RATIONALE:Access to polysomnography, the recommended standard for the diagnosis of OSA in children, is limited in many jurisdictions. Many children undergo treatment for OSA without confirmatory testing, are denied treatment in the absence of testing, or have a delay in treatment of other sleep disorders until OSA can be ruled out. METHODS:An expert panel conducted a systematic review and meta-analysis examining alternative testing to polysomnography. Recommendations formulation followed a process of proposal, discussion, revisions and voting, considering the evidence, panel members' judgment, as well as patient and family preferences. RESULTS:A total of 250 articles across 5 types of clinical assessment were included. Most articles excluded children with comorbidities or did not report exclusions (62% and 26%, respectively). Only 13% of articles included children under 2-years of age. Limited data were appropriate for meta-analysis. CONCLUSIONS:The panel upholds polysomnography as the recommended standard for the diagnosis of OSA in children given the absence of an alternative test that could be considered a replacement. Level 3/Home Sleep Apnea Testing is recommended as an alternative option to diagnose OSA in otherwise healthy children over 5-years of age for whom access to polysomnography is effectively absent. While the Pediatric Sleep Questionnaire and overnight oximetry add information to the clinical assessment, we suggest that these should not be used to diagnose OSA in children. Given patient and family preferences for testing at home, further investment is needed in developing accurate and accessible home-based testing options to diagnose OSA in children.

BACKGROUND:Obstructive sleep apnoea (OSA) in children is diagnosed using polysomnography (PSG), but children with neurodisability are >3 times more likely to not tolerate leads and sensors. The diagnostic accuracy of Sunrise, a small sensor applied to the chin, has not been assessed in this special population. OBJECTIVE:Compare the diagnostic accuracy of Sunrise to simultaneously recorded in-laboratory PSG. METHODS:We conducted a prospective, cross-sectional study of children aged 3-18 years with neurodisability, who attended the Queensland Children's Hospital sleep laboratory for diagnostic PSG between February 2024 and September 2025. Sunrise- and PSG-derived measures of sleep disordered breathing were compared, and questionnaire data examining interest in novel sensor technology, and portable, home-based sleep studies were also collected. RESULTS:There were 48 children with a neurodisability (mean [SD] age = 11.2 [3.6]; 39% female). For 43 children with complete Sunrise data, sensitivity and specificity for identifying moderate-severe obstructive sleep apnoea (OSA) using calibrated MM-ORDI were 0.88 (95% CI: 0.45-1.00) and 0.63 (95% CI: 0.45-0.79), respectively. The concordance correlation coefficient of the obstructive respiratory disturbance index was 0.65 (95% CI: 0.5-0.8) and the median absolute error was 5.7 (IQR: 2.7-13.1) events per hour. Most carers in attendance preferred in-laboratory sleep studies (56%) to home (33%), but the majority preferred Sunrise (50%) to limited-channel (17%) or full PSG (10%). CONCLUSION:Sunrise shows excellent ability to exclude moderate-severe OSA in children with neurodisability with moderate specificity. There is substantial appetite among parents and carers for alternative sleep testing modalities.

Background: Biological age is a key concept in the development of biomarkers of health and disease. We develop a prediction of the functional autonomic age (FAA) from infancy to adolescence based on the ECG-derived tachogram recorded at the onset of N2 sleep. Methods: A cohort of ECG recordings from 1004 typically developing infants, children and adolescents (age range: 1 month to 17 years) was used to train feature-based and deep neural network-based regression models for the prediction of FAA. Weighted mean absolute error (wMAE) was used to define accuracy and evaluated with 10-fold cross-validation. Effect size was used to compare model accuracies and linear regression was used to evaluate confounds. The combination of FAA with an EEG-based estimate of functional brain age (FBA) was also tested. Results: A feature-based FAA had a wMAE of 1.78 years (95 %CI: 1.62-1.93, n = 1004) and was comparable to deep neural network regression (wMAE = 1.85 years, 95 %CI: 1.66-1.98). Accuracy was affected by age and age2 (t = -4.97, p < 0.001 and t = 9.66, p < 0.001, respectively) with smaller errors at younger ages, but not biological sex (t = -0.660, p = 0.510). Combining the FAA with a functional brain age derived from the EEG resulted improved accuracy, with neural networks-based methods superior (wMAE of 0.81 years, 95 %CI: 0.73-0.88, effect size D = 0.77, 95 %CI: 0.70-0.84, n = 1004). Conclusion: FAA derived from the tachogram accurately represents age from infancy to adolescence. The combination of FAA with FBA improves age prediction accuracy.

