Predicting poor school performance in children suspected for sleep-disordered breathing
Predicting poor school performance in children suspected for sleep-disordered breathing
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DOI：
10.1016/j.sleep.2015.03.021
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发表时间：
2015-09-01
期刊：
影响因子：
4.8
通讯作者：
Urschitz, Michael S.
中科院分区：
文献类型：
--
作者：
Brockmann, Pablo E.;Schlaud, Martin;Urschitz, Michael S.
目的：习惯性打鼾的儿童在学校表现不佳（PSP）的风险更大。我们调查的能力，传统的睡眠呼吸障碍（SDB）的措施，预测PSP在习惯性打鼾children.Methods：汉诺威研究睡眠呼吸暂停在儿童（HASSAC），一个大型的社区为基础的研究，在小学生的数据集进行了回顾性分析。包括所有习惯性打鼾者。根据他们的成绩，孩子们被分为好的和差的学校表现。通过计算受试者工作特征曲线和曲线下面积（AUC），评估父母问卷、家庭脉搏血氧仪和家庭多导睡眠图获得的SDB测量值在预测不良学习表现方面的准确性。最具预测性的单因素被确定，并进入一个预测model.Results：114习惯性打鼾（平均年龄9.6岁，51名男孩），59 PSP。所有研究的SDB测量均显示低准确性（即AUC < 0.8）。观察到的最高AUC为问卷评分0.686，血氧测定因子0.565，多导睡眠图因子0.624。PSP的20个单一的显着的预测因子，五个被选中纳入预测模型。模型的校正后AUC为0.851，未校正AUC为0.826。结论：常规的SDB测量方法，如问卷调查、血氧饱和度测定或多导睡眠监测，对疑似SDB儿童PSP的预测不充分。然而，在临床预测模型中组合因素可以改善预测。这种模型的结果可用于评估发展神经认知障碍的风险，并决定是否有SDB的儿童可能受益于治疗。(C)2015 Elsevier B.V.版权所有。
Objective: Habitually snoring children are at a greater risk of poor school performance (PSP). We investigated the ability of conventional sleep-disordered breathing (SDB) measures for predicting PSP in habitually snoring children.Methods: The dataset of Hannover Study on Sleep Apnea in Childhood (HASSAC), a large community-based study in primary school children, was retrospectively analyzed. All habitual snorers were included. Based on their grades, children were grouped into good and poor school performers. SDB measures obtained by a parental questionnaire, a home pulse oximetry, and a home polysomnography were evaluated for their accuracy in predicting poor school performance by calculating receiver operating characteristic curves and area under this curve (AUC). The most predictive single factors were identified and entered into a prediction model.Results: Of 114 habitual snorers (mean age 9.6 years, 51 boys), 59 had PSP. All investigated SDB measures showed low accuracy (ie, AUC < 0.8). The highest AUC observed was 0.686 for a questionnaire score, 0.565 for an oximetry factor, and 0.624 for a polysomnography factor. Of 20 single significant predictors for PSP, five were selected for inclusion into a prediction model. The model reached an unadjusted AUC of 0.826 and an adjusted AUC of 0.851.Conclusions: Conventional SDB measures obtained with questionnaire, oximetry, or polysomnography may not be sufficiently predictive of PSP in children suspected for SDB. However, combining factors in a clinical prediction model may improve prediction. Results of such a model may be used to assess the risk of developing neurocognitive impairment and to decide whether a child suspected for SDB might benefit from treatment. (C) 2015 Elsevier B.V. All rights reserved.