Kun-Tai Kang1,2,3, Wen-Chin Weng4,5, Chia-Hsuan Lee2,3, Tzu-Yu Hsiao1, Pei-Lin Lee4,6, Wei-Chung Hsu7,8. 1. Department of Otolaryngology, National Taiwan University Hospital, Taipei. 2. Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University Hospital, Taipei. 3. Department of Otolaryngology, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan. 4. Sleep Center, National Taiwan University Hospital, Taipei. 5. Department of Pediatrics, National Taiwan University Hospital, Taipei. 6. Department of Internal Medicine, National Taiwan University Hospital, Taipei. 7. Department of Otolaryngology, National Taiwan University Hospital, Taipei. hsuwc@ntu.edu.tw. 8. Sleep Center, National Taiwan University Hospital, Taipei. hsuwc@ntu.edu.tw.
Abstract
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.
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.