Pediatric Sleep Apnea Research Articles

Surgical treatment for pediatric sleep apnea syndrome complicated with Prader-Willi syndrome

Surgical treatment for pediatric sleep apnea syndrome complicated with Prader-Willi syndrome

Extracapsular versus intracapsular tonsillectomy: Outcomes in children with a focus on developmental delay

IntroductionOutcomes following intracapsular tonsillectomy (IT) have not been well established in children with developmental delays. The objective of this study was to compare outcomes and complications between intracapsular and extracapsular tonsillectomy (TT) in pediatric patients with developmental delay (DD) in comparison to non-developmentally delayed children. MethodsThis is a retrospective study of pediatric patients with DD undergoing tonsillectomy between 2016 and 2019 at a tertiary care hospital. This group included patients with Down Syndrome, Autism Spectrum Disorder, other genetic syndromes, and patients with a diagnosis of global developmental delay. Outcomes and complications were analyzed for IT and TT. Results2267 charts were reviewed, and 320 patients were identified with DD. Of those, 72 patients underwent IT and 248 underwent TT. In the DD cohort, the IT group had a shorter length of stay (0.97 vs 1.7 days, p < .0001) and was less likely to receive post-operative narcotic medication (2.8% vs 35%, p < .0001) and corticosteroids (9.7% vs 64%, p < .0001) during their hospital stay. Reductions in emergency room (ER) visits (5.6% vs 10%, p = .21) and post-op bleeding (PTH) (1.4% vs 4.8%, p = .31) for IT vs TT were not statistically significant in the DD group. In the NDD group, fewer patients undergoing IT returned to the ER (11% vs 2.3%, p < .0001) or had PTH (4.8% vs 0.25%, p, 0.0001) as compared to those children undergoing TT. There was no difference between parental report of symptom improvement between the groups (39% vs 33%, p = .39). Analysis of 180 patients with preoperative and postoperative sleep study data revealed post-op Apnea Hypopnea Index (AHI) improved with both techniques (74% TT vs 79% IT, p = .7). There were no differences noted for persistent obstructive sleep apnea (OSA) among the two techniques for both study groups (p = .63). ConclusionChildren with DD undergoing IT have reduced length of stay and reduced inpatient administration of post-operative opioids and steroids. IT has comparable efficacy to TT in treating symptoms of pediatric sleep apnea with a better safety profile. Overall, children undergoing IT return to the operating room less frequently than those undergoing TT. Longer follow-up studies will be needed to evaluate rate of tonsil regrowth, risk of revision surgery and persistence of OSA in these patients.

Pediatric Sleep Apnea: The Overnight Electroencephalogram as a Phenotypic Biomarker.

Pediatric obstructive sleep apnea (OSA) is a prevalent disorder that disrupts sleep and is associated with neurocognitive and behavioral negative consequences, potentially hampering the development of children for years. However, its relationships with sleep electroencephalogram (EEG) have been scarcely investigated. Here, our main objective was to characterize the overnight EEG of OSA-affected children and its putative relationships with polysomnographic measures and cognitive functions. A two-step analysis involving 294 children (176 controls, 57% males, age range: 5–9 years) was conducted for this purpose. First, the activity and irregularity of overnight EEG spectrum were characterized in the typical frequency bands by means of relative spectral power and spectral entropy, respectively: δ1 (0.1–2 Hz), δ2 (2–4 Hz), θ (4–8 Hz), α (8–13 Hz), σ (10–16 Hz), β1 (13–19 Hz), β2 (19–30 Hz), and γ (30–70 Hz). Then, a correlation network analysis was conducted to evaluate relationships between them, six polysomnography variables (apnea–hypopnea index, respiratory arousal index, spontaneous arousal index, overnight minimum blood oxygen saturation, wake time after sleep onset, and sleep efficiency), and six cognitive scores (differential ability scales, Peabody picture vocabulary test, expressive vocabulary test, design copying, phonological processing, and tower test). We found that as the severity of the disease increases, OSA broadly affects sleep EEG to the point that the information from the different frequency bands becomes more similar, regardless of activity or irregularity. EEG activity and irregularity information from the most severely affected children were significantly associated with polysomnographic variables, which were coherent with both micro and macro sleep disruptions. We hypothesize that the EEG changes caused by OSA could be related to the occurrence of respiratory-related arousals, as well as thalamic inhibition in the slow oscillation generation due to increases in arousal levels aimed at recovery from respiratory events. Furthermore, relationships between sleep EEG and cognitive scores emerged regarding language, visual–spatial processing, and executive function with pronounced associations found with EEG irregularity in δ1 (Peabody picture vocabulary test and expressive vocabulary test maximum absolute correlations 0.61 and 0.54) and β2 (phonological processing, 0.74; design copying, 0.65; and Tow 0.52). Our results show that overnight EEG informs both sleep alterations and cognitive effects of pediatric OSA. Moreover, EEG irregularity provides new information that complements and expands the classic EEG activity analysis. These findings lay the foundation for the use of sleep EEG to assess cognitive changes in pediatric OSA.