AIMS:Asthma affects > 10% of children in Australia and New Zealand (NZ), with up to 5% of those having severe disease, presenting a management challenge. We aimed to survey tertiary paediatric respiratory services across Australia and NZ using a custom-designed questionnaire, to conduct a cross-sectional observational study of the numbers of children with problematic severe asthma (PSA) seen, the number treated with biologic therapy, outpatient clinic/multidisciplinary team (MDT) services available, investigations and tools routinely used and approaches utilised for transition to adult care. METHODS:A custom-designed online survey was distributed via email to Directors of public paediatric respiratory services across Australia and NZ (n = 14). Reminders to prompt completion were emailed regularly over 3 months. RESULTS:All sites provided survey responses. The estimated number of children with PSA across 12 sites was 561 (53 prescribed biologic treatment); two sites felt unable to provide accurate estimations. Most sites (n = 8) did not have a MDT approach either as MDT clinics or meetings; patients were managed in either asthma (n = 7, 50%) or general respiratory clinics. Most sites (85%) utilised questionnaires regarding asthma control for assessment, although some utilised additional questionnaires. Remaining tools and investigations varied widely across centres. Only four sites (29%) had established processes for transition to adult care. CONCLUSIONS:Given notable heterogeneity in service availability and PSA management across tertiary sites, children may experience variability in care dependent on their location. Most centres lack MDT models of care, which are considered the international best-practise standard for management of children with PSA.

PURPOSE:In-laboratory polysomnography (PSG) is the gold standard test for diagnosing certain paediatric sleep conditions. Children with neurodevelopmental disorders (NDD) often have difficulty tolerating PSG, but parent and patient experiences of PSG for children with NDD have not been thoroughly explored. The study aim was to evaluate the parent-reported experience of in-laboratory PSG undertaken in children with NDD and to identify factors predictive of poorer experience. METHODS:In this cross-sectional multicentre study, parents of 143 children with NDD who underwent in-laboratory PSG completed a customised survey to provide feedback on parent and child worry levels, subjective tolerance and overall experience of PSG, and hypothetical preference between in-laboratory PSG versus an in-home mat-based sleep test. ANOVA, Chi-squared and Kruskal-Wallis tests were used to determine participant factors associated with these outcomes. RESULTS:On average, parents rated their child's worry level with respect to undergoing PSG as 'moderate,' but their own worry levels lower. Autism spectrum / neuromuscular disorder diagnoses were risk factors for both higher worry score and reporting that sleep during PSG was non-representative of usual sleep at home. Parental preference was for in-home (mat-based) testing, with 57% indicating a preference for this if it wereavailable vs. 7% preferring in-laboratory testing. CONCLUSION:Parent/carer reports regarding in-laboratory PSG experiences for their children with NDD suggest the test is associated with child worry and concerns that the sleep is not-representative of usual sleep at home. Consumer preference favours in-home sleep study testing over current in-laboratory diagnostic testing. CLINICAL TRIAL REGISTRATION:This study is part of a larger trial ACTRN12622001544763.

SummaryPositional obstructive sleep apnea, in which there is a ≥ 2:1 predominance of obstructive events in the supine position, is a sleep‐disordered breathing phenotype with a targeted treatment in the form of positional device therapy. We sought to determine the prevalence of positional obstructive sleep apnea in a cohort of children prescribed continuous positive airway pressure therapy, ascertain risk factors for the condition, and determine the associated continuous positive airway pressure treatment adherence rate. A retrospective cohort study of all children > 2 years old from a single tertiary paediatric centre prescribed continuous positive airway pressure therapy over an 8‐year period was conducted. Positional obstructive sleep apnea prevalence was established by analysing positional and respiratory event data from the participants' original diagnostic polysomnography. Continuous positive airway pressure therapy adherence was determined using data from machine download. Univariable and multivariable logistic regression modelling was used to determine participant demographic and clinical factors associated with positional obstruction. Positional obstructive sleep apnea (defined by Bignold's criteria modified for paediatric use) prevalence in the cohort of 237 analysed participants was 38%. Suboptimal continuous positive airway pressure adherence was noted in 30% of this group based on initial machine download, performed a median of 96 days post‐treatment initiation. Higher age and lower obstructive apnea–hypopnea index were independent predictors of positional obstructive sleep apnea, whereas neurodevelopmental diagnosis, presence/absence of rapid eye movement‐related obstructive sleep apnea, overweight/obesity status and history of adenoidectomy/adenotonsillectomy were not. For children, positional device therapy is a treatment option worthy of further consideration and research.

