Sleep Events Research Articles

O041 The impact of including oxygen desaturations occurring during awake epochs on the oxygen desaturation index

Abstract Introduction The oxygen desaturation index (ODI) is an important measure of sleep disordered breathing during polysomnography (PSG) however there is no accepted standard for its calculation. The AASM Manual for the Scoring of Sleep and Associated events (V2.6) does not specify whether oxygen desaturations occurring during awake epochs should be included. More generally, epoch-based scoring is potentially problematic for accurate ODI calculation. This study aims to compare the calculation of ODI including and excluding oxygen desaturations occurring during awake epochs and to determine the impact of sleep efficiency (SE) on any discrepancy. Methods Using twenty-one consecutive unattended PSG’s for investigation of OSA, two oxygen desaturation indices were calculated from each PSG; one excluding (ODIsleep) and one including (ODIall) oxygen desaturations marked in awake epochs. Results The median (IQR) ODIall was 19.3/h (10.3, 27.0) and ODIsleep was 13.0/h (6.6, 16.7). The median (IQR) difference (ODIall - ODIsleep) was 5.2/h (2.7, 10.4). This difference was greater with decreasing SE (r = -.63, p = .002). Patients with SE ≤ 75% (n=10) had a median ODI difference of 11.5/h (4.0, 17.6), and those with SE > 75% (n=11) had a difference of 2.8/h (2.0, 5.5) (p = .02). Discussion ODI was greater when including oxygen desaturations during awake epochs, with this discrepancy being greatest when SE is ≤ 75%. We plan to confirm these findings in a larger sample. This investigation informs clinical practice, highlights the difficulties of epoch scoring, and informs future standards for the scoring of sleep and associated events.

P144 Sleep-Related Limb Movements and Subjective Sleepiness in a Professional Rugby League Team

Abstract Introduction This study aims to assess the relationship between LMs, PLMD and limb related arousals during sleep and subjective sleepiness in a group of professional athletes (National Rugby League (NRL) players). Methods 25 type II HSAT polysomnographic (PSG) studies were performed on 23 male individuals (2 repeated studies). 2 x Piezo limb EMG sensors were applied to each HSAT. PSG data analysed as per AASM Manual for the Scoring of Sleep and Associated events. Epworth Sleepiness Scale (ESS) collected for each participant obtaining subjective daytime sleepiness. PSG data was checked for normality using the Shapiro-Wilk normality test. Parametric data was then subsequently analysed using Pearson correlation coefficients, whereas non-parametric data was analysed with the Spearman correlation coefficient. A line of best fit was implemented using Deming’s linear regression model to report r2. Results The most significant relationships were noted between daytime sleepiness and the frequency of limb-related arousals (Pearson’s r 0.273) and the relationship between PLM arousals and ESS (p-0.082). No significant relationships were noted between LMs, PLMs, limb related arousals and daytime sleepiness were found. No results were found to be statistically significant. Conclusions This PSG data demonstrates a mildly positive correlation with all limb movement parameters measured against the athletes’ self-reported sleepiness. This may therefore be of significance with their performance and recovery. Further research is recommended to verify these relationships.

P110 The association between sleep spindles and cognitive function in middle-aged and older men: A community-based study

