Is sleep bruxism in obstructive sleep apnea only an oral health related problem?

BACKGROUND: The etiology of sleep bruxism in obstructive sleep apnea (OSA) patients is not yet fully clarified. This prospective clinical study aimed to investigate the connection between probable sleep bruxism, electromyographic muscle tone, and respiratory sleep patterns recorded during polysomnography. METHODS: 106 patients with OSA (74 males, 31 females, mean age: 56.1 ± 11.4 years) were divided into two groups (sleep bruxism: SB; no sleep bruxism: NSB). Probable SB were based on the AASM criteria: self-report of clenching/grinding, orofacial symptoms upon awakening, abnormal tooth wear and hypertrophy of the masseter muscle. Both groups underwent clinical examination for painful muscle symptoms aligned with Temporomandibular Disorders Diagnostic Criteria (DC/TMD), such as myalgia, myofascial pain, and headache attributed to temporomandibular disorder. Additionally, non-complaint positive muscle palpation and orofacial-related limitations (Jaw Functional Limited Scale-20: JFLS-20) were assessed. A one-night polysomnography with electromyographic masseter muscle tone (EMG) measurement was performed. Descriptive data, inter-group comparisons and multivariate logistic regression were calculated. RESULTS: OSA patients had a 37.1% prevalence of SB. EMG muscle tone (N1-N3, REM; P = 0.001) and the number of hypopneas (P = 0.042) were significantly higher in the sleep bruxism group. While measures like apnea-hypopnea-index (AHI), respiratory-disturbance-index (RDI), apnea index (AI), hypopnea-index (HI), number of arousals, and heart rate (1/min) were elevated in sleep bruxers, the differences were not statistically significant. There was no difference in sleep efficiency (SE; P = 0.403). Non-complaint masseter muscle palpation (61.5%; P = 0.015) and myalgia (41%; P = 0.010) were significant higher in SB patients. Multivariate logistic regression showed a significant contribution of EMG muscle tone and JFLS-20 to bruxism risk. CONCLUSION: Increased EMG muscle tone and orofacial limitations can predict sleep bruxism in OSA patients. Besides, SB patients suffer more from sleep disorder breathing. Thus, sleep bruxism seems to be not only an oral health related problem in obstructive apnea. Consequently, interdisciplinary interventions are crucial for effectively treating these patients. TRIAL REGISTRATION: The study was approved by the Ethics Committee of Philipps-University Marburg (reg. no. 13/22-2022) and registered at the "German Clinical Trial Register, DRKS" (DRKS0002959).

Sleep Disorders and Traffic Accidents: Unveiling the Hidden Risks.

BACKGROUND Numerous countries, Vietnam included, have persistently high annual rates of traffic accidents. Despite concerted government efforts to reduce the annual traffic accident rate, the toll of fatalities and consequential injuries from these accidents rises each year. Various factors contribute to these incidents, notably including alcohol consumption while driving, inadequate awareness of traffic regulations, and substandard traffic infrastructure. However, an under-recognized risk in developing nations such as Vietnam is the prevalence of sleep disorders. Conditions such as obstructive sleep apnea syndrome and obesity hypoventilation syndrome, while prevalent, remain inadequately assessed and treated. These disorders represent significant yet largely unaddressed contributors to the heightened risk of traffic accidents. CASE REPORT We describe the case of a 55-year-old Vietnamese man hospitalized due to long-standing respiratory complications and profound daytime sleepiness. Over the past 2 years, the patient gained 10 kg. Consequently, he frequently experienced drowsiness, leading to 4 traffic accidents. Despite previous hospitalizations, this sleep disorder had gone undiagnosed and untreated. Diagnostic assessments confirmed concurrent obstructive sleep apnea and obesity hypoventilation syndrome through polysomnography and blood gas analyses. Treatment involving non-invasive positive airway pressure therapy notably alleviated symptoms and substantially improved his quality of life within a concise 3-month period. CONCLUSIONS Obstructive sleep apnea and obesity hypoventilation syndrome are contributory factors to excessive daytime somnolence, significantly increasing vulnerability to traffic accidents. Regrettably, this critical intersection remains inadequately addressed. Addressing these concerns comprehensively through dedicated research initiatives should be imperative before considering the universal issuance of driver's licenses to all road users in Vietnam.

