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A Nomogram Model for Predicting Moderate to Severe OSA in Western China: A Retrospective Analysis
Aman Gul1,2,3, Ainiwaer Talifu4, Maimaiti Aili1
1Department of Clinical Basic Research Center, Uyghur Medicines Hospital of Xinjiang Uyghur Autonomous Region, Urumqi, 830000, People's Republic of China.
Nature and Science of Sleep
|July 1, 2026
Summary
A new nomogram model effectively predicts moderate-to-severe obstructive sleep apnea (OSA) using key clinical factors. This tool aids clinicians in assessing OSA risk and guiding treatment strategies.
Area of Science:
- Medical research
- Clinical diagnostics
- Sleep medicine
Background:
- Obstructive sleep apnea (OSA) is a prevalent condition requiring accurate prediction tools.
- Existing methods for OSA assessment may lack interpretability or broad applicability.
Purpose of the Study:
- To develop and validate an interpretable nomogram model for predicting moderate-to-severe obstructive sleep apnea (OSA).
Main Methods:
- Retrospective analysis of 11,030 OSA patients undergoing polysomnography.
- Development of a nomogram using multivariate logistic regression on a modeling cohort (n=7,721) and validation on a separate cohort (n=3,309).
- Identification of independent risk factors including age, BMI, neck circumference, gender, hypertension, and diabetes.
Main Results:
- The nomogram identified age, BMI, neck circumference, male gender, hypertension, and diabetes as significant predictors of moderate-to-severe OSA.
- The model demonstrated good fit and clinical utility, with AUCs of 0.676 (modeling cohort) and 0.613 (validation cohort).
Conclusions:
- The developed nomogram is a valuable and interpretable tool for predicting moderate-to-severe OSA.
- This model offers strategic guidance for clinical practice in OSA risk assessment and management.