Automatic Sleep Staging in Children with Sleep Apnea using Photoplethysmography and Convolutional Neural Networks.

Sleep staging is of paramount importance in children with suspicion of pediatric obstructive sleep apnea (OSA). Complexity, cost, and intrusiveness of overnight polysomnography (PSG), the gold standard, have led to the search for alternative tests. In this sense, the photoplethysmography signal (PPG) carries useful information about the autonomous nervous activity associated to sleep stages and can be easily acquired in pediatric sleep apnea home tests with a pulse oximeter. In this study, we use the PPG signal along with convolutional neural networks (CNN), a deep-learning technique, for the automatic identification of the three main levels of sleep: wake (W), rapid eye movement (REM), and non-REM sleep. A database of 366 PPG recordings from pediatric OSA patients is involved in the study. A CNN architecture was trained using 30-s epochs from the PPG signal for three-stage sleep classification. This model showed a promising diagnostic performance in an independent test set, with 78.2% accuracy and 0.57 Cohen's kappa for W/NREM/REM classification. Furthermore, the percentage of time in wake stage obtained for each subject showed no statistically significant differences with the manually scored from PSG. These results were superior to the only state-of-the-art study focused on the analysis of the PPG signal in the automated detection of sleep stages in children suffering from OSA. This suggests that CNN can be used along with PPG recordings for sleep stages scoring in pediatric home sleep apnea tests.

Pediatric Sleep Apnea: A Multidisciplinary Diagnosis with a Multimodality Treatment

Pediatric Sleep Apnea: A Multidisciplinary Diagnosis with a Multimodality Treatment

Epigenetic Alterations in Pediatric Sleep Apnea.

Pediatric obstructive sleep apnea has significant negative effects on health and behavior in childhood including depression, failure to thrive, neurocognitive impairment, and behavioral issues. It is strongly associated with an increased risk for chronic adult disease such as obesity and diabetes, accelerated atherosclerosis, and endothelial dysfunction. Accumulating evidence suggests that adult-onset non-communicable diseases may originate from early life through a process by which an insult applied at a critical developmental window causes long-term effects on the structure or function of an organism. In recent years, there has been increased interest in the role of epigenetic mechanisms in the pathogenesis of adult disease susceptibility. Epigenetic mechanisms that influence adaptive variability include histone modifications, non-coding RNAs, and DNA methylation. This review will highlight what is currently known about the phenotypic associations of epigenetic modifications in pediatric obstructive sleep apnea and will emphasize the importance of epigenetic changes as both modulators of chronic disease and potential therapeutic targets.

Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea.

Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0–13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0–0.04 Hz; low frequency: 0.04–0.15 Hz; and high frequency: 0.15–0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001–0.005 Hz; BW2: 0.028–0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.

Correlation between craniofacial changes and respiratory improvement after nasomaxillary skeletal expansion in pediatric obstructive sleep apnea patients.