U-Sleep is a publicly available automated sleep stager, but has not been independently validated using pediatric data. We aimed to (1) test the hypothesis that U-Sleep performance is equivalent to trained humans, using a concordance dataset of 50 pediatric polysomnogram excerpts scored by multiple trained scorers, and (2) identify clinical and demographic characteristics that impact U-Sleep accuracy, using a clinical dataset of 3,114 polysomnograms from a tertiary center. Agreement between U-Sleep and “gold” 30-second epoch sleep staging was determined across both datasets. Utilizing the concordance dataset, the hypothesis of equivalence between human scorers and U-Sleep was tested using a Wilcoxon 2 1-sided test. Multivariable regression and generalized additive modeling were used on the clinical dataset to estimate the effects of age, comorbidities, and polysomnographic findings on U-Sleep performance. The median (interquartile range) Cohen’s kappa agreement of U-Sleep and individual trained humans relative to “gold” scoring for 5-stage sleep staging in the concordance dataset were similar, kappa = 0.79 (0.19) vs 0.78 (0.13), respectively, and satisfied statistical equivalence (2 1-sided test P < .01). Median (interquartile range) kappa agreement between U-Sleep 2.0 and clinical sleep-staging was kappa = 0.69 (0.22). Modeling indicated lower performance for children < 2 years, those with medical comorbidities possibly altering sleep electroencephalography (kappa reduction = 0.07–0.15) and those with decreased sleep efficiency or sleep-disordered breathing (kappa reduction = 0.1). While U-Sleep algorithms showed statistically equivalent performance to trained scorers, accuracy was lower in children < 2 years and those with sleep-disordered breathing or comorbidities affecting electroencephalography. U-Sleep is suitable for pediatric clinical utilization provided automated staging is followed by expert clinician review. Kevat A, Steinkey R, Suresh S, et al. Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network. J Clin Sleep Med. 2025;21(2):277–285.

To assess whether the number of nights utilised for initiation and titration of continuous positive airway pressure (CPAP) therapy (and/or the use of an additional ‘parent night’) for patients attending the Queensland Children’s Hospital (QCH) Sleep Unit was an independent predictor of: 1. Subsequent adherence to therapy (primary outcome) 2. Amount of readjustment of the therapy at subsequent visit A retrospective review of children initiated on CPAP from 2015 to 2022 was performed using existing data extracted from the QCH Sleep Unit records. Children aged <18 years at commencement of CPAP were included. Demographic data, number of nights for initiation, change in CPAP prescription at next titration study, and adherence data were included in the study. From 376 patients assessed, results demonstrated that single night initiation (p-value 0.03), later year of initiation (p-value 0.03), and comorbid neurodisability diagnosis (p-value 0.03) predicted non-adherence on univariable analysis, but these factors did not retain statistical significance in multivariable modelling. Parent nights were more likely in those receiving multi-night titration studies (30

Childhood sleep electroencephalography (EEG) reveals brain maturation patterns aligned with age, offering a window into development at the bedside. Leveraging this, we used supervised neural networks to predict age from overnight EEG and derive a Functional Brain Age (FBA) across wake, NREM (N1-N3), and REM sleep in 814 children with clinically normal sleep studies. We evaluated how FBA varies across sleep architecture and assessed the accuracy of neural networks, EEG channels, sleep segments, data quality, quantitative EEG features, and explainability methods on prediction performance. Prediction accuracy varied developmentally, with a mean absolute error (MAE) of 0.78 years in infancy (0-2 years), 0.87 years in childhood (2-12 years), and 1.55 years in adolescence (12-18 years), yielding an overall MAE of 0.96 years (95CI 0.90-1.01). FBA fell within ± 25% of chronological age in over 95% of children, with highest accuracy (< 1 year MAE) during N2, N3, and REM stages that reflect well-defined developmental EEG changes such as delta power and spindles. FBA reliability was shaped by signal quality and stable, age-specific patterns across sleep stages. Explainability analyses showed that network activations aligned with quantitative EEG features, supporting the biological validity of FBA. These findings support scalable, non-invasive tools that use sleep EEG to track brain maturation as an objective marker of neurodevelopmental health.

