Key result
Novel pediatric scoring tools outperform standard DLCN criteria for detecting familial hypercholesterolemia, reaching ~0.93 AUC.
Why the study?
Do the novel FH-PeDS and ML-FH-PeDS diagnostic tools improve the detection of familial hypercholesterolemia in pediatric cohorts compared to established criteria?
Cross-Sectional (n=1,700)
Yes
Do the novel FH-PeDS and ML-FH-PeDS diagnostic tools improve the detection of familial hypercholesterolemia in pediatric cohorts compared to established criteria?
Answers are grounded in this paper and cite their sources.
Novel pediatric-specific diagnostic tools (FH-PeDS and ML-FH-PeDS) outperform established criteria for detecting familial hypercholesterolemia in children, potentially guiding genetic testing decisions.
BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) significantly increases cardiovascular risk from childhood yet remains widely underdiagnosed. This cross-sectional study aimed to evaluate existing pediatric FH diagnostic criteria in real-world cohorts and to develop two novel diagnostic tools: a semi-quantitative scoring system (FH-PeDS) and a machine learning model (ML-FH-PeDS) to enhance early FH detection. METHODS: Five established FH diagnostic criteria were assesed (Dutch Lipid Clinics Network [DLCN], Simon Broome, EAS, Simplified Canadian, and Japanese Atherosclerosis Society) in Slovenian (N=1,360) and Portuguese (N=340) pediatric hypercholesterolemia cohorts, using FH-causing variants as the reference standard. FH-PeDS was developed from the Slovenian cohort, and ML-FH-PeDS was trained and tested using a 60%/40% split before external validation in the Portuguese cohort. RESULTS: Only 47.4% of genetically confirmed FH cases were identified by all established criteria, while 10.9% were missed entirely. FH-PeDS outperformed DLCN in the combined cohort (AUC 0.897 vs. 0.857; p<0.01). ML-FH-PeDS showed superior predictive power (AUC 0.932 in training, 0.904 in testing vs. 0.852 for DLCN; p<0.01) and performed best as a confirmatory test in the testing subgroup (39.7% sensitivity, 87.7% PPV at 98% specificity). In the Portuguese cohort, ML-FH-PeDS maintained strong predictive performance (AUC 0.867 vs. 0.815 for DLCN; p<0.01) despite population differences. CONCLUSIONS: Current FH diagnostic criteria perform suboptimally in children. FH-PeDS and ML-FH-PeDS provide tools to improve FH detection, particularly where genetic testing is limited. They also help guide genetic testing decisions for hypercholesterolemic children. By enabling earlier diagnosis and intervention, these tools may reduce long-term cardiovascular risk and improve outcomes.
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Kafol et al. (2025) conducted a cross-sectional in Familial hypercholesterolemia (n=1,700). FH-PeDS and ML-FH-PeDS diagnostic tools vs. Established FH diagnostic criteria (e.g., DLCN) was evaluated on Detection of genetically confirmed familial hypercholesterolemia (p=<0.01). The FH-PeDS and ML-FH-PeDS diagnostic tools outperformed the Dutch Lipid Clinics Network criteria for detecting familial hypercholesterolemia in children (AUC 0.897 and 0.932 vs 0.857; p<0.01).
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