Current advances and gaps in knowledge on personalizing masks for non-invasive respiratory support: a scoping review.

Non-invasive respiratory support delivered through a face mask has become a cornerstone treatment for adults and children with acute or chronic respiratory failure. However, an imperfect mask fit using commercially available interfaces is frequently encountered, which may result in patient discomfort and treatment inefficiency or failure. To overcome this challenge, over the last decade increasing attention has been given to the development of personalized face masks, which are custom made to address the specific facial dimensions of an individual patient. With this scoping review we aim to provide a comprehensive overview of the current advances and gaps in knowledge regarding the personalization of ventilation masks. We performed a systematic search of the literature, and identified and summarized a total of 23 studies. Most studies included were involved in the development of nasal masks. Studies targeting adult respiratory care mainly focused on chronic (home) ventilation and included some clinical testing in a relevant subject population. In contrast, pediatric studies focused mostly on respiratory support in the acute setting, while testing was limited to bench or case studies only. Most studies were positive regarding the performance (i.e. comfort, level of air leak and mask pressure applied to the skin) of personalized masks in bench testing or in human, healthy or patient, subjects. Advances in the field of 3D scanning and soft material printing were identified, but important gaps in knowledge remain. In particular, more insight into cushion materials, headgear design, clinical feasibility and cost-effectiveness is needed, before definite recommendations can be made regarding implementation of large scale clinical programs that personalize non-invasive respiratory support masks for adults and children.

Review of implementation models for children's sleep support services in the UK.

Sleep deprivation has a serious impact on physical and mental health. Children with neurodevelopmental disorders are frequently affected by chronic insomnia, defined as difficulty in either initiating sleep, maintaining sleep continuity or poor sleep quality which can lead to long-term detrimental effects on behaviour, learning and development.Interventions to address chronic insomnia in children include both pharmacological and non-pharmacological approaches. While some children unequivocally benefit from pharmacological treatment, recommendations suggest an intervention based on cognitive-behavioural techniques involving a thorough assessment of the child's sleep pattern, environment and psychosocial factors supporting the child to learn to self-soothe as first-line treatment. Evidence from sleep clinics delivered by trained community practitioners supports the efficacy of an intensive programme, whereby education, practical advice and follow-up support were key factors; however, these services are inconsistently resourced. In practice, sleep support interventions range from verbal advice given in clinics to healthy sleep leaflets to tailored and non-tailored parent-directed interventions. Delivery models include promotion of safe sleep within a wider health promotion context and targeted early intervention within sleep clinics delivered in health and community services or by the third sector but evidence for each model is lacking.We describe a comprehensive whole systems city-wide model of sleep support, ranging from awareness raising, universal settings, targeted support for complex situations to specialist support, delivered according to complexity and breadth of need. By building capacity and quality assurance into the existing workforce, the service has been sustainable and has continued to develop since its initial implementation in 2017. With increasing access to specialist sleep services across the UK, this model could become a widely generalisable approach for delivery of sleep services to children in the UK and lead to improved outcomes in those with severe sleep deprivation.

A systematic review on automatic identification of insomnia.

Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual's sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection. Methods: We followed the recommendations given in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a se- lection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various meth- ods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intel- ligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this de- tection task. Results: Result: The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential com- ponents under investigation in the automated technique include the data input source, objective, machine learning (ML) and deep learning (DL) network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types. Conclusion: Based on our review of the studies featured in this paper, we have identi- fied a notable research gap in the current methods for identifying insomnia and oppor- tunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia. .

The role of rigid laryngo-tracheo-bronchoscopy in children with obstructive sleep apnoea: a case series of 65 children.

