Pablo E Brockmann1, Martin Schlaud2, Christian F Poets3, Michael S Urschitz4. 1. Working Group on Pediatric Sleep Medicine, Department of Neonatology, University Children's Hospital, Tuebingen, Germany; Department of Pediatrics, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile. Electronic address: pbrockmann@med.puc.cl. 2. Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany. 3. Working Group on Pediatric Sleep Medicine, Department of Neonatology, University Children's Hospital, Tuebingen, Germany. 4. Working Group on Pediatric Sleep Medicine, Department of Neonatology, University Children's Hospital, Tuebingen, Germany; Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center, Johannes Gutenberg University, Mainz, Germany.
Abstract
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.
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.
Authors: Jeong-Hui Park; Eunhye Yoo; Myong-Won Seo; Hyun Chul Jung; Jung-Min Lee Journal: Int J Environ Res Public Health Date: 2021-02-03 Impact factor: 3.390