The aim of this study was to investigate the correlation between the changes in respiratory function and dimensions of the nasomaxillary complex (NMC) and upper airway (UA) compartments after nasomaxillary skeletal expansion (NMSE) treatment for pediatric patients withobstructive sleep apnea (OSA). Nonobese OSA patients (mean age, 13.6 ± 2.9years; mean body mass index, 18.1 ± 3.0kg/m2); mean apnea-hypopnea index (AHI, 7.0 ± 5.4 events/h) presenting with transverse nasomaxillary constriction were evaluated before and after NMSE using cone-beam computed tomography (CBCT), home sleep test, and modified pediatric sleep questionnaire (m-PSQ). Paired t tests were performed to examine the treatment-related changes in all the parameters, and a multiple regression analysis adjusted for age and sagittal and vertical skeletal patterns was conducted to determine the dimensional parameters to affect the functional improvement. Among 26 patients,NMSE treatment significantly increased NMC dimensions at all tested levels and all UA compartments in CBCT, except glossopharyngeal airway. Concurrently, AHI, oxygen desaturation index, the lowest oxygen saturation (LSaO2), flow limitation (FL), snoring, and m-PSQ were significantly improved. AHI reduction was correlated with UA enlargement with no correlation with NMC expansion, whereas FL reduction was affected by NMC expansion. The minimal cross-sectional area was the most predictive of functional improvement, presenting correlations with AHI, LSaO2, and m-PSQ. NMSE can be a good treatment for pediatric OSA patients when applied to enhance the nasal and pharyngeal airway patencies beyond the NMC, ultimately to improve pharyngeal collapsibility as well as nasal airflow.

Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea.

Given the high prevalence and risk for outcomes associated with pediatric obstructive sleep apnea (OSA), there is a need for simplified diagnostic approaches. A prospective study in 140 children undergoing in-laboratory polysomnography (PSG) evaluates the accuracy of a recently developed system (Sunrise) to estimate respiratory efforts by monitoring sleep mandibular movements (MM) for the diagnosis of OSA (Sunrise™). Diagnosis and severity were defined by an obstructive apnea/hypopnea index (OAHI) ≥ 1 (mild), ≥ 5 (moderate), and ≥ 10 events/h (severe). Agreement between PSG and Sunrise™ was assessed by Bland-Altman method comparing respiratory disturbances hourly index (RDI) (obstructive apneas, hypopneas, and respiratory effort-related arousals) during PSG (PSG_RDI), and Sunrise RDI (Sr_RDI). Performance of Sr_RDI was determined via ROC curves evaluating the device sensitivity and specificity at PSG_OAHI ≥ 1, 5, and 15 events/h. A median difference of 1.57 events/h, 95% confidence interval: -2.49 to 8.11 was found between Sr_RDI and PSG_RDI. Areas under the ROC curves of Sr_RDI were 0.75 (interquartile range [IQR]: 0.72-0.78), 0.90 (IQR: 0.86-0.92) and 0.95 (IQR: 0.90-0.99) for detecting children with PSG_OAHI ≥ 1, PSG_OAHI ≥ 5, or PSG_ OAHI ≥ 10, respectively. MM automated analysis shows significant promise to diagnose moderate-to-severe pediatric OSA.

Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis

Pediatric Obstructive Sleep Apnea (OSA) is a respiratory disease whose diagnosis is performed through overnight polysomnography (PSG). Since it is a complex, time-consuming, expensive, and labor-intensive test, simpler alternatives are being intensively sought. In this study, bispectral analysis of overnight airflow (AF) signal is proposed as a potential approach to replace PSG when indicated. Thus, our objective was to characterize AF through bispectrum, and assess its performance to diagnose pediatric OSA. This characterization was conducted using 13 bispectral features from 946 AF signals. The oxygen desaturation index ≥3% (ODI3), a common clinical measure of OSA severity, was also obtained to evaluate its complementarity to the AF bispectral analysis. The fast correlation-based filter (FCBF) and a multi-layer perceptron (MLP) were used for subsequent automatic feature selection and pattern recognition stages. FCBF selected 3 bispectral features and ODI3, which were used to train a MLP model with ability to estimate apnea-hypopnea index (AHI). The model reached 82.16%, 82.49%, and 90.15% accuracies for the common AHI cut-offs 1, 5, and 10 events/h, respectively. The different bispectral approaches used to characterize AF in children provided complementary information. Accordingly, bispectral analysis showed that the occurrence of apneic events decreases the non-gaussianity and non-linear interaction of the AF harmonic components, as well as the regularity of the respiratory patterns. Moreover, the bispectral information from AF also showed complementarity with ODI3. Our findings suggest that AF bispectral analysis may serve as a useful tool to simplify the diagnosis of pediatric OSA, particularly for children with moderate-to-severe OSA.