Abstract Introduction The association between sleep spindles and cognitive function and the potential confounding influence of obstructive sleep apnea (OSA) remains uncertain. This study examined cross-sectional associations between sleep spindle metrics and cognitive function outcomes in community-dwelling men. Methods Men, Androgen, Inflammation, Lifestyle, Environment, and Stress (MAILES) study participants (n=477) underwent home-based polysomnography between 2010–2011 and completed the inspection time task, trail-making test A (TMT-A) and B (TMT-B), and Fuld object memory evaluation. Frontal spindle metrics derived from sleep electroencephalography included occurrence (total no. of sleep spindle events) and slow (11–13 Hz) and fast (13–16 Hz) spindle density (no./min) during N2 and N3 sleep. Results Men with OSA (any OSA and severe OSA) had significantly impaired sleep spindles (reduced occurrence and densities). In the complete study sample, higher spindle occurrence during N2 sleep was independently associated with faster inspection time (B= -0.44, 95% CI [-0.87, -0.02], p=0.041), whereas higher fast spindle density during N3 sleep was independently associated with worse TMT-B performance (B=20.7, 95% CI [0.55, 40.9], p=0.044). Furthermore, in men with severe OSA (apnea-hypopnea index ≥30/h), higher slow spindle density during N2 sleep was independently associated with worse TMT-A and TMT-B performance, whereas only higher spindle occurrence during N2 sleep was independently associated with worse TMT-A performance (all p<0.05). Discussion Specific spindle metrics during N2 and N3 sleep are independently associated with cognitive function in an unselected population of men and men with undiagnosed severe OSA. The utility of sleep spindles for predicting cognitive dysfunction and decline requires further investigation.

Characterization of sleep-disordered breathing in children with Duchenne muscular dystrophy by the American Academy of Sleep Medicine criteria vs disease-specific criteria: what are the differences?

Individuals with Duchenne muscular dystrophy (DMD) frequently develop sleep-disordered breathing. Noninvasive ventilation is often prescribed for sleep-disordered breathing treatment based on the American Academy of Sleep Medicine (AASM) criteria. In 2018, DMD disease-specific criteria for sleep-disordered breathing were established. Our study aimed to examine the clinical interpretation differences using these different criteria. We performed a multicenter, retrospective chart review of children with DMD followed at The Hospital for Sick Children, Toronto, Canada, and Rady Children's Hospital, San Diego, California, who underwent polysomnography from August 1, 2012, to February 29, 2020. Baseline characteristics and polysomnography data were summarized using descriptive statistics. Agreement for the diagnosis of sleep-disordered breathing evaluated by kappa statistics and sensitivity/specificity analysis was assessed. One hundred five male children with DMD (mean ± SD age: 12.1 ± 3.8 years; body mass index z score: 0.2 ± 2.3) were included. The proportions of children with DMD that met at least 1 AASM criterion and at least 1 DMD criterion were 45.7% and 67.6%, respectively. We found that 32.4% of children met neither AASM nor DMD criteria. Overall agreement between AASM and DMD criteria was moderate (k = 0.57). There was almost perfect agreement in sleep apnea diagnosis (k = 0.90); however, there was only slight agreement in hypoventilation diagnosis (k = 0.12) between AASM and DMD criteria. There were more children with DMD diagnosed with nocturnal hypoventilation and prescribed noninvasive ventilation using DMD criteria compared with AASM criteria. Future studies should address whether the prescription of noninvasive ventilation for children with DMD based on both criteria is associated with different clinical outcomes. Hurvitz MS, Sunkonkit K, Massicotte C, Li R, Bhattacharjee R, Amin R. Characterization of sleep-disordered breathing in children with Duchenne muscular dystrophy by the American Academy of Sleep Medicine criteria vs disease-specific criteria: what are the differences? J Clin Sleep Med. 2022;18(2):609-615.

Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions.

The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because interareal communication has been reported both during specific sleep stages and in awake, behaving animals, these brain regions can serve as an archetype to establish that measuring functional interactions is important for comprehending neural systems. To this end, we analyze here a public dataset of local field potentials (LFPs) recorded in rats simultaneously from the hippocampus and amygdala during different behaviors. Employing a specific, time-lagged embedding technique, named topological causality (TC), we infer directed interactions between the LFP band powers of the two regions across six frequency bands in a time-resolved manner. The combined power and interaction signals are processed with our own unsupervised tools developed originally for the analysis of molecular dynamics simulations to effectively visualize and identify putative, neural states that are visited by the animals repeatedly. Our proposed methodology minimizes impositions onto the data, such as isolating specific epochs, or averaging across externally annotated behavioral stages, and succeeds in separating internal states by external labels such as sleep or stimulus events. We show that this works better for two of the three rats we analyzed, and highlight the need to acknowledge individuality in analyses of this type. Importantly, we demonstrate that the quantification of functional interactions is a significant factor in discriminating these external labels, and we suggest our methodology as a general tool for large, multisite recordings.

