Classification of narcolepsy type 1 using machine learning on Stanford Cataplexy Questionnaire responses and HLA-DQB1∗06:02.

BACKGROUND: Narcolepsy type 1 (NT1) is characterized by sleepiness, disturbed sleep, and cataplexy-episodes of sudden muscle tone loss triggered by emotions. Accurate diagnosis in large-scale studies remains challenging due to NT1's low prevalence and reliance on polysomnography. Prior studies have shown that cataplexy-related questions can help identify genuine cataplexy. This study investigates whether a brief cataplexy questionnaire can support scalable NT1 screening. METHODS: We analyzed responses from 280 NT1 patients and 927 controls from the Stanford narcolepsy database. We trained six ML classifiers using 10- and 27-item cataplexy feature sets (emotional triggers, affected muscles), Epworth Sleepiness Scale (ESS) with and without HLA-DQB106:02 typing. Model was trained with nested cross-validation and Optuna optimization. A post hoc veto rule reclassified any subject predicted as NT1 but lacking the HLA-DQB106:02 allele as non-NT1 to reduce false positives when HLA was not part of the feature set. Given the low prevalence of NT1, optimization prioritized specificity. RESULTS: ESS obtained an AUC of 0.863 (95% CI: 0.840-0.885) with sensitivity of 38.9% and specificity of 92.1%, at τ=0.5. The reduced feature set with (k = 11) and without (k = 10) HLA obtained similar AUCs of 0.995 (95% CI: 0.993-0.998), at τ=0.5. The full feature set with (k = 28) and without (k = 27) HLA obtained a similar AUCs of 0.996 (95% CI: 0.995-0.998), at τ=0.5. Inclusion of HLA corrected false positives increasing specificity up to 98.8%, 99.0% and 99.2%, respectively. CONCLUSIONS: ML applied to cataplexy questionnaire and HLA typing enables scalable NT1 screening. Further population-based validations are needed to confirm these findings using larger samples of controls.