Machine learning-empowered sleep staging classification using multi-modality signals.

The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.

Predictors of moderate to severe obstructive sleep apnea in patients with lung cancer.

BACKGROUND AND OBJECTIVES: OSA is a known medical condition that is associated with several comorbidities and affect patients' quality of life. The association between OSA and lung cancer remains debated. Some studies reported increased prevalence of OSA in patients with lung cancer. We aimed to assess predictors of moderate-to-severe OSA in patients with lung cancer. METHODS: We enrolled 153 adult patients who were newly diagnosed with lung cancer. Cardiorespiratory monitoring was performed using home sleep apnea device. We carried out Univariate and multivariate logistic regression analysis on multiple parameters including age, gender, smoking status, neck circumference, waist circumference, BMI, stage and histopathology of lung cancer, presence of superior vena cava obstruction, and performance status to find out the factors that are independently associated with a diagnosis of moderate-to-severe OSA. RESULTS: Our results suggest that poor performance status is the most significant predictor of moderate to severe OSA in patients with lung cancer after controlling for important confounders. CONCLUSION: Performance status is a predictor of moderate to severe OSA in patients with lung cancer in our population of middle eastern ethnicity.

Association Between Sleep Quality and Deep Learning-Based Sleep Onset Latency Distribution Using an Electroencephalogram.

To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep learning-based model using a 30 sec sleep electroencephalogram (EEG) early in the sleep cycle to predict sleep onset latency (SOL) distribution and explore associations with sleep quality (SQ). We propose a deep learning model composed of a structure that decomposes and restores the signal in epoch units and a structure that predicts the SOL distribution. We used the Sleep Heart Health Study public dataset, which includes a large number of study subjects, to estimate and evaluate the proposed model. The proposed model estimated the SOL distribution and divided it into four clusters. The advantage of the proposed model is that it shows the process of falling asleep for individual participants as a probability graph over time. Furthermore, we compared the baseline of good SQ and SOL and showed that less than 10 minutes SOL correlated better with good SQ. Moreover, it was the most suitable sleep feature that could be predicted using early EEG, compared with the total sleep time, sleep efficiency, and actual sleep time. Our study showed the feasibility of estimating SOL distribution using deep learning with an early EEG and showed that SOL distribution within 10 minutes was associated with good SQ.

Sleep disturbance in clinical and preclinical scrapie-infected sheep measured by polysomnography.

Neurodegenerative diseases are characterised by neuronal loss and abnormal deposition of pathological proteins in the nervous system. Among the most common neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease and transmissible spongiform encephalopathies (TSEs). Sleep and circadian rhythm disturbances are one of the most common symptoms in patients with neurodegenerative diseases. Currently, one of the main objectives in the study of TSEs is to try to establish an early diagnosis, as clinical signs do not appear until the damage to the central nervous system is very advanced, which prevents any therapeutic approach. In this paper, we provide the first description of sleep disturbance caused by classical scrapie in clinical and preclinical sheep using polysomnography compared to healthy controls. Fifteen sheep classified into three groups, clinical, preclinical and negative control, were analysed. The results show a decrease in total sleep time as the disease progresses, with significant changes between control, clinical and pre-clinical animals. The results also show an increase in sleep fragmentation in clinical animals compared to preclinical and control animals. In addition, sheep with clinical scrapie show a total loss of Rapid Eye Movement sleep (REM) and alterations in Non Rapid Eyes Movement sleep (NREM) compared to control sheep, demonstrating more shallow sleep. Although further research is needed, these results suggest that prion diseases also produce sleep disturbances in animals and that polysomnography could be a diagnostic tool of interest in clinical and preclinical cases of prion diseases.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing.

BACKGROUND: Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep-wake classification and SDB event detection in order to automatically estimate the apnea-hypopnea index (AHI) as severity indicator. METHODS: The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep-wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. RESULTS: We achieved a Cohen's kappa of 0.70 in the sleep-wake classification task, corresponding to a Spearman's correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep-wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen's kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). CONCLUSION: Assisted with automatic sleep-wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep-wake detection.

In-Laboratory Polysomnography Worsens Obstructive Sleep Apnea by Changing Body Position Compared to Home Testing.