Relating three-dimensional airway measurements to the apnea-hypopnea index in pediatric sleep apnea patients.

(a) To evaluate three-dimensional radiographic airway analysis as it relates to the pre-test probability for sleep apnea in pediatric patients, and (b) to develop cut-off values for measurements showing promising results. A consecutive series of pediatric patients between the ages of 7 and 17years, referred for a sleep study were recruited. Cone beam computed tomography (CBCT) scans were acquired for 103 subjects within one month following the sleep study. Three-dimensional airway analysis was performed including volumetric, area and linear measurements. Correlations with the apnea-hypopnea index (AHI) and receiver operating characteristic (ROC) curves were constructed. Sensitivity and specificity were calculated for prediction of AHI≥5 and AHI≥10. 99 CBCT scans were included (median age=11years). The nasopharyngeal volume (NPV) significantly correlated with AHI (rho≈-0.4, P<.05). In subjects aged 7-11years, proposed cut-off values for NPV are 2400mm3 and 1600mm3 for AHI≥5 and AHI≥10, respectively. In subjects aged 12-17years, proposed cut-off values for NPV are 3500mm3 and 2700mm3 for AHI≥5 and AHI≥10, respectively. Oropharyngeal cross-sectional area (OCSA) demonstrated significant predictive value in ROC curve analysis, and cut-off values for this airway measure are also proposed. Contrary to findings in adults, the NPV shows promise when screening for sleep apnea in children when CBCT scans are available. The OCSA might also be of value when screening for sleep apnea especially in older children.

Comparing two pediatric sleep questionnaires: The Pediatric Sleep Questionnaire (PSQ) and a set of 6 hierarchically arranged questions (6Q)

ABSTRACT Objective To compare the Pediatric Sleep Questionnaire (PSQ) and a less time-consuming set of 6 hierarchically arranged questions (6Q) as they relate to the pre-test probability for sleep apnea in pediatric patients. Methods Parents of 116 subjects between the ages of 7 and 17 answered two sleep questionnaires (the PSQ and the 6Q) distributed in random order before the subjects had sleep studies. Correlation coefficients were used for apnea-hypopnea index (AHI) prediction, while the area under the curve (AUC) was calculated for sleep apnea classification prediction. Results The 6Q showed statistical significance, while the more commonly used PSQ did not, both in terms of correlating with AHI (rho = 0.294, p = 0.001) and predicting moderate and severe sleep apnea (AUC = 0.650 and 0.788, respectively). Conclusion Although additional field validation is required, these pediatric sleep questionnaires are sensitive and easy-to-use screening tools that can greatly help in the screening for pediatric sleep apnea.

Automatic Assessment of Pediatric Sleep Apnea Severity Using Overnight Oximetry and Convolutional Neural Networks.

