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Published on: December 6, 2016
Prospective validation of a brief questionnaire for predicting the severity of pediatric obstructive sleep apnea
Catherine L Kennedy1, Bella E Onwumbiko1, Jasmine Blake1
1Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland Medical Center, Baltimore, MD, USA.
Insights
A new screening questionnaire, the Selected Features (SF) tool, effectively identifies children with obstructive sleep apnea (OSA). This brief questionnaire shows improved prediction of OSA severity compared to existing methods, aiding in clinical decisions for pediatric sleep disorders.
Area of Science:
- Pediatric Sleep Medicine
- Diagnostic Tools
- Respiratory Disorders
Background:
- Obstructive sleep apnea (OSA) in children is often diagnosed via polysomnography, but its cost and limited availability mean many undergo tonsillectomy and adenoidectomy (T&A) based on clinical symptoms alone.
- A previously developed brief screening questionnaire, 'Selected Features' (SF), showed promise in predicting OSA severity.
Purpose of the Study:
- To prospectively validate the predictive accuracy of the SF questionnaire for diagnosing pediatric obstructive sleep apnea (OSA).
- To compare the performance of the SF questionnaire against the Pediatric Sleep Questionnaire-Sleep Related Breathing Disorder (PSQ-SRBD) scale.
Main Methods:
- A prospective study involving 124 children with sleep-disordered breathing referred for T&A.
- Comparison of SF and PSQ-SRBD using linear regression for OSA prediction and logistic regression for severe OSA (Apnea-Hypopnea Index [AHI] > 10).
Main Results:
- The SF questionnaire demonstrated superior performance over the PSQ-SRBD and null models, as indicated by Akaike Information Criteria.
- SF achieved an overall accuracy of 0.73 (0.64-0.80) in predicting severe OSA (AHI > 10), compared to 0.65 (0.56-0.73) for PSQ-SRBD.
Conclusions:
- The SF questionnaire, by reducing redundancy, offers improved prediction of OSA and its severity in children with a high likelihood of the condition.
- The SF tool shows potential for screening children before T&A, particularly in resource-limited settings, though multi-site validation is recommended.
Introduction:
Pediatric obstructive sleep apnea (OSA) is diagnosed and stratified by polysomnography. However, due to cost and inaccessibility, up to 90% of children undergo tonsillectomy and adenoidectomy (T&A) solely based on clinical criteria. We previously developed a data-driven brief screening questionnaire ('Selected Features,' SF) that predicted OSA severity than alternatives. The SF asks the parent whether a child: (i) has had breath-holding spells at night over the past 4 weeks, (ii) is a mouth-breather during the day, (iii) has stopped growing at a normal rate any time since birth, and (iv) is overweight. This study sought prospectively validate the SF questionnaire.
Methods:
We conducted a prospective assessment of the predictive accuracy of SF compared to the Pediatric Sleep Questionnaire-Sleep Related Breathing Disorder (PSQ-SRBD) scale in otherwise healthy children with sleep disordered breathing referred for T&A. We compared the model fits of PSQ-SRDB and SF for (i) a linear regression model for the prediction of OSA, and (ii) a logistic regression model for severe OSA, defined as apnea hypopnea index (AHI) > 10. P < 0.05 was significant.
Results:
A total of 124 patients were included. The average age was 7.3 years (95% confidence interval, 6.6-8.0) and 66 (54%) were male. The racial composition was 54 (44%) black, 41 (33%) white, and 28 (23%) other. The median AHI was 4.8 (interquartile range 12) and 43 (35%) of patients had severe OSA. In linear and logistic regression models, SF outperformed the PSQ-SRBD and null models as measured by Akaike Information Criteria. The overall accuracy in predicting AHI >10 for PSQ-SRBD was 0.65 (0.56-0.73, P = 0.54) compared to 0.73 (0.64-0.80, P = 0.04) for SF.
Conclusion:
By eliminating redundancy, we have developed a questionnaire with improved prediction of OSA and its severity, in children with high pre-test probability of the condition. While multi-site validation is necessary, SF demonstrates value in screening children prior to T&A in resource-limited environments.
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