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Updated: Aug 8, 2026
Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
Published on: December 6, 2016
Based on the middle ear negative pressure and multimodal data to construct and externally validate the predictive
Simin Zhu1,2, Yewen Shi1,2,3, Yanuo Zhou1,2
1Department of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China.
Purpose:
The aim of this study was to analyze negative middle ear pressure in children with obstructive sleep apnea (OSA) and establish and evaluate predictive models according to these findings.
Methods:
This retrospective study involved 931 children: 715 with OSA and 216 controls. Demographic, clinical, lateral head radiograph, and tympanometry data were collected. These characteristics of children with OSA were analyzed, with a particular focus on exploring the value of middle ear-related parameters for the diagnosis of pediatric OSA. Additionally, a logistic regression model incorporating optimal indicators was developed to predict pediatric OSA. The model was visualized via a nomogram and evaluated for discrimination, calibration, clinical effectiveness and external validation.
Results:
Children with OSA were younger and exhibited longer soft palates, larger tonsils and adenoids than non-OSA children. Additionally, children with OSA presented higher acoustic admittance (AC) and resonance frequency (RF), lower middle ear pressure (MEP), and narrower pressure gradient (PG) than non-OSA children. The external auditory canal volume (ECV), MEP, and PG were identified as independent predictors of childhood OSA. We constructed a foundational prediction model for childhood OSA (Model 0, AUC = 0.845, 95% CI: 0.813-0.878), and then added each tympanometric indicator to the model individually. After incorporating MEP into the model (Model 4), the AUC increased by 0.022 (p < 0.05).
Conclusions:
Based on a large sample size and multivariate analysis of factors associated with pediatric OSA, we developed a predictive model incorporating middle ear negative pressure for pediatric OSA, which may assist clinicians in diagnosing pediatric OSA in complex clinical settings.