OBJECTIVE: To assess the role of laryngo-tracheo-bronchoscopy in children with obstructive sleep apnoea by identifying airway abnormalities at surgery, that occur separately or in addition to adenotonsillar hypertrophy, and examining the correlation with respiratory parameters. METHODS: A retrospective study was conducted of children with obstructive sleep apnoea who underwent laryngo-tracheo-bronchoscopy intra-operatively, performed by a single ENT surgeon from February 2016 to July 2019. Pre- and post-operative minimum oxygen saturation, apnoea-hypopnoea index, and oxygen desaturation index were recorded. RESULTS: Sixty-five children were identified; 34 were aged less than three years and 31 were aged three years or more. 77 per cent and 13 per cent respectively had an airway abnormality; the t-test showed a significantly higher mean oxygen desaturation index and lower mean minimum oxygen saturation pre-operatively compared to children without an airway abnormality. CONCLUSION: An update of the surgical pathway for children aged less than three years with obstructive sleep apnoea is required to include laryngo-tracheo-bronchoscopy intra-operatively. A t-test analysis of the pre-operative respiratory parameters suggests that airway abnormalities contribute to obstructive sleep apnoea severity.

Evaluation of the efficacy of asthma information videos ('Moving On Asthma') at improving knowledge in teenage children.

INTRODUCTION: Asthma is a common inflammatory condition that can be life threatening. The National Review of Asthma Deaths (2014) recommended: Parents and children…should be educated about managing asthma. The aim of this study was to assess the efficacy of an educational video on asthma at improving knowledge in adolescent children. METHODS: A 3-min asthma education video was shown to young people aged 13-15 years in two contrasting schools. Knowledge of asthma was evaluated using a 6-question form completed at 3 timepoints: baseline (pre), immediately after intervention (post), and 1 week later (delayed). A total of 151 data sets from two schools were analysed. RESULTS: Knowledge was significantly improved immediately after watching the video for four out of six questions, indicating that the video was successful in effectively educating the children about asthma. There was no significant change to responses between immediately after watching the video and a week later, suggesting retention of the knowledge gained from viewing the intervention material. CONCLUSION: The results suggest acquisition and retention of knowledge in young people after watching a video on asthma, providing evidence to support the use of digital, video-assisted, internet-based learning tools such as the 'Moving on Asthma' website as an aid to regular clinics for young people with asthma.

Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges.

BACKGROUND AND OBJECTIVES: Sleep quality is associated with wellness, and its assessment can help diagnose several disorders and diseases. Sleep analysis is commonly performed based on self-rating indices, sleep duration, environmental factors, physiologically and polysomnographic-derived parameters, and the occurrence of disorders. However, the correlation that has been observed between the subjective assessment and objective measurements of sleep quality is small. Recently, a few automated systems have been suugested to measure sleep quality to address this challenge. Sleep quality can be assessed by evaluating macrostructure-based sleep analysis via the examination of sleep cycles, namely Rapid Eye Movement (REM) and Non Rapid Eye Movement (NREM) with N1, N2, and N3 stages. However, macrostructure sleep analysis does not consider transitory phenomena like K-complexes and transient fluctuations, which are indispensable in diagnosing various sleep disorders. The CAP, part of the microstructure of sleep, may offer a more precise and relevant examination of sleep and can be considered one of the candidates to measure sleep quality and identify sleep disorders such as insomnia and apnea. CAP is characterized by very subtle changes in the brain's electroencephalogram (EEG) signals that occur during the NREM stage of sleep. The variations among these patterns in healthy subjects and subjects with sleep disorders can be used to identify sleep disorders. Studying CAP is highly arduous for human experts; thus, developing automated systems for assessing CAP is gaining momentum. Developing new techniques for automated CAP detection installed in clinical setups is essential. This paper aims to analyze the algorithms and methods presented in the literature for the automatic assessment of CAP and the development of CAP-based sleep markers that may enhance sleep quality assessment, helping diagnose sleep disorders. METHODS: This literature survey examined the automated assessment of CAP and related parameters. We have reviewed 34 research articles, including fourteen ML, nine DL, and ten based on some other techniques. RESULTS: The review includes various algorithms, databases, features, classifiers, and classification performances and their comparisons, advantages, and limitations of automated systems for CAP assessment. CONCLUSION: A detailed description of state-of-the-art research findings on automated CAP assessment and associated challenges has been presented. Also, the research gaps have been identified based on our review. Further, future research directions are suggested for sleep quality assessment using CAP.

