Clinical risk assessment model for pediatric obstructive sleep apnea

Objectives/Hypothesis To develop a clinical risk prediction model that identifies children with obstructive sleep apnea (OSA) in a clinical setting by examining the symptoms, physical status, and OSA‐18 questionnaire results. Design Single institutional, cross‐sectional study. Methods Children aged 2 to 18 years with symptoms of OSA were enrolled. Pediatric OSA was diagnosed through full‐night polysomnography. Clinical data, namely demographics, symptoms, OSA‐18 survey results, tonsil and adenoid sizes, and the weight of each child, were examined for constructing a simple point‐based clinical model for OSA prediction. Variables for the risk model were selected using multivariable logistic regression analyses. Results Of the 310 participants (mean age, 7.6 ± 3.7 years; boys, 67%), 170 (55%) experienced OSA. Modeling variables were determined using several univariate logistic regression analyses, followed by multivariable logistic regression analyses. A point‐based clinical model incorporating the age, tonsil size (5 points maximum), adenoid size (5 and 20 points for age > 6 years and < 6 years, respectively), obesity (5 points for age > 6 years), and breathing pauses (5 points) was developed (area under the curve = 0.832). Moreover, the optimal cutoff points for predicting the apnea–hypopnea index of > 1 and > 5 were 10 (sensitivity, 72.9%; specificity, 65.0%) and 12 (sensitivity, 77.5%; specificity, 56.9%), respectively. Internal validation using the bootstrap method revealed no apparent overfitting problem. Conclusion A novel clinical prediction model was developed for determining the risk of pediatric OSA; the model can be useful in identifying high‐risk patients among those with sleep disturbances. Level of Evidence 4. Laryngoscope , 126:2403–2409, 2016