia syndrome in Central Europe were lacking.
Does data mining combined with the FCS score improve the identification of familial chylomicronemia syndrome patients in large electronic health record databases?
Observational (n=1,342,124)
Yes
Does data mining combined with the FCS score improve the identification of familial chylomicronemia syndrome patients in large electronic health record databases?
Applying machine learning to clinical databases using triglyceride-related lab values and the FCS score can efficiently identify individuals with suspected rare familial chylomicronemia syndrome.
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.
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Németh et al. (2022) conducted an observational in Familial Chylomicronemia Syndrome (n=1,342,124). Machine learning and FCS score-based data mining was evaluated on Patient recognition (AUC) (AUC >0.95). Machine learning using boosting tree models and support vector machines achieved an overall AUC above 0.95 for the recognition of familial chylomicronemia syndrome.
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