Changes in UK paediatric long-term ventilation practice over 10 years.

OBJECTIVES: To provide up-to-date information on the use of long-term ventilation (LTV) in the UK paediatric population and to compare the results with data collected 10 and 20 years previously. DESIGN: A single timepoint census completed by LTV centres in the UK, carried out via an online survey. SETTING AND PATIENTS: All patients attending paediatric LTV services in the UK. RESULTS: Data were collected from 25 LTV centres in the UK. The total study population was 2383 children and young people, representing a 2.5-fold increase in the last 10 years. The median age was 9 years (range 0-20 years). Notable changes since 2008 were an increase in the proportion of children with central hypoventilation syndrome using mask ventilation, an increase in overall numbers of children with spinal muscular atrophy (SMA) type 1, chronic lung disease of prematurity and cerebral palsy being ventilated, and a 4.2-fold increase in children using LTV for airway obstruction. The use of 24-hour ventilation, negative pressure ventilation and tracheostomy as an interface had declined. 115 children had received a disease-modifying drug. The use of ataluren and Myozyme did not influence the decision to treat with LTV, but in 35% of the children with SMA type 1 treated with nusinersin, the clinician stated that the use of this drug had or may have influenced their decision to initiate LTV. CONCLUSION: The results support the need for national database for children and young people using LTV at home to inform future recommendations and assist in resource allocation planning.

A review of automated sleep disorder detection.

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand.

Meaningful involvement of children and young people in health technology development.

The anatomical, physiological, and developmental changes which arise as children mature through childhood and adolescence support the need to develop new health technologies that meet the specific requirements of children and young people (CYP). Failing to involve CYP during the development of technology increases the risk that the outcome falls short of their expectations and needs, leading to rejection of novel interventions. Through participation in health technology development, CYP and their families can provide context, insight, personal experience and tacit knowledge to ensure that the end-product is usable, acceptable, and can be integrated into its intended environment. A nuanced, balanced understanding of the methods that can be used to facilitate participation will support researchers in choosing an effective approach to involving CYP in health technology development. Methodological approaches include patient and public involvement and engagement, co-design, and experienced based co-design. These methods can be used in isolation or in combination, to facilitate meaningful involvement of CYP and encourage the development of impactful solutions, in consideration of the context, stakeholders, and objectives of the project. We provide the rationale and justification for involving CYP in health technology design and development, an explanation of the methods supporting meaningful involvement, and case studies exemplifying real world application of these methods with positive outputs.

The (extended) achondroplasia foramen magnum score has good observer reliability.

BACKGROUND: Achondroplasia is the most common skeletal dysplasia. A significant complication is foramen magnum stenosis. When severe, compression of the spinal cord may result in sleep apnea, sudden respiratory arrest and death. To avoid complications, surgical decompression of the craniocervical junction is offered in at-risk cases. However, practice varies among centres. To standardize magnetic resonance (MR) reporting, the achondroplasia foramen magnum score was recently developed. The reliability of the score has not been assessed. OBJECTIVE: To assess the interobserver reliability of the achondroplasia foramen magnum score. MATERIALS AND METHODS: Base of skull imaging of children with achondroplasia under the care of Sheffield Children's Hospital was retrospectively and independently reviewed by four observers using the achondroplasia foramen magnum score. Two-way random-effects intraclass coefficient (ICC) was used to assess inter- and intra-observer reliability. RESULTS: Forty-nine eligible cases and five controls were included. Of these, 10 were scored normal, 17 had a median score of 1 (mild narrowing), 11 had a median score of 2 (effacement of cerebral spinal fluid), 10 had a score of 3 (compression of cord) and 6 had a median score of 4 (cord myelopathic change). Interobserver ICC was 0.72 (95% confidence interval = 0.62-0.81). Intra-observer ICC ranged from 0.60 to 0.86. Reasons for reader disagreement included flow void artefact, subtle T2 cord signal and myelopathic T2 cord change disproportionate to canal narrowing. CONCLUSION: The achondroplasia foramen magnum score has good interobserver reliability. Imaging features leading to interobserver disagreement have been identified. Further research is required to prospectively validate the score against clinical outcomes.

