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Background <p>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.</p> Methods <p>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 (<i>n</i> = 175) was retrospectively enrolled between 2019 and 2023. A prospective validation cohort (<i>n</i> = 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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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Predicting treatment success in pediatric mild-to-moderate OSA: real-world evidence from a model based on polysomnographic parameters

  • 期刊论文

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  • 发表日期：

    2025年9月26日

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  • Yuanming Wang,
  • Chen Cheng

摘要

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.