(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more naturally. Adopting a natural position may decrease obstructive sleep apnea (OSA) severity in PSG3, independently of missing hypopneas associated with arousals. (2) Methods: Patients with suspected OSA performed PSG1 and PSG3 in a randomized sequence. We performed an additional analysis, called reduced polysomnography, in which we blindly reassessed all PSG1 tests to remove electroencephalographic electrodes, electrooculogram, and surface electromyography data to estimate the impact of not scoring sleep and arousal-based hypopneas on the test results. A difference of 15 or more in the apnea-hypopnea index (AHI) between tests was deemed clinically relevant. We compared the group of patients with and without clinically relevant differences between lab and home tests (3) Results: As expected, by not scoring sleep, there was a decrease in OSA severity in the lab test, similar to the home test results. The group of patients with clinically relevant differences between lab and home tests presented more severe OSA in the lab compared to the other group (mean AHI, 42.5 vs. 20.2 events/h, p = 0.002), and this difference disappeared in the home test. There was no difference between groups in the shift of OSA severity by abolishing sleep scoring in the lab. However, by comparing lab and home tests, there were greater variations in supine AHI and time spent in the supine position in the group with a clinically relevant difference, either with or without scoring sleep, showing an impact of the site of the test on body position during sleep. These variations presented as a marked increase or decrease in supine outcomes according to the site of the test, with no particular trend. (4) Conclusions: In-lab polysomnography may artificially increase OSA severity in a subset of patients by inducing marked changes in body position compared to home tests. The location of the sleep test seems to interfere with the evaluation of patients with more severe OSA.

Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder.

Cognitive symptoms and sleep disturbance (SD) are common non-mood-related symptoms of major depressive disorder (MDD). In clinical practice, both cognitive symptoms and SD are related to MDD progression. However, there are only a few studies investigating the connection between cognitive symptoms and SD in patients with MDD, and only preliminary evidence suggests a significant association between cognitive symptoms and SD in patients with mood disorders. This study investigates the relationship between cognitive symptoms and sleep quality in patients with major depressive disorder. Patients (n = 20) with MDD were enrolled; their mean Hamilton Depression Scale-17 score was 21.95 (±2.76). Gold standard polysomnography (PSG) was used to assess sleep quality, and the validated THINC-integrated tool (the cognitive screening tool) was used to evaluate cognitive function in MDD patients. Overall, the results showed significant correlations between the cognitive screening tool's total score and sleep latency, wake-after-sleep onset, and sleep efficiency. These findings indicate that cognitive symptoms are associated with poor sleep quality among patients with MDD.

Older adults at greater risk for Alzheimer's disease show stronger associations between sleep apnea severity in REM sleep and verbal memory.

BACKGROUND: Obstructive sleep apnea (OSA) increases risk for cognitive decline and Alzheimer's disease (AD). While the underlying mechanisms remain unclear, hypoxemia during OSA has been implicated in cognitive impairment. OSA during rapid eye movement (REM) sleep is usually more severe than in non-rapid eye movement (NREM) sleep, but the relative effect of oxyhemoglobin desaturation during REM versus NREM sleep on memory is not completely characterized. Here, we examined the impact of OSA, as well as the moderating effects of AD risk factors, on verbal memory in a sample of middle-aged and older adults with heightened AD risk. METHODS: Eighty-one adults (mean age:61.7 ± 6.0 years, 62% females, 32% apolipoprotein E ε4 allele (APOE4) carriers, and 70% with parental history of AD) underwent clinical polysomnography including assessment of OSA. OSA features were derived in total, NREM, and REM sleep. REM-NREM ratios of OSA features were also calculated. Verbal memory was assessed with the Rey Auditory Verbal Learning Test (RAVLT). Multiple regression models evaluated the relationships between OSA features and RAVLT scores while adjusting for sex, age, time between assessments, education years, body mass index (BMI), and APOE4 status or parental history of AD. The significant main effects of OSA features on RAVLT performance and the moderating effects of AD risk factors (i.e., sex, age, APOE4 status, and parental history of AD) were examined. RESULTS: Apnea-hypopnea index (AHI), respiratory disturbance index (RDI), and oxyhemoglobin desaturation index (ODI) during REM sleep were negatively associated with RAVLT total learning and long-delay recall. Further, greater REM-NREM ratios of AHI, RDI, and ODI (i.e., more events in REM than NREM) were related to worse total learning and recall. We found specifically that the negative association between REM ODI and total learning was driven by adults 60 + years old. In addition, the negative relationships between REM-NREM ODI ratio and total learning, and REM-NREM RDI ratio and long-delay recall were driven by APOE4 carriers. CONCLUSION: Greater OSA severity, particularly during REM sleep, negatively affects verbal memory, especially for people with greater AD risk. These findings underscore the potential importance of proactive screening and treatment of REM OSA even if overall AHI appears low.

Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging.

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.

Effect of a nutraceutical combination on sleep quality among people with impaired sleep: a randomised, placebo-controlled trial.

In this randomised, placebo-controlled trial, adults with impaired sleep (Pittsburgh Sleep Quality Index ≥ 5) were randomly assigned using a minimization algorithm to receive a formulation containing L-theanine plus lemon balm, valerian, and saffron extracts, or placebo, during 6 weeks. Objective sleep quality parameters were measured using an actigraphy device. We enrolled and randomised 64 individuals, 31 from the active group and 27 from the placebo group completed the 6 week follow-up. Mean sleep efficiency remained unmodified in the active group, and increased by 3% in the placebo group, the between-group difference in the change was not statistically significant (p = 0.49). Total sleep time also improved more with placebo (13.0 vs. 1.33 min, p = 0.66). Time wake after sleep onset (WASO) decreased more in the active group (4.6% vs. 2.4%), but the difference was not significant (p = 0.33). Mean PSQI decreased by 3.11 points (32.3%) in the active group, and by 3.86 points (39.5%) in the placebo group (p = 0.41). SF-36 increased more with placebo (+ 18.3 in active, + 32.1 in placebo, p = 0.68). Salivary cortisol remained unchanged in both groups. No serious adverse events were reported. Among adults with impaired sleep, a nutraceutical combination did not improve objective or subjective sleep parameters more than a placebo infusion.

The Association Between the 5-Hydroxytryptamine Receptor 2A Gene Variants rs6311 and rs6313 and Obstructive Sleep Apnea in the Iranian Kurdish Population.

Introduction: Sleep is one of the most significant parts of everyone's life. Most people sleep for about one-third of their lives. Sleep disorders negatively impact the quality of life. Obstructive sleep apnea (OSA) is a severe sleep disorder that significantly impacts the patient's life and their family members. This study aimed to investigate the relationship between rs6313 and rs6311 polymorphisms in the serotonin receptor type 2A gene and OSA in the Kurdish population. Materials and Methods: The study's population comprises 100 OSA sufferers and 100 healthy people. Polysomnography diagnostic tests were done on both the patient and control groups. The polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP) was used to investigate the relationship between OSA and LEPR gene polymorphisms. Results: Statistical analysis showed a significant relationship between genotype frequencies of patient and control groups of rs6311 with OSA in dominant [odds ratio (OR) = 5.203, p < 0.001) and codominant models (OR = 9.7, p < 0.001). Also, there was a significant relationship between genotype frequencies of patient and control groups of rs6313 with OSA in dominant (OR = 10.565, p < 0.001) and codominant models (OR = 5.938, p < 0.001). Conclusions: Findings from the study demonstrated that the two polymorphisms rs6311 and rs6313 could be effective at causing OSA; however, there was no correlation between the severity of the disease and either of the two polymorphisms.

Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders.

Objective.Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders.Approach.We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram.Main results.For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without.Significance.The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.

Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults.

Introduction: This study aimed to validate the ability of a prototype sport watch (Polar Electro Oy, FI) to recognize wake and sleep states in two trials with and without an interval training session (IT) 6 h prior to bedtime. Methods: Thirty-six participants completed this study. Participants performed a maximal aerobic test and three polysomnography (PSG) assessments. The first night served as a device familiarization night and to screen for sleep apnea. The second and third in-home PSG assessments were counterbalanced with/without IT. Accuracy and agreement in detecting sleep stages were calculated between PSG and the prototype. Results: Accuracy for the different sleep stages (REM, N1 and N2, N3, and awake) as a true positive for the nights without exercise was 84 ± 5%, 64 ± 6%, 81 ± 6%, and 91 ± 6%, respectively, and for the nights with exercise was 83 ± 7%, 63 ± 8%, 80 ± 7%, and 92 ± 6%, respectively. The agreement for the sleep night without exercise was 60.1 ± 8.1%, k = 0.39 ± 0.1, and with exercise was 59.2 ± 9.8%, k = 0.36 ± 0.1. No significant differences were observed between nights or between the sexes. Conclusion: The prototype showed better or similar accuracy and agreement to wrist-worn consumer products on the market for the detection of sleep stages with healthy adults. However, further investigations will need to be conducted with other populations.