Paediatric narcolepsy: a review of diagnosis and management.

Narcolepsy is a chronic disabling neurological sleep disorder that requires lifelong treatment. We have outlined the clinical features of narcolepsy, the assessment and diagnosis process and have summarised the existing treatment options for children and adolescents with narcolepsy. In the future, the approach to management of paediatric narcolepsy should ideally be in a multidisciplinary setting, involving specialists in sleep medicine, sleep physiology, neurologists and psychologists/psychiatrists. A multidisciplinary approach will help to manage the potential impact of narcolepsy on children and adolescents who are in a stage of their life that is critical to their physical, emotional and social development and their academic attainment.

International Consensus Statement on the diagnosis, multidisciplinary management and lifelong care of individuals with achondroplasia.

Achondroplasia, the most common skeletal dysplasia, is characterized by a variety of medical, functional and psychosocial challenges across the lifespan. The condition is caused by a common, recurring, gain-of-function mutation in FGFR3, the gene that encodes fibroblast growth factor receptor 3. This mutation leads to impaired endochondral ossification of the human skeleton. The clinical and radiographic hallmarks of achondroplasia make accurate diagnosis possible in most patients. However, marked variability exists in the clinical care pathways and protocols practised by clinicians who manage children and adults with this condition. A group of 55 international experts from 16 countries and 5 continents have developed consensus statements and recommendations that aim to capture the key challenges and optimal management of achondroplasia across each major life stage and sub-specialty area, using a modified Delphi process. The primary purpose of this first International Consensus Statement is to facilitate the improvement and standardization of care for children and adults with achondroplasia worldwide in order to optimize their clinical outcomes and quality of life.

Sleep-Related Rhythmic Movement Disorder in Young Children with Down Syndrome: Prevalence and Clinical Features.

Sleep-related Rhythmic Movement Disorder (RMD) affects around 1% of UK pre-school children. Little is known about RMD in Down syndrome (DS). We aimed to determine: (a) the prevalence of RMD in children with DS aged 1.5-8 years; (b) phenotypic and sleep quality differences between children with DS and RMD and sex- and age-matched DS controls; and (c) night-to-night variability in rhythmic movements (RMs). Parents who previously reported RMs from a DS research registry of 202 children were contacted. If clinical history suggested RMD, home videosomnography (3 nights) was used to confirm RMs and actigraphy (5 nights) was used to assess sleep quality. Phenotype was explored by demographic, strengths and difficulties, Q-CHAT-10/social communication and life events questionnaires. Eight children had confirmed RMD. Minimal and estimated maximal prevalence were 4.10% and 15.38%, respectively. Sleep efficiency was significantly lower in RMD-cases (69.1%) versus controls (85.2%), but there were no other phenotypic differences. There was considerable intra-individual night-to-night variability in RMs. In conclusion, RMD has a high prevalence in children with DS, varies from night to night and is associated with poor sleep quality but, in this small sample, no daytime phenotypic differences were found compared to controls. Children with DS should be screened for RMD, which is amenable to treatment.

Custom-made 3D printed masks for children using non-invasive ventilation: a comparison of 3D scanning technologies and specifications for future clinical service use, guided by patient and professional experience.

Non-invasive ventilation (NIV) is assisted mechanical ventilation delivered via a facemask for people with chronic conditions that affect breathing. Mass-produced masks are available for both the adult and paediatric markets but masks that fit well are difficult to find for children who are small or have asymmetrical facial features. A good fit between the mask and the patient's face to minimise unintentional air leakage is essential to deliver the treatment effectively. We present an innovative use of 3D assessment and manufacturing technologies to deliver novel custom-made facemasks for children for whom a well-fitting standard mask is not available. This paper aims to describe the processes undertaken to investigate and compare currently available technologies for 3D scanning children and to explore the design of a system for creating custom-made paediatric NIV masks within the NHS. The paper therefore considers not only the quality and accuracy of the data, but also other factors such as the time and ease of process. Searches for all currently available scanning technologies were made. Photogrammetry image stitch using a smartphone and a digital camera, and two structured light scanners were selected and compared in the laboratory, in discussion with user groups, and in adult volunteers. Using the processes described, it became apparent that the optimal 3D scanning system for this purpose was the handheld structured light scanner. This option offered both superior accuracy and convenience and was more cost effective.