Selection of stimulus parameters for enhancing slow wave sleep events with a neural-field theory thalamocortical model.

Slow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates with memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events' co-occurrence. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0°, 45°, and 90° of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0° stimulation produces better results in the power and number of SO and SP than the rhythmic or random stimulation. On the other hand, stimulating at 45° or 90° change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0° phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.

Comparison of home ambulatory type 2 polysomnography with a portable monitoring device and in-laboratory type 1 polysomnography for the diagnosis of obstructive sleep apnea in children.

To compare type 2 polysomnography (T2PSG) to the gold standard type 1 in-laboratory polysomnography (T1PSG) for diagnosing obstructive sleep apnea (OSA) in children; validate home T2PSG in children with suspected OSA. Eighty-one participants (ages 6-18) with suspected OSA had simultaneous T1PSG and T2PSG in the sleep laboratory, 47 participants (ages 5-16) had T1PSG in the sleep laboratory and T2PSG performed at home. Sleep scientists staged and scored polysomnography data, and pediatric sleep physicians assigned a diagnosis of normal or OSA. Participant demographics, polysomnography variables, and diagnoses were compared using chi-square and Fisher's exact tests for nominal variables, t test for continuous variables and Cohen's kappa to assess concordance. Acceptable recordings were obtained for every home T2PSG. When T1PSG and T2PSG were simultaneous, correlation between the number of arousals, respiratory disturbance index, and sleep stages was excellent. T2PSG at home demonstrated less stage 2 sleep, more rapid eye movement sleep, and higher sleep efficiency. Comparison of home T2PSG to T1PSG for diagnosing OSA showed a false-positive rate of 6.6% and false-negative rate of 3% for those performed at home. T2PSG in the home is feasible with excellent concordance with T1PSG for the purposes of diagnosing OSA in children aged 5-18 years. Home T2PSG may be more representative of a "normal" night for children and could benefit those suspected of having OSA by reducing waiting times for laboratory PSG, improving access to PSG and possibly reducing costs of investigating and treating OSA. Withers A, Maul J, Rosenheim E, O'Donnell A, Wilson A, Stick S. Comparison of home ambulatory type 2 polysomnography with a portable monitoring device and in-laboratory type 1 polysomnography for the diagnosis of obstructive sleep apnea in children. J Clin Sleep Med. 2022;18(2):393-402.

Sleep increases leaks and asynchronies during home noninvasive ventilation: a polysomnographic study.

In patients treated with noninvasive ventilation, sleep-related breathing changes can modify patient-ventilator interactions, which could reduce its effectiveness. The aim of this prospective observational study was to determine the impact of sleep/wake state on leaks, upper airway obstructive events, and asynchronies in patients treated by long-term noninvasive ventilation. Stable patients adapted to noninvasive ventilation were considered for nocturnal polysomnography. Unintentional leaks, upper airway obstructive events, and asynchronies were compared between sleep and awake periods. Twenty-eight patients were enrolled. Underlying diagnoses were neuromuscular disease (n = 11), chest wall disease (n = 8), and obesity-hypoventilation (n = 9). Leaks were more frequent in sleep than in awake periods, with a median of 10% (interquartile range [IQR], 0%-75%) vs 1% (IQR, 0%-9%) of time (P < .001), respectively. During sleep, asynchronies with and without associated leak affected 27% of breaths (IQR, 16%-39%) compared with non-leak-related asynchronies that were recorded in 8% (IQR, 3%-25%) of breaths (P < .001). Asynchronies affecting more than 10% of total breaths were more frequent in sleep (25 patients, 89%) than in awake time (8 patients, 29%; P = .25). Eleven patients (39%) presented with 5 or more upper airway obstructive events without reduction in ventilatory drive per hour of sleep. In patients adapted to home noninvasive ventilation, leaks, asynchronies, and upper airway obstructive events are frequent during the night and are concentrated in sleep periods. Asynchronies are often associated with leaks. These findings may have clinical implications considering that in patients with low sleep efficiency respiratory events could be underestimated if sleep is not evaluated. Martí S, Ferré A, Sampol G, etal. Sleep increases leaks and asynchronies during home noninvasive ventilation: a polysomnographic study. J Clin Sleep Med. 2022;18(1):225-233.