Abstract Background Positional obstructive sleep apnoea (POSA) is an obstructive sleep apnoea (OSA) phenotype in which most obstructive events occur in supine sleep. For adults, recent guidelines and trials suggest positional OSA devices are suitable treatment alternatives to continuous positive airway pressure (CPAP). We sought to determine prevalence of POSA and associated rates of suboptimal CPAP adherence in children. Methods This single centre retrospective cohort study included all children >2 years of age prescribed CPAP from 01/01/2020–01/01/2023 through Queensland Children’s Hospital (QCH) for moderate-severe OSA. Children were classified as having POSA or not through examination of obstructive events and sleep position determined by their diagnostic polysomnography. POSA was defined according to three different established diagnostic criteria (Cartwright’s, Bignold’s and Amsterdam POSA criteria), modified for paediatric use. CPAP adherence was defined as on average >4 hours usage for ≥5 nights/week. Results Of 120 eligible participants (median age = 13 years), 34% were female, 62% were obese, 33% had a syndromic or neurodevelopmental diagnosis and 48% had undergone adenoidectomy +/- tonsillectomy. Depending on POSA definition, 34%-68% (41-73) of the 120 participants had POSA. Two-thirds (64-68%, depending on POSA definition used) of those with POSA had optimal CPAP adherence based on their first CPAP download. Conclusion A significant proportion of children with OSA prescribed CPAP at QCH have POSA, some of whom struggle to maintain CPAP adherence. Positional device therapy may represent an appropriate alternative management strategy. However, well-designed prospective trials in children are needed.

BACKGROUND:Healthy sleep is vital for optimal child development, yet over 30% of Australian parents report having children with disrupted sleep affecting all family members. These sleep difficulties might co-exist with sleep breathing disorders, contributing to morbidity and reduced quality of life.OBJECTIVE:This article aims to provide general practitioners (GPs) with an evidence-based, biopsychosocial approach to managing common sleep problems in infants and preschool-aged children.DISCUSSION:Strategies and techniques are outlined to aid GPs in promoting healthy sleep during infancy, educating parents on typical sleep patterns and supporting families in managing problematic sleep patterns in toddlers. Emphasis is placed on a tailored approach to developing a healthy sleep environment to meet the child's needs and parental values. Valuable resources and indications for specialist consultation are included.

Background Adolescence is a stage of significant transition as children develop into young adults. Optimal sleep is crucial during this period to ensure physical, emotional and mental wellbeing. However, it is well recognised that insufficient quality and quantity of sleep is common among adolescents worldwide. Objective This article aims to provide general practitioners with an overview of the key issues encountered in adolescent patients relating to sleep and summarises approaches to assessment and evidence based management of sleep problems in this population. Discussion This review highlights the physiological changes that affect sleep during adolescence and how other factors, including unhealthy sleep behaviours, influence these. It discusses the importance of healthy sleep and the consequences of sleep disturbance in adolescents. Management strategies are outlined, focusing on the key common issues that affect sleep in the teenage years, and guidance on when to consider co-management with specialist care is provided.

