Sleep & breathing = Schlaf & Atmung · 2025
Predicting treatment success in pediatric mild-to-moderate OSA: real-world evidence from a model based on polysomnographic parameters.
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Wang Yuanming, Cheng Chen
Аннотация
BACKGROUND: Pediatric mild-to-moderate obstructive sleep apnea (OSA) is often treated with intranasal corticosteroids (INCS), but response rates vary. Identifying early predictors of treatment success may facilitate individualized therapy.
METHODS: We conducted a single-center, two-phase observational study to develop and validate a predictive model for INCS response in children aged 3-12 years with mild-to-moderate OSA, defined by a baseline apnea-hypopnea index (AHI) of 1.0-10.0 events/hour. The derivation cohort (n = 175) was retrospectively enrolled between 2019 and 2023. A prospective validation cohort (n = 60) was recruited between 2024 and 2025 using identical diagnostic and treatment protocols. All patients received standardized INCS therapy. Treatment response was defined as a ≥ 50% reduction in AHI along with improvement in clinical symptoms at 6-9 months follow-up.
RESULTS: Candidate predictors were extracted from baseline clinical and polysomnographic (PSG) parameters. Multivariable logistic regression was used to identify independent predictors. A predictive nomogram model was constructed based on these variables and externally validated in the prospective cohort. The model demonstrated good calibration and discrimination.
CONCLUSION: This study presents a validated nomogram model based on PSG and clinical parameters to predict treatment response to INCS in pediatric OSA, supporting early decision-making and personalized treatment strategies.
METHODS: We conducted a single-center, two-phase observational study to develop and validate a predictive model for INCS response in children aged 3-12 years with mild-to-moderate OSA, defined by a baseline apnea-hypopnea index (AHI) of 1.0-10.0 events/hour. The derivation cohort (n = 175) was retrospectively enrolled between 2019 and 2023. A prospective validation cohort (n = 60) was recruited between 2024 and 2025 using identical diagnostic and treatment protocols. All patients received standardized INCS therapy. Treatment response was defined as a ≥ 50% reduction in AHI along with improvement in clinical symptoms at 6-9 months follow-up.
RESULTS: Candidate predictors were extracted from baseline clinical and polysomnographic (PSG) parameters. Multivariable logistic regression was used to identify independent predictors. A predictive nomogram model was constructed based on these variables and externally validated in the prospective cohort. The model demonstrated good calibration and discrimination.
CONCLUSION: This study presents a validated nomogram model based on PSG and clinical parameters to predict treatment response to INCS in pediatric OSA, supporting early decision-making and personalized treatment strategies.