Evaluating consumer and clinical sleep technologies: an American Academy of Sleep Medicine update

Evaluating consumer and clinical sleep technologies: an American Academy of Sleep Medicine update

Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics.

Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance.

Sleep medicine catalogue of knowledge and skills - Revision.

The 'catalogue of knowledge and skills' for sleep medicine presents the blueprint for a curriculum, a textbook, and an examination on sleep medicine. The first catalogue of knowledge and skills was presented by the European Sleep Research Society in 2014. It was developed following a formal Delphi procedure. A revised version was needed in order to incorporate changes that have occurred in the meantime in the International Classification of Sleep Disorders, updates in the manual for scoring sleep and associated events, and, most important, new knowledge in sleep physiology and pathophysiology. In addition, another major change can be observed in sleep medicine: a paradigm shift in sleep medicine has taken place. Sleep medicine is no longer a small interdisciplinary field in medicine. Sleep medicine has increased in terms of recognition and importance in medical care. Consequently, major medical fields (e.g. pneumology, cardiology, neurology, psychiatry, otorhinolaryngology, paediatrics) recognise that sleep disorders become a necessity for education and for diagnostic assessment in their discipline. This paradigm change is considered in the catalogue of knowledge and skills revision by the addition of new chapters.

394 A convolutional neural networks model for the detection of cortical arousals from heart rate

Abstract Introduction Cortical arousals are transient events of disturbed sleep that occur frequently in sleep disordered breathing (SDB) and can be used as an indicator of sleep quality. While cortical arousals are typically scored from the electroencephalogram (EEG), arousals are associated with increased sympathetic activity and could therefore be detected from measures of sympathetic activity such as heart rate. Most home sleep test and consumer wearable devices enable continuous recording of heart rate via the electrocardiogram (ECG) or optical heart rate sensors without the inconvenience of EEG electrodes. In this preliminary study, we developed a deep learning-based convolutional neural networks (CNN) model to detect arousals from heart rate. Methods This study included 1,083 polysomnograms (PSGs) from five independent studies (Tucson Children’s Assessment of Sleep Apnea, Mechanisms of Pharyngeal Collapse in Sleep Apnea, Impact of the Arousal Threshold in Obstructive Sleep Apnea, Predicting Successful Sleep Apnea Treatment with Acetazolamide in Heart Failure Patients, Combination Therapy for the Treatment of Obstructive Sleep Apnea) that were scored for arousals according to American Academy of Sleep Medicine scoring rules. These studies included PSGs from both children and adults (ages 6 and above), with most data coming from participants with evidence or diagnosis of SDB. We used the Pan-Tomkins algorithm to detect R-peaks from the raw ECG signal, transformed the peaks into normalized instantaneous heart rate at 1 Hz frequency, and produced arousal probability in 1-second resolution using a simple CNN model. Due to slight asynchrony between the appearance of arousals in the EEG versus the heart rate, all overlaps between model-predicted arousals and manually scored arousals were considered true-positives. Results We evaluated the model on a validation set (n=216). The model achieved a gross area under precision-recall curve score of 0.67 and a gross area under receiver operating characteristics curve of 0.91 Correlation between the number of model-detected and manually scored arousal events was r=0.76. Conclusion This preliminary study demonstrates that a deep learning approach has the potential to accurately detect arousals in home sleep tests and consumer wearable devices that measure heart rate. Support (if any) The study was supported by grant #207-SR-19 from the American Academy of Sleep Medicine Foundation.