Pseudomonas aeruginosa infection in respiratory samples in children with neurodisability-to treat or not to treat?

The objective was to investigate the prevalence of Pseudomonas aeruginosa (PA) in patients with complex neurodisability and current treatment practice in our centre in order to inform future guidelines. A retrospective case note review was undertaken at a tertiary children's hospital. One hundred sixty-two patients (mean age 11.7 years) with a primary diagnosis of neuromuscular disease (NMD) or severe cerebral palsy (CP) and a respiratory sample sent for analysis during the study period were studied. Associations between PA in respiratory samples and diagnosis, long-term ventilation, presence of a gastrostomy or a tracheostomy, antibiotic choice, clinical deterioration and adverse events were analysed. Twenty-five (15%) had one or more PA isolate in respiratory samples. There was a significant association between PA in respiratory samples and tracheostomy (p<0.05). In 52% samples, multiple pathogens co-existed. There was no significant association between choice of antibiotic and clinical outcome but when antibiotics were changed to specific PA antibiotics during the course of the illness, all resulted in clinical improvement. Twenty-six episodes involving 8 patients with recurrent admissions involved PA organisms that were resistant to one or more antibiotics.Conclusions: A larger prospective study may establish clearer criteria for guideline development. Techniques such as point-of-care testing to identify virulent strains of PA may improve patient outcomes and prevent the development of antibiotic resistance in the future. What is Known: •Children with complex neurodisability are at increased risk of respiratory morbidity and of infection with gram-negative organisms such as Pseudomonas aeruginosa. •There are currently no guidelines to inform treatment choices in this group of vulnerable children. What is New: •15% children in this study population had Pseudomonas aeruginosa in respiratory samples during a 12-month period, the majority of whom did not require critical care treatment. Thirteen of these children had a tracheostomy in situ and 12 did not.  •In those that deteriorated clinically or developed antibiotic resistant organisms, earlier detection and targeted treatment of Pseudomonas aeruginosa may have prevented deterioration.

Aerosol Therapy in Asthma-Why We Are Failing Our Patients and How We Can Do Better.

In order for inhaled corticosteroids to be delivered adequately to the airways they require patients to take them regularly using an effective technique. Patients often have a poor inhaler technique, and this has been shown to result in sub-optimal asthma control. It is important for all clinicians prescribing inhaled medication to be experienced in the correct technique, and take time to train children so that they have mastered corrected inhaler technique. Using Teach to Goal or teach back methodology is a simple and effective way to provide this in the clinic setting. More than one training session is typically needed before children can master correct inhaler technique. Adherence to inhaled therapy has been shown to be sub-optimal in pediatric populations, with studies showing an average rate of around 50%. Subjective methods of measuring adherence have been shown to be inaccurate and overestimate rates. The advent of new technology has allowed adherence rates to be measured electronically, and it has been shown that regular feedback of these data can be effective at improving asthma control. New mobile apps and smart technology aim to engage patients and families with their asthma care. Effective use of these apps in collaboration with health care professionals has a vast potential to improve adherence rates and inhaler technique, resulting in improved asthma control.

Custom-made 3D printed masks for children using non-invasive ventilation: a feasibility study of production method and testing of outcomes in adult volunteers.