In this study, we use the overnight blood oxygen saturation (SpO2) signal along with convolutional neural networks (CNN) for the automatic estimation of pediatric sleep apnea-hypopnea syndrome (SAHS) severity. The few preceding studies have focused on the application of conventional feature extraction methods to obtain information from the SpO2 signal, which may omit relevant data related to the illness. In contrast, deep learning techniques are able to automatically learn features from raw input signal. Thus, we propose to assess whether CNN, a deep learning algorithm, could automatically estimate the apnea-hypopnea index (AHÍ) from nocturnal oximetry to help establish pediatric SAHS presence and severity. A database of 746 SpO2 recordings is involved in the study. CNN was trained using 20-min segments from the SpO2 signal in the training set (400 subjects). Hyperparameters of the CNN architecture were tuned using a validation set (100 subjects). This model was applied to a test set (246 subjects), in which the final AHI of each patient was obtained as the average of the output of the CNN for all the segments of the corresponding SpO2 signal. The AHI estimated by the CNN showed a promising diagnostic performance, with 74.8%, 90.7%, and 95.1% accuracies for the common AHI severity thresholds of 1, 5, and 10 events per hour (e/h), respectively. Furthermore, this model reached 28.6, 32.9, and 120.0 positive likelihood ratios for the above-mentioned AHI thresholds. This suggests that the information extracted from the oximetry signal by deep learning techniques may be useful to both establish pediatric SAHS and its severity.

A different sleep apnea classification system with neural network based on the acceleration signals

Background and objectiveThe apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. MethodsIn this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical system (MEMS) based acceleration sensor. It records the value of acceleration by measuring the movements of the diaphragm in three axes during the respiratory. The measurements are carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement results. An artificial neural network model was designed to determine the apnea event. For the number of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer. ResultsA study group was formed of 5 patients (having different characteristics (age, height, and body weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data. ConclusionsThe sleep apnea event detection system is presented by using ANN from directly acceleration values. Measurements are performed by the MEMS-based accelerometer and Industrial Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients. The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with 1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark shows us that trained ANN successfully detects apnea events. One of the contributions of this study to literature is that only ACC data are used in the ANN training step. After training for one patient, the ANN system can monitor the apnea event situation on-line for others.

Popularity of sleep disordered breathing in childhood: an analysis of worldwide search using Google Trends.

Sleep-disordered breathing (SDB) is a general term encompassing a variety of breathing problems occurring during the sleep and characterized by frequently reported signs such as sleep apneas and snoring. Published studies on this topic are increasing over time, as demonstrated by the number of papers available on on-line databases. Google Trends is a freely accessible and reliable online portal, which allows to explore internet search data. Due to the focus on of childhood SDB in published research, and because of its impact on health and wellbeing, this original study aims at investigating the worldwide popularity of most common sleep signs (apnea and snoring) from January 1st, 2004 to present time. The trend of interest for 'sleep', 'sleep apneas' and 'snoring' limited to pediatric age was lower than its overall popularity in the general population, thus reflecting a lower interest of these symptoms in children than in adults. Unlike an increasing trend of ongoing research, the popularity of pediatric sleep apnea and snoring was found to be relatively low around the world, and remained fairly stable over time. This is a matter for concern, since the low popularity of SDB in children is counterbalanced by a need for reinforcing educational programs aimed at increasing widespread awareness, diagnosis and management of this condition.

A Novel Wearable Real-Time Sleep Apnea Detection System Based on the Acceleration Sensor

Background and objectiveThe apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that, it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. MethodsIn this study, a device is developed to detect apnea events to alert the patient. The device records continuously accelerations on the diaphragm by using an acceleration sensor, which is placed on the patient's diaphragm. When the apnea is detected by the accelerometer-based system, a signal is sent to the wristband, and then the vibration motor on the wristband vibrates until the patient starts breathing again. The force of the vibration motor can be adjusted according to the patients' sleep debt, especially for elderly, disabled, or pediatric patients. There is no need for a sleeping room to see the patients' breathing properties since those parameters can be stored by using the developed device on a secure digital memory card (SD) at patients' homes during sleeping. ResultsA study group were formed of 10 patients (4 males (40%) and 6 females (60%)) with different characteristics ([mean] age, 36.3; height, 169.6 cm; and body weight, 81.4 kg). The patients in the study group have sleep apnea (SA). All the apnea events were detected, and all the patients were successfully alerted. Also, the lying position, which is a significant issue, is performed in this study. ConclusionsThis work proposed using an acceleration sensor as a reliable method of sleep apnea screening, detection of an apnea event, sending alert to the patient, and detection of the patient lying position. The developed device is more economical, comfortable, and convenient than existing systems for not only the patients but also the doctors. The patients can easily use this device in their home environment.

Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis

Background and objectiveIn-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). Methods946 AF and blood oxygen saturation (SpO2) recordings from children ages 0–13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea–hypopnoea index (AHI). Results8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. ConclusionsRP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.

Impact of montelukast and fluticasone on quality of life in mild pediatric sleep apnea

ObjectivesResearch has shown improvement in apnea-hypopnea index in children with mild obstructive sleep apnea treated with anti-inflammatory medications. Data on quality of life outcomes in children receiving these medications is lacking. We aim to assess quality of life in children with mild obstructive sleep apnea treated with montelukast and fluticasone. MethodsChildren between 3 and 16 years old with mild sleep apnea (apnea-hypopnea index > 1 and ≤ 5) presenting to a pediatric otolaryngology clinic were recruited prospectively and treated with 4 months of montelukast and fluticasone. Subjects’ caregivers completed the OSA-18, a validated quality of life survey, at baseline and 4 months. Children with ongoing obstruction at follow-up underwent adenotonsillectomy. ResultsThirty-one patients were included. Mean (SD) age was 6.8 (3.9) years. Most subjects (54.8%) were black and 48% were obese. Mean (SD) apnea-hypopnea index of the subjects was 2.8 (1.0). The mean (SD) baseline OSA-18 score was 60.2 (18.5), indicating a moderate impact of sleep disturbance on quality of life. Following treatment, there was significant improvement (p < 0.005) in mean OSA-18 score. Four children discontinued montelukast due to behavioral side effects. Seven children (22%) underwent adenotonsillectomy after failing medical therapy. Demographic factors such as obesity [OR 0.63 (0.11, 3.49)] and apnea hypopnea index [OR 1.38 (0.59, 3.66)] failed to predict which children would respond to anti-inflammatory medications. ConclusionsChildren with mild obstructive sleep apnea treated with montelukast and fluticasone experience significant improvements in quality of life. Further research is needed to determine optimal duration of therapy.

Developing pediatric three-dimensional upper airway normative values using fixed and interactive thresholds.

To develop and compare pediatric upper airway three-dimensional normative values using the two most commonly used cone beam computed tomography (CBCT) software: Invivo5 (fixed threshold) and Dolphin 3D (interactive threshold). Out of 3738 CBCT scans, scans of 81 pediatric patients were utilized after applying strict exclusion criteria. The sample was grouped into two age groups (7-11 and 12-17years). Intra-class correlation coefficient was used to test intra-rater and inter-rater reliability and showed coefficients greater than 0.9 indicating good reliability of the methods used. Paired t tests showed that volumetric and area measurements obtained using Dolphin 3D were significantly larger than those obtained using Invivo5 (p < 0.05). The mean minimal cross-sectional areas (MCSA) for Dolphin 3D were 151mm2 and 177mm2 for age groups 1 and 2, respectively. The mean MCSA values for Invivo5 for age groups 1 and 2 were 120mm2 and 145mm2, respectively. Pediatric upper airway volumetric, area, and linear measurements were reported after applying strict exclusion criteria including a validated sleep questionnaire. Our goal is that clinicians utilize the proposed-here normative values for screening and assist in the timely diagnosis and management of pediatric sleep apnea.

Simplification of the severity hierarchy score for pediatric sleep apnea screening

Pediatric Obstructive Sleep Apnea is a common condition. However, it remains under diagnosed exposing to associated morbidities. Polysomnography is the standard diagnostic test, but its availability is sometimes limited, especially in poor countries.The questionnaire proposed by Spruyt and Gazal has been validated as a screening tool by many studies. A score above 2.75 have an 82% sensitivity, a 81% specificity and a negative predictive value of 92% for detecting moderate OSA. However, the way of calculating the score seems to us rather complicated and may limit its use in daily practice.Therefore, we propose, for calculating the score, a table that may be printed in sheets of paper. We propose a simple modification that could, in our opinion; greatly facilitate the use of this screening tool by reducing the use of calculators and the risks of errors. In this way, we hope to make its use much easier in everyday practice