701 Daily Associations Between Adolescent Sleep and Mental Health During the COVID-19 Pandemic

IntroductionSleep disturbances after a trauma forecast mental health difficulties such as post-traumatic stress symptoms (PTSS). COVID-19 has resulted in numerous stressors for youth (e.g., school closures, social isolation) that directly disrupt sleep and may have negative mental health consequences. However, investigations of sleep and adverse events in youth have primarily been limited to months or years after a trauma has occurred. How adolescent sleep patterns and mental health intersect during an ongoing adverse experience is unknown.MethodsAdolescents from the United States (N = 503; 80.9% female; 13–18 years) participated in an online daily diary study within 3 weeks after COVID-19 was declared a national emergency. Participants completed one week of twice daily reports (5406 observations) on their nightly sleep (sleep timing, sleep quality, sleep onset difficulties, and nightmares) and daytime mental health symptoms (COVID-19 PTSS, positive affect, negative affect, and loneliness).ResultsMixed models adjusting for age, sex, and socioeconomic status indicated that cyclical, bidirectional effects emerged, with daytime mental health symptoms predicting same-night sleep disturbances, and sleep disturbances predicting next-day mental health. Greater PTSS predicted sleep onset difficulties (Estimate = .02, SE = .004, t = 5.56 p < .001) and sleep onset difficulties predicted next-day PTSS (Estimate = .35, SE = .17, t = 2.04, p < .05). Greater daytime negative affect predicted greater nightmares (Estimate = .07, SE = .01, t = 4.97, p < .001), and nightmares predicted marginally more next-day negative affect (Estimate = .07, SE = .04, t = 1.66, p = .09). PTSS (Estimate = .01, SE = .003, t = 3.47, p < .001) and negative affect (Estimate = .05, SE = .03, t = 2.06, p < .05) both predicted poorer sleep quality the following night.ConclusionFindings suggest that mental health symptoms during the onset of the COVID-19 pandemic are associated with increased sleep disturbances. In turn, these sleep disturbances predict increased mental health symptoms. Overall, results provide a greater understanding of youth sleep patterns during COVID-19 and how sleep disturbances may inhibit resilience in the context of an ongoing stressor.Support (if any)Center for American Indian and Rural Health Equity