Abstract Background Positional obstructive sleep apnoea (POSA) is an obstructive sleep apnoea (OSA) phenotype in which most obstructive events occur in supine sleep. For adults, recent guidelines and trials suggest positional OSA devices are suitable treatment alternatives to continuous positive airway pressure (CPAP). We sought to determine prevalence of POSA and associated rates of suboptimal CPAP adherence in children. Methods This single centre retrospective cohort study included all children >2 years of age prescribed CPAP from 01/01/2020–01/01/2023 through Queensland Children’s Hospital (QCH) for moderate-severe OSA. Children were classified as having POSA or not through examination of obstructive events and sleep position determined by their diagnostic polysomnography. POSA was defined according to three different established diagnostic criteria (Cartwright’s, Bignold’s and Amsterdam POSA criteria), modified for paediatric use. CPAP adherence was defined as on average >4 hours usage for ≥5 nights/week. Results Of 120 eligible participants (median age = 13 years), 34% were female, 62% were obese, 33% had a syndromic or neurodevelopmental diagnosis and 48% had undergone adenoidectomy +/- tonsillectomy. Depending on POSA definition, 34%-68% (41-73) of the 120 participants had POSA. One-third (32-36%, depending on POSA definition used) of those with POSA had suboptimal CPAP adherence based on their first CPAP download. Conclusion A significant proportion of children with OSA prescribed CPAP at QCH have POSA, some of whom struggle to maintain CPAP adherence. Positional device therapy may represent an appropriate alternative management strategy. However, well-designed prospective trials in children are needed.

Osteogenesis imperfecta (OI) is a rare presentation in the pediatric population. Whilst orthopedic manifestations are well-publicised, the multiple respiratory complications and mechanisms of respiratory failure in more severe cases are less well described. We report the clinical, radiological and histopathological details of the case of an infant with genetically-confirmed OI (Type 2) and associated respiratory insufficiency, as well as summarise the relevant existing literature. This case highlights the importance of the recognition of clinical challenges associated with the management of respiratory complications in a patient with OI.

In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer physiologically reliable measures of brain function. Here, we develop a novel measure of functional brain age (FBA) using a residual neural network based interpretation of the pediatric EEG. We show that the FBA from a 10 to 15 minute segment of 18-channel EEG during light sleep (stages 1 and 2) in typically developing children and adolescents was strongly associated with chronological age (R 2 = 0.96, 95%CI: 0.94 - 0.96, n = 1062, age range: 1 month to 18 years). The mean absolute error (MAE) between FBA and age was 0.6 years ( n = 1062), with an MAE of 2.1 years following validation on an independent set of EEG recordings ( n = 723). The FBA detected group level maturational delays in a small cohort of children with abnormal neurodevelopment ( p = 0.00053, n = 40). Our work offers a practical, scalable and powerful automated tool for tracking maturation of brain function throughout childhood with an accuracy comparable to that of widely used physical growth charts.

Abstract Background In-laboratory polysomnography (PSG) remains the gold-standard method for the detection of significant paediatric sleep-disordered breathing, although it is resource-intensive. We therefore trained, using data from 2380 diagnostic PSGs, a support vector regression (SVR) algorithm to predict apnoea hypopnea index (AHI) using oximetry data alone, for potential use as a pre-PSG screening/triage tool. This study aimed to test the algorithm’s performance in detecting potentially significant sleep apnoea, defined as AHI ≥5. Methods We extracted oximetry data (oxygen saturation and heart rate, down-sampled to 1Hz) from 300 PSGs performed in 2018 in a single Australian paediatric centre. De-identified demographic and clinical information was extracted from medical records. Oximetry data was then evaluated by the SVR algorithm, which utilised 19 heart rate and oxygen saturation-derived features to predict AHI. The SVR-predicted AHI was compared to gold-standard AHI determined by original PSG, with AHIs undergoing binary classification into AHI <5 or ≥5. Results Of 297 patients (median age 7.4 years), 26% had a significant neuromuscular, neurologic or genetic disorder diagnosed, 15% had undergone previous adenoidectomy+/-tonsillectomy and 14% were obese. On PSG 102 (34%) had AHI ≥5. Sensitivity and specificity of the SVR algorithm for detecting this was 0.75 and 0.76 respectively; positive predictive value was 0.62 and negative predictive value was 0.85. Conclusion This demonstration of a machine learning algorithm classifying oximetry traces from a real-world cohort of a diverse group of children undergoing evaluation for possible sleep apnoea demonstrates the automated tool may be useful in helping with PSG waitlist prioritisation.

Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected ‘handcrafted’ EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance—Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.