Non-invasive ventilation (NIV) is assisted mechanical ventilation delivered via a facemask for people with chronic conditions that affect breathing. NIV is most commonly delivered via an interface (mask) covering the nose (nasal mask) or the nose and mouth (oronasal mask). The number of children in the UK requiring NIV is currently estimated to be around 5000. Mass-produced masks are available for both the adult and paediatric markets but masks that fit well are difficult to find for children who are small or have asymmetrical facial features. A good conforming fit between the mask and the patient's face to minimise unintentional air leakage is essential to deliver the treatment effectively; most ventilators will trigger an alarm requiring action if such leakage is detected. We present an innovative use of 3D scanning and manufacturing technologies to deliver novel mask-face interfaces to optimise mask fit to the needs of individual patients. Ahead of planned user trials with paediatric patients, the project team trialled the feasibility of the process of creating and printing bespoke masks from 3D scan data and carried out testing of the masks in adult volunteers to select the strongest design concept for the paediatric trial. The evaluation of the process of designing a bespoke mask from scan data, arranging for its manufacture and carrying out user testing has been invaluable in gaining knowledge and discovering the pitfalls and timing bottlenecks in the processes. This allowed the team to iteratively refine the techniques and methods involved, informing user trials later on in the project. It has also provided indicative cost estimates for 3D printed mask prototype components which are useful in project decision making and trial planning. The value of the process extends to considerations for future implementation of the process within a clinical pathway.

Psychometric Properties and Predictive Value of a Screening Questionnaire for Obstructive Sleep Apnea in Young Children With Down Syndrome.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is common in children with Down syndrome (DS) and is associated with adverse health and cognitive outcomes. Daytime clinical assessment is poorly predictive of OSA, so regular screening with sleep studies is recommended. However, sleep studies are costly and not available to all children worldwide. We aimed to evaluate the psychometric properties and predictive value of a newly developed screening questionnaire for OSA in this population. METHODS: 202 children aged 6 months to 6th birthday with DS were recruited, of whom 188 completed cardio-respiratory sleep studies to generate an obstructive apnea hypopnea index (OAHI). Parents completed the 14-item Down syndrome OSA screening questionnaire. Responses were screened, a factor analysis undertaken, internal consistency calculated and receiver operator characteristic (ROC) curves drawn to generate an area under the curve (AUC) to assess criterion related validity. RESULTS: Of 188 children who completed cardiorespiratory sleep studies; parents completed the screening questionnaire for 186. Of this study population 15.4% had moderate to severe OSA defined by an OAHI of ≥5/h. Sixty-three (33.9%) participants were excluded due to "unsure" responses or where questions were not answered. Using the remaining 123 questionnaires a four-factor solution was found, with the 1st factor representing breathing related symptoms, explaining a high proportion of the variance. Internal consistency was acceptable with a Cronbach alpha of 0.87. ROC curves for the total score generated an AUC statistic of 0.497 and for the breathing subscale an AUC of 0.603 for moderate to severe OSA. CONCLUSION: A well designed questionnaire with good psychometric properties had limited predictive value to screen for moderate to severe OSA in young children with DS. The use of a screening questionnaire is not recommended. Screening for OSA in this population requires objective sleep study measures.

Respiratory rate is an early predictor of clinical deterioration in children.

OBJECTIVE: To assess the value of respiratory rate (RR) as a predictor of clinical deterioration in children, compared with other vital sign measurements. DESIGN: A retrospective case-control study, comparing children who deteriorated, requiring admission to critical care with children who did not deteriorate. METHODS: RR, heart rate (HR), and blood pressure (BP) measurements were collected from each patient for a 48-hour duration. The 95th centile was identified for each and 5% to 30% thresholds above the 95th centile were calculated. For each threshold the sensitivity, specificity, odds ratio, positive, and negative predictive value for deterioration was calculated. RESULTS: Forty cases (age range 7 weeks-15 years) and 40 control patients matched for age, gender, and hospital location were recruited. In 30/40 patients who deteriorated at least one RR ≥ 30% above the 95th centile for their age was recorded in the 48 hours before deterioration, compared with 10/40 controls, regardless of clinical diagnosis. Only 3/40 children that deteriorated had a HR > 30% greater than the 95th centile, compared with 2/40 controls. An elevated RR was the only vital sign whose odds ratios were significant at each threshold level above the 95th centile. Maximum RR occurred 16.8 hours before deterioration. CONCLUSION: RR is a more accurate predictor of clinical deterioration in children than other vital signs. Greater weighting and importance should be placed on RR, which is often omitted in children due to difficulties with its measurement.