423 Evaluation of Electronic Medical Record Artificial Intelligence Screening Tools for Undiagnosed OSA

Abstract Introduction The STOP-Bang is a concise, simple and widely adopted obstructive sleep apnea (OSA) screening tool. However, it has limited predictive ability and is susceptible to subjective reporting bias. Artificial Intelligence (AI) methodologies can be utilized together with existing data in electronic medical records (EMRs) to create new screening tools to increase diagnostic sensitivity and facilitate discovery of preclinical OSA phenotypes. Methods The study comprised two independent retrospective sleep study datasets: 1) Type III HSATS (N=5583) and, 2) Type I polysomnograms (N=1037). Each contained raw sleep study waveforms, manually scored sleep events (respiratory, arousal, sleep staging), and standard report indices (apnea-hypopnea index; AHI, arousal index). Additionally, the first dataset contained 90 EMR based metadata variables and the second dataset contained 54 EMR based metadata variables. Three random forest models were trained to detect OSA diagnostic thresholds (AHI&amp;gt; 5, AHI&amp;gt;15, and AHI&amp;gt;30) over three different screening models: STOP-Bang, P-Bang (blood-pressure, BMI, age, neck-size, gender), and Common Clinical Data Set (CCDS)-OSA (all metadata variables simulating EMR CCDS standard). Results CCDS-OSA ROC-AUC exceeded STOP-Bang and P-Bang for both sleep study collections, resulting in AHI&amp;gt;15 ROC-AUC values of 0.73 and 0.71 (CCDS-OSA) compared to AHI&amp;gt;15 ROC-AUC values of 0.68 and 0.69 (STOP-Bang). Additionally, we analyzed the Gini feature importance ranking of the trained CCDS-OSA model to evaluate which variables showed highest predictive value of OSA. The ranking revealed the top 5 features were the five physiologic based STOP-Bang parameters, followed by EMR based physiologic measurements such as HDL, triglycerides, systolic BP, and disease conditions such as diabetes, hypertension, and depression. Conclusion This study shows that while STOP-Bang contains data critical to OSA screening, a variety of other EMR-based parameters can improve performance of OSA detection. AI-based EMR screening can provide a critical tool for more systematic and accurate screening of undiagnosed sleep apnea. Nationwide standards facilitating patient EMR data interoperable health information exchange, particularly the United States Core Data for Interoperability (USCDI CCDS), holds promise to foster broad clinical and research opportunities. Resulting data sharing will allow application of AI screening tools at the population health scale with ubiquitous, existing EMR data to improve population sleep health. Support (if any):

407 Explanatory analysis of polysomnography for the identification of sleep apnea hypopnea events using deep learning neural network

Abstract Introduction Using deep learning algorithms, we investigated univariate and multivariate effects of four polysomnography features including heart rate (HR), electrocardiogram (ECG), oxygen saturation (SpO2) and nasal air flow (NAF) on the identification of sleep apnea and hypopnea events. This explanatory analysis that may clarify the sensitivity and specificity of those features to SAs and SHs have not been probed. Methods We studied 804 polysomonography samples from 704 patients with obstructive sleep apnea and 100 controls. The input data were converted into scalograms as 4-channel 2D images to train Xception networks. For training, 77,638 patches were sampled from the original 6-hour sleep data with 30-second time width. A 10% of these patches were segregated as the test-set. With each feature sets, we tested the following classifications: 1) normal vs apnea vs hypopnea; 2) normal vs. apnea+hypopnea; 3) normal vs. apnea; and 4) normal vs. hypopnea. Results SpO2 classified normal vs. apnea most accurately (98%), followed by NAF (85%), ECG (77%), and HR (63%). SpO2 also showed the highest accuracy in classifying normal vs. hypopnea (87%), and normal vs. apnea+hypopnea (96%) and three groups (82%). When the combination of four features were used, the classification accuracies were generally improved compared to use of SpO2 only (normal vs. apnea 99%; vs. hypopnea 89%; vs. apnea+hypopnea: 94%; three groups: 86%). Conclusion Deep learning with SpO2 or NAF feature most accurately classified apneas from normal sleep events, suggesting these features’ characterization of sleep apnea events. Oxygen desaturation, which is a typical pattern of hypopnea, was only the feature showing reliable accuracy in classifying hypopnea vs. normal. Nevertheless, combination of four polysomnography features could improve the identification of sleep apnea and hypopnea. Furthermore, classifying normal vs. apnea+hypopnea was more accurate than separately classifying three groups, suggesting deep learning approaches as the primary screen tool. Since the classification accuracy of using SpO2 was higher than any other features, developing a portable equipment measuring SpO2 and running deep learning algorithms has the potential for inexpensive, accurate diagnostics of obstructive sleep apnea syndrome. Support (if any) This study was supported by USC STEVENS CENTER FOR INNOVATION TECHNOLOGY ADVANCEMENT GRANTS (TAG), BrightFocus Foundation Award (A2019052S).

Signal acquisition and analysis of ambulatory electromyographic recordings for the assessment of sleep bruxism: A scoping review.

