Unchiti Kantarakorn, Samerchua Artid, Pipanmekaporn Tanyong, Leurcharusmee Prangmalee, Sonsuwan Nuntigar, Phinyo Phichayut, Patumanond Jayanton
Department of Anesthesiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Department of Otolaryngology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Sleep Breath. 2024 Dec 5;29(1):48. doi: 10.1007/s11325-024-03226-7.
Undetected obstructive sleep apnea (OSA) in children increases the likelihood of perioperative respiratory complications. Current screening tools for OSA often lack sensitivity or are overly complex. This study aimed to develop and validate a simplified preoperative predictive model for moderate-to-severe pediatric OSA.
The study included children aged 1 to 18 years who underwent either polysomnography or nocturnal pulse oximetry from January 2013 to December 2020. OSA severity was categorized using these tests, and potential predictors were identified using multivariable logistic regression. The outcomes of the tests were used to create a risk-based scoring system. Internal validation was performed using bootstrapping procedures.
Out of the 1,327 participants, 882 individuals (66.5%) were diagnosed with moderate-to-severe OSA. Predictors considered for developing the scoring system included Craniofacial abnormalities, adenotonsillar Hypertrophy, Age 1-5 years, Snoring > 5 nights/week, Excessive daytime sleepiness, Obesity, Stopping breathing, and Awakening during sleep (CHASE-OSA). The scoring system developed demonstrated an area under the receiver operating characteristic curve of 0.85 (95% CI: 0.83-0.88). The CHASE-OSA score, ranging from 0 to 14, classified scores < 6 as low-risk and ≥ 6 as high-risk for moderate-to-severe pediatric OSA. This cutoff demonstrated a sensitivity of 86%, specificity of 70%, and positive and negative predictive values of 85% and 71%, respectively.
The CHASE-OSA predictive model provides a concise and user-friendly preoperative screening tool for identifying moderate-to-severe pediatric OSA. It facilitates risk assessment, enhances perioperative care optimization, and informs postoperative management planning. Further research is needed to comprehensively validate its clinical utility.
儿童未被诊断出的阻塞性睡眠呼吸暂停（OSA）会增加围手术期呼吸并发症的发生几率。目前用于OSA的筛查工具通常缺乏敏感性或过于复杂。本研究旨在开发并验证一种用于中重度小儿OSA的简化术前预测模型。
该研究纳入了2013年1月至2020年12月期间接受多导睡眠图或夜间脉搏血氧饱和度监测的1至18岁儿童。通过这些测试对OSA严重程度进行分类，并使用多变量逻辑回归确定潜在预测因素。测试结果用于创建基于风险的评分系统。使用自助法进行内部验证。
在1327名参与者中，882人（66.5%）被诊断为中重度OSA。用于开发评分系统的预测因素包括颅面异常、腺样体扁桃体肥大、1至5岁、每周打鼾超过5晚、白天过度嗜睡、肥胖、呼吸暂停和睡眠中觉醒（CHASE-OSA）。所开发的评分系统在受试者工作特征曲线下的面积为0.85（95%可信区间：0.83-0.88）。CHASE-OSA评分范围为0至14分，将<6分分类为中重度小儿OSA的低风险，≥6分为高风险。该临界值的敏感性为86%，特异性为70%，阳性预测值和阴性预测值分别为85%和71%。
CHASE-OSA预测模型为识别中重度小儿OSA提供了一种简洁且用户友好的术前筛查工具。它有助于风险评估，加强围手术期护理优化，并为术后管理规划提供信息。需要进一步研究以全面验证其临床效用。
