Obstructive sleep apnea syndrome and growth failure

Objectives To determine predictors of obstructive sleep apnea (OSA) in underweight children and to describe the demographic, clinical, and polysomnographic characteristics of an ethnically diverse population of underweight children with OSA. Study Design Case-control study. Setting University of Texas Southwestern Medical Center and Children’s Medical Center of Dallas Methods Underweight children aged 2 to 18 years who underwent a polysomnogram for suspected OSA between January 2014 and December 2020 were included. Underweight was defined as body mass index <5th percentile per Centers for Disease Control and Prevention guidelines. Children with apnea-hypopnea index <1.0 served as a control group. Univariate and multiple logistic regression analysis was used to determine the predictors of OSA. Significance was set at P < .05. Results An overall 124 children met inclusion criteria: mean age, 6.4 years; 50% female; 44% Hispanic, 31% African American, and 18% Caucasian. A total of 83 children had OSA (apnea-hypopnea index ≥1.0). Height was negatively correlated with OSA (odds ratio, 0.94; 95% CI, 0.88-0.99; P = .02) while allergic rhinitis (odds ratio, 2.97; 95% CI, 1.24-7.08; P = .01) and tonsillar hypertrophy (odds ratio, 3.38; 95% CI, 1.42-8.02; P = .01) were predictors for the presence of OSA. No demographic or clinical characteristics were predictors for severe OSA. Conclusion Underweight children with OSA, as compared with those without OSA, are more likely to have decreased height, tonsillar hypertrophy, and allergic rhinitis. There are no predictors of severe OSA in underweight children. We recommend polysomnography for the diagnosis of OSA in symptomatic underweight children with large tonsils, especially when they have a history of allergies.

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“…6,7 Sequelae of untreated OSA include failure to thrive, neurocognitive deficits, and cardiovascular abnormalities. 1,4,8,9…”

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confidence: 99%

Obstructive Sleep Apnea in Underweight Children

Johnson

Leavitt

Daram

et al. 2021

Otolaryngol.--head neck surg.

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“…Many previous studies have focused on the association between mouth breathing and dentofacial development, according to which mouth breathing could lead to narrow upper arch, longer facial height, steeper mandibular plane angle, and a 2 of 11 more retrognathic mandible [3,4]. In addition, failure to thrive, neurobehavioral problems, and depressive symptoms are also believed to be associated with pediatric OSA [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

et al. 2021

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Adenoid hypertrophy may lead to pediatric obstructive sleep apnea and mouth breathing. The routine screening of adenoid hypertrophy in dental practice is helpful for preventing relevant craniofacial and systemic consequences. The purpose of this study was to develop an automated assessment tool for adenoid hypertrophy based on artificial intelligence. A clinical dataset containing 581 lateral cephalograms was used to train the convolutional neural network (CNN). According to Fujioka’s method for adenoid hypertrophy assessment, the regions of interest were defined with four keypoint landmarks. The adenoid ratio based on the four landmarks was used for adenoid hypertrophy assessment. Another dataset consisting of 160 patients’ lateral cephalograms were used for evaluating the performance of the network. Diagnostic performance was evaluated with statistical analysis. The developed system exhibited high sensitivity (0.906, 95% confidence interval [CI]: 0.750–0.980), specificity (0.938, 95% CI: 0.881–0.973) and accuracy (0.919, 95% CI: 0.877–0.961) for adenoid hypertrophy assessment. The area under the receiver operating characteristic curve was 0.987 (95% CI: 0.974–1.000). These results indicated the proposed assessment system is able to assess AH accurately. The CNN-incorporated system showed high accuracy and stability in the detection of adenoid hypertrophy from children’ lateral cephalograms, implying the feasibility of automated adenoid hypertrophy screening utilizing a deep neural network model.

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“…Pediatric sleep-disordered breathing (SDB) is characterized by loud snoring and or hypopnea at night and is associated with upper airway infections [1], attention disorder [2], and failure to thrive [3]. The pediatric SDB is correlated with pediatric upper respiratory tract infection, failure to thrive, pediatric hypertension [4,5], and attention deficiency [2].…”

Section: Introductionmentioning

confidence: 99%

“…[6]. The association of pediatric SDB caused to increase interest in its role in upper respiratory tract infection [1], and pediatric hypertension [4,5], failure to thrive (increased catabolic status by chronic inflammation) [3], and attention deficiency disorders [2]. Platelet volume correlated with cardiovascular disease [7], and coronary heart disease [8].…”

Section: Introductionmentioning

confidence: 99%

Mean platelet volume levels in children with sleep-disordered breathing: a meta-analysis

2020

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Background: Pediatric sleep-disordered breathing (SDB) correlated with respiratory conditions of snoring and hypopnea. Mean platelet volume (MPV) was an inflammatory marker, related to increased inflammatory condition of pediatric patients. Increase of MPV level may cause failure to thrive or increased upper airway infection rate. The aim of this study was to perform systematic review and meta-analysis to investigate the difference on MPV values for pediatric SDB, and compare the change on MPV after surgery in patients with pediatric SDB. Methods: A systemic review of the studies from PubMed, EMBASE, and Cochrane Library databases was conducted in March 2020, supported by reviewing of published articles for studies comparing MPV in pediatric SDB. Metaanalysis was used to compare the change of MPV in pediatric SDB, and subgroup analysis was also used to compare the MPV decrease after surgeries of adenoidectomy or adenotonsillectomy. Results: There were seven studies included in the review. Six of them including 963 subjects showed that a significant increase of MPV was noted in pediatric SDB compared to those in pediatric non-SDB (P < 0.05). Total standardized mean difference (SMD) in MPV between pediatric SDB and non-SDB was 0.51 (95% CI = 0.30-0.72, P < 0.05). A significant decrease of MPV was found in pediatric SDB patients who underwent surgery (total SMD = − 0.36; 95% CI = − 0.70-0.02, P < 0.05). Decreases of MPV after adenoidectomy and adenotonsillectomy were observed, but only the effect of adenotonsillectomy had a statistical significance (total SMD = − 0.72; 95% CI = − 1.18-0.26, P < 0.05). Conclusion: The MPV was significantly higher in patients with pediatric SDB, indicating the presence of increased platelet activity in pediatric SDB patients. The level of MPV could be reduced by the two surgeries, especially adenotonsillectomy.

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Obstructive sleep apnea syndrome and growth failure

Cited by 36 publications

References 40 publications

Obstructive Sleep Apnea in Underweight Children

Obstructive Sleep Apnea in Underweight Children

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

Mean platelet volume levels in children with sleep-disordered breathing: a meta-analysis

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