BackgroundAmbulatory electromyographic (EMG) devices are increasingly being used in sleep bruxism studies. EMG signal acquisition, analysis and scoring methods vary between studies. This may impact comparability of studies and the assessment of sleep bruxism in patients.Objectives(a) To provide an overview of EMG signal acquisition and analysis methods of recordings from limited‐channel ambulatory EMG devices for the assessment of sleep bruxism; and (b) to provide an overview of outcome measures used in sleep bruxism literature utilising such devices.MethodA scoping review of the literature was performed. Online databases PubMed and Semantics Scholar were searched for studies published in English until 7 October 2020. Data on five categories were extracted: recording hardware, recording logistics, signal acquisition, signal analysis and sleep bruxism outcomes.ResultsSeventy‐eight studies were included, published between 1977 and 2020. Recording hardware was generally well described. Reports of participant instructions in device handling and of dealing with failed recordings were often lacking. Basic elements of signal acquisition, for example amplifications factors, impedance and bandpass settings, and signal analysis, for example rectification, signal processing and additional filtering, were underreported. Extensive variability was found for thresholds used to characterise sleep bruxism events. Sleep bruxism outcomes varied, but typically represented frequency, duration and/or intensity of masticatory muscle activity (MMA).ConclusionAdequate and standardised reporting of recording procedures is highly recommended. In future studies utilising ambulatory EMG devices, the focus may need to shift from the concept of scoring sleep bruxism events to that of scoring the whole spectrum of MMA.

Are there sex-related differences in therapeutic CPAP levels in adults undergoing in-laboratory titration?

The first-choice therapy for adults with moderate/severe obstructive sleep apnea is continuous positive airway pressure (CPAP). However, studies evaluating whether the therapeutic CPAP level obtained from a titration is affected by sex are surprisingly scarce. Our main objective was to verify if sex influenced the optimal CPAP measurement obtained during a titration. This cross-sectional study was conducted in adults diagnosed with moderate/severe obstructive sleep apnea [baseline apnea-hypopnea index (AHI) ≥ 15.0 events/h] who underwent auto-adjusting CPAP titration (S9 or S10 AutoSet ResMed) in a sleep laboratory setting. All participants used a nasal mask during the titration. The optimal pressure, leak, and residual AHI values were registered. Multiple linear regression was used to evaluate if clinical and polysomnographic data influenced the therapeutic CPAP level setting (95th percentile pressure). A total of 1,006 adults were enrolled: 354 women and 652 men. There were no statistically significant sex-related differences in the CPAP requirements and leak values delineated during the titration; all P-values > .005. However, the median residual AHI was significantly higher in males vs females: 2.7 events/h vs 2.2 events/h (P = .008). Body mass index (β: 0.292, P < .001), baseline AHI (β: 0.167, P < .001), and age (β: 0.065, P = .035) were independent predictors of the therapeutic CPAP level settings. Sex does not significantly influence the therapeutic CPAP settings. However, age, BMI, and baseline AHI emerge as independent predictors of the 95thpercentile CPAP requirement during an auto-adjusting CPAP titration. Duarte RLM, Magalhães-da-Silveira FJ, Gozal D. Are there sex-related differences in therapeutic CPAP levels in adults undergoing in-laboratory titration? JClin Sleep Med. 2021;17(9):1815-1820.

