Do Sleep Questionnaires Add Predictive Value Beyond Demographics for Obstructive Sleep Apnea Severity? Evidence from 67,397 Patients
- 1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
Abstract
Background: Sleep questionnaires are widely used to screen for obstructive sleep apnea (OSA), but their incremental value beyond collecting basic demographics is rarely assessed. Using a large sample of sleep clinic intake questionnaire data that includes the Epworth Sleepiness Scale, questions on snoring and breathing pause as well as medical history data, we quantified questionnaire-item contributions relative to demographics for OSA severity classification. Methods: In a retrospective cohort of 67,397 adults referred for in-laboratory polysomnography and with questionnaire data, XGBoost classifiers were trained on all features (169 variables), questionnaire items only (161), or demographics only (age, gender, BMI, race; 4). Outcomes were the apnea-hypopnea index (AHI) and respiratory disturbance index (RDI) at 5, 15 and 30 events/h, evaluated on a held-out 10% test set; TabPFN v2.5 served as a secondary comparator. SHAP (SHapley Additive exPlanations) analysis was performed globally and within age and BMI strata; Berlin and STOP-Bang were not evaluated. Results: At AHI [≥] 5, [≥] 15 and [≥] 30, the demographics-only model reached area under the receiver operating characteristic curve (AUROC) 0.749, 0.718 and 0.726, 0.016-0.038 below the all-features model; across all six endpoints it retained 86-94% of the above-chance AUROC. The questionnaire-only model performed worst (AUROC 0.674-0.699 for AHI). Age, BMI and gender were the leading SHAP predictors at every threshold, and the contributions of age and BMI varied across strata. Four-class severity classification was poor (XGBoost accuracy [≤] 0.45). Conclusions: In this referred cohort, four demographic variables captured most of the achievable discrimination, and the questionnaire added at most 0.038 AUROC. Questionnaire-based screening tools should be benchmarked against a demographic baseline, and prospective validation is needed.
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BibTeX
@misc{patted2026sleep,
title = {{Do Sleep Questionnaires Add Predictive Value Beyond Demographics for Obstructive Sleep Apnea Severity? Evidence from 67,397 Patients}},
author = {R. Patted and M. Dige and U. Hanif and E. Mignot and G. Ricciardiello Mejia},
howpublished = {medRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.01.26367019},
url = {https://www.medrxiv.org/content/10.64898/2026.11.01.26367019v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Do Sleep Questionnaires Add Predictive Value Beyond Demographics for Obstructive Sleep Apnea Severity? Evidence from 67,397 Patients
AU - R. Patted
AU - M. Dige
AU - U. Hanif
AU - E. Mignot
AU - G. Ricciardiello Mejia
PY - 2026
DA - 2026/11/01
PB - medRxiv
DO - 10.64898/2026.11.01.26367019
UR - https://www.medrxiv.org/content/10.64898/2026.11.01.26367019v1
AB - Background: Sleep questionnaires are widely used to screen for obstructive sleep apnea (OSA), but their incremental value beyond collecting basic demographics is rarely assessed. Using a large sample of sleep clinic intake questionnaire data that includes the Epworth Sleepiness Scale, questions on snoring and breathing pause as well as medical history data, we quantified questionnaire-item contributions relative to demographics for OSA severity classification. Methods: In a retrospective cohort of 67,397 adults referred for in-laboratory polysomnography and with questionnaire data, XGBoost classifiers were trained on all features (169 variables), questionnaire items only (161), or demographics only (age, gender, BMI, race; 4). Outcomes were the apnea-hypopnea index (AHI) and respiratory disturbance index (RDI) at 5, 15 and 30 events/h, evaluated on a held-out 10% test set; TabPFN v2.5 served as a secondary comparator. SHAP (SHapley Additive exPlanations) analysis was performed globally and within age and BMI strata; Berlin and STOP-Bang were not evaluated. Results: At AHI [≥] 5, [≥] 15 and [≥] 30, the demographics-only model reached area under the receiver operating characteristic curve (AUROC) 0.749, 0.718 and 0.726, 0.016-0.038 below the all-features model; across all six endpoints it retained 86-94% of the above-chance AUROC. The questionnaire-only model performed worst (AUROC 0.674-0.699 for AHI). Age, BMI and gender were the leading SHAP predictors at every threshold, and the contributions of age and BMI varied across strata. Four-class severity classification was poor (XGBoost accuracy [≤] 0.45). Conclusions: In this referred cohort, four demographic variables captured most of the achievable discrimination, and the questionnaire added at most 0.038 AUROC. Questionnaire-based screening tools should be benchmarked against a demographic baseline, and prospective validation is needed.
ER -