BACKGROUND:Obstructive sleep apnea (OSA) is a common problem in children and can result in developmental and cognitive complications if untreated. The gold-standard tool for diagnosis is polysomnography (PSG); however, it is an expensive and time-consuming test to undertake. Overnight oximetry has been suggested as a faster and cheaper initial test in comparison to PSG as it can be performed at home using limited, reusable equipment.AIM:This retrospective case control study aims to evaluate the effectiveness of a home oximetry service (implemented in response to extended waiting times for routine PSG) in reducing the time between patient referral and treatment.METHODS:Patients undergoing diagnostic sleep evaluation for suspected OSA who utilized the Queensland Children's Hospital screening home oximetry service in the first year since its inception in 2021 (n = 163) were compared to a historical group of patients who underwent PSG in 2018 (n = 311). Parameters compared between the two groups included time from sleep physician review to sleep test, ENT review, and definitive treatment in the form of adenotonsillectomy surgery (or CPAP initiation for those who had already undergone surgery).RESULTS:The time from sleep physician review and request of the sleep-related study to ENT surgical treatment was significantly reduced (187 days for the HITH oximetry group vs 359 days for the comparable PSG group; p-value <0.05), and time from sleep study request to the report of results was significantly lower for patients in the oximetry group compared to those in the PSG group (11 days vs 105 days; p-value <0.05).CONCLUSION:These results suggest that for children referred to a tertiary sleep center for possible obstructive sleep disordered breathing, a home oximetry service can be effective in assisting sleep evaluation and reducing the time to OSA treatment.

Adenotonsillectomy forms part of first-line management for pediatric obstructive sleep apnea (OSA). In nonrandomized studies of preschool-aged children, it is associated with postoperative weight gain. Being overweight or obese in childhood is a predictor of cardiovascular and metabolic disease in later life. Using longitudinal data from a multicenter randomised controlled trial, we assessed the impact of adenotonsillectomy on growth trajectory in preschool-aged children with mild-moderate OSA. Secondary aims were to assess the influence of social factors and baseline polysomnography parameters on growth trajectory. A total of 190 children (aged 3–5 years) with obstructive apnea hypopnea index ≤10 were randomly assigned to early (within 2 months) or routine (12-month wait) adenotonsillectomy. Anthropometry and polysomnography were performed at baseline, 12-month and 24-month timepoints for 126 children. Social risk factors were recorded using a questionnaire. Baseline characteristics were compared using a Mann-Whitney or t-test for continuous variables, and Fisher’s exact test for categorical variables. Data were analyzed using linear mixed modelling. Demographic and polysomnographic parameters were similar between groups at baseline. Baseline body mass index (BMI) z-score was 0.52 for both groups. For BMI z-score, there was a significant increase in the early surgery group between 0 and 12 months (0.4, 95%CI 0.1–0.8) but not from 12–24 months. For the routine surgery group, there was a significant BMI z-score increase following surgery between 12 and 24 months (0.45, 95%CI 0.1–0.8), but not from 0–12 months. Final BMI z-score was similar between the two groups. Findings for weight-for-age z-score were similar to the abovementioned findings for BMI z-score. Height-for-age z-score was not significantly different between different timepoints or intervention groups. Children with an unemployed primary income earner had a higher BMI z-score than those with a full-time employed income earner. No other social risk or polysomnography parameters were statistically significant. This study provides randomized controlled trial evidence of notable weight increase in preschool children with milder spectrum OSA that occurs in the months immediately following adenotonsillectomy. For children undergoing adenotonsillectomy, counselling regarding nutritional intake and exercise alongside weight monitoring should be considered, especially for those already at risk of becoming overweight or obese. Support (if any):

Diagnostic polysomnography (PSG) is the gold standard test to evaluate sleep-disordered breathing in children. Little is known about how children with neurodevelopmental disorders (NDD) tolerate electrodes and sensors in PSG compared to neurotypical children. In this retrospective cohort study of children > 12 months of age who underwent diagnostic PSG at our center from 01/01/2021–30/06/2021, we used sleep technician and physician reports to determine how PSG was tolerated in children with NDD compared to neurotypical children. Subanalyses included tolerance of individual electrodes and sensors and subgroups of NDD (eg, Trisomy 21). A total of 132 children with a NDD and 139 neurotypical children underwent diagnostic PSG. The median age of all children was 8 years, 39