Altered sleep behavior in a genetic mouse model of impaired fear extinction

Sleep disturbances are a common complaint of anxiety patients and constitute a hallmark feature of post-traumatic stress disorder (PTSD). Emerging evidence suggests that poor sleep is not only a secondary symptom of anxiety- and trauma-related disorders but represents a risk factor in their development, for example by interfering with emotional memory processing. Fear extinction is a critical mechanism for the attenuation of fearful and traumatic memories and multiple studies suggest that healthy sleep is crucial for the formation of extinction memories. However, fear extinction is often impaired in anxiety- and trauma-related disorders—an endophenotype that is perfectly modelled in the 129S1/SvImJ inbred mouse strain. To investigate whether these mice exhibit altered sleep at baseline that could predispose them towards maladaptive fear processing, we compared their circadian sleep/wake patterns to those of typically extinction-competent C57BL/6 mice. We found significant differences regarding diurnal distribution of sleep and wakefulness, but also sleep architecture, spectral features and sleep spindle events. With regard to sleep disturbances reported by anxiety- and PTSD patients, our findings strengthen the 129S1/SvImJ mouse models’ face validity and highlight it as a platform to investigate novel, sleep-focused diagnostic and therapeutic strategies. Whether the identified alterations causally contribute to its pathological anxiety/PTSD-like phenotype will, however, have to be addressed in future studies.

Efficacy and safety of pharmacological, physical, and psychological interventions for the management of chronic pain in children: a WHO systematic review and meta-analysis.

Chronic pain in childhood is an international public health problem. We conducted a systematic review and meta-analysis to provide a summary of the published evidence of pharmacological, physical, and psychological therapies for children with chronic pain conditions. We searched CENTRAL, MEDLINE, EMBASE, and PsycINFO from inception to April 2020; clinical trial registries; and other sources for randomised controlled trials or comparative observational trials. We extracted critical outcomes of pain intensity, quality of life, physical functioning, role functioning, emotional functioning, sleep, and adverse events. We assessed studies for risk of bias and certainty of the evidence using GRADE. We included 34 pharmacological (4091 participants), 25 physical therapy (1470 participants), and 63 psychological trials (5025 participants). Participants reported a range of chronic pain conditions. Most studies were assessed to have unclear or high risk of bias across multiple domains. Pharmacological, physical, and psychological therapies showed some benefit for reducing pain, posttreatment, but only physical and psychological therapies improved physical functioning. We found no benefit of any treatment modality for health-related quality of life, role functioning, emotional functioning, or sleep. Adverse events were poorly reported, particularly for psychological and physical interventions. The largest evidence base for the management of chronic pain in children supports the use of psychological therapies, followed by pharmacological and physical therapies. However, we rated most outcomes as low or very low certainty, meaning further evidence is likely to change our confidence in the estimates of effects. This protocol was registered on PROSPERO (CRD42020172451).

Oral magnesium supplementation for insomnia in older adults: a Systematic Review & Meta-Analysis

BackgroundMagnesium supplementation is often purported to improve sleep; however, as both an over-the-counter sleep aid and a complementary and alternative medicine, there is limited evidence to support this assertion. The aim was to assess the effectiveness and safety of magnesium supplementation for older adults with insomnia.MethodsA search was conducted in MEDLINE, EMBASE, Allied and Complementary Medicine, clinicaltrials.gov and two grey literature databases comparing magnesium supplementation to placebo or no treatment. Outcomes were sleep quality, quantity, and adverse events. Risk of bias and quality of evidence assessments were carried out using the RoB 2.0 and Grading of Recommendations Assessment, Development and Evaluation (GRADE) approaches. Data was pooled and treatment effects were quantified using mean differences. For remaining outcomes, a modified effects direction plot was used for data synthesis.ResultsThree randomized control trials (RCT) were identified comparing oral magnesium to placebo in 151 older adults in three countries. Pooled analysis showed that post-intervention sleep onset latency time was 17.36 min less after magnesium supplementation compared to placebo (95% CI − 27.27 to − 7.44, p = 0.0006). Total sleep time improved by 16.06 min in the magnesium supplementation group but was statistically insignificant. All trials were at moderate-to-high risk of bias and outcomes were supported by low to very low quality of evidence.ConclusionThis review confirms that the quality of literature is substandard for physicians to make well-informed recommendations on usage of oral magnesium for older adults with insomnia. However, given that oral magnesium is very cheap and widely available, RCT evidence may support oral magnesium supplements (less than 1 g quantities given up to three times a day) for insomnia symptoms.