Implications of depressive mood in OSAHS patients: insights from event-related potential.

BACKGROUND: Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a chronic breathing disorder characterized by recurrent upper airway obstruction during sleep. Although previous studies have shown a link between OSAHS and depressive mood, the neurobiological mechanisms underlying mood disorders in OSAHS patients remain poorly understood. This study aims to investigate the emotion processing mechanism in OSAHS patients with depressive mood using event-related potentials (ERPs). METHODS: Seventy-four OSAHS patients were divided into the depressive mood and non-depressive mood groups according to their Self-rating Depression Scale (SDS) scores. Patients underwent overnight polysomnography and completed various cognitive and emotional questionnaires. The patients were shown facial images displaying positive, neutral, and negative emotions and tasked to identify the emotion category, while their visual evoked potential was simultaneously recorded. RESULTS: The two groups did not differ significantly in age, BMI, and years of education, but showed significant differences in their slow wave sleep ratio (P = 0.039), ESS (P = 0.006), MMSE (P < 0.001), and MOCA scores (P = 0.043). No significant difference was found in accuracy and response time on emotional face recognition between the two groups. N170 latency in the depressive group was significantly longer than the non-depressive group (P = 0.014 and 0.007) at the bilateral parieto-occipital lobe, while no significant difference in N170 amplitude was found. No significant difference in P300 amplitude or latency between the two groups. Furthermore, N170 amplitude at PO7 was positively correlated with the arousal index and negatively with MOCA scores (both P < 0.01). CONCLUSION: OSAHS patients with depressive mood exhibit increased N170 latency and impaired facial emotion recognition ability. Special attention towards the depressive mood among OSAHS patients is warranted for its implications for patient care.

Cardiopulmonary Coupling Spectrogram as an Ambulatory Method for Assessing Sleep Disorders in Patients With Autoimmune Encephalitis.

BACKGROUND AND OBJECTIVES: Sleep disorders are a common and important clinical feature in patients with autoimmune encephalitis (AE); however, they are poorly understood. We aimed to evaluate whether cardiopulmonary coupling (CPC), an electrocardiogram-based portable sleep monitoring technology, can be used to assess sleep disorders in patients with AE. METHODS: Patients fulfilling the diagnostic criteria of AE were age- and sex-matched with recruited healthy control subjects. All patients and subjects received CPC testing between August 2020 and December 2022. Demographic data, clinical information, and Pittsburgh Sleep Quality Index (PSQI) scores were collected from the medical records. Data analysis was performed using R language programming software. RESULTS: There were 60 patients with AE (age 26.0 [19.8-37.5] years, male 55%) and 66 healthy control subjects (age 30.0 [25.8-32.0] years, male 53%) included in this study. Compared with healthy subjects, patients with AE had higher PSQI scores (7.00 [6.00-8.00] vs 3.00 [2.00-4.00], p < 0.001), lower sleep efficiency (SE 80% [71%-87%] vs 92% [84%-95%], p < 0.001), lower percentage of high-frequency coupling (25% [14%-43%] vs 45% [38%-53%], p < 0.001), higher percentage of REM sleep (19% ± 9% vs 15% ± 7%, p < 0.001), higher percentage of wakefulness (W% 16% [11%-25%] vs 8% [5%-16%], p = 0.074), higher low-frequency to high-frequency ratio (LF/HF 1.29 [0.82-2.40] vs 0.91 [0.67-1.29], p = 0.001), and a higher CPC-derived respiratory disturbance index (9.78 [0.50-22.2] vs 2.95 [0.40-6.53], p < 0.001). Follow-up evaluation of 14 patients showed a decrease in the PSQI score (8.00 [6.00-9.00] vs 6.00 [5.00-7.00], p = 0.008), an increased SE (79% [69%-86%] vs 89% [76%-91%], p = 0.030), and a decreased W% (20% [11%-30%] vs 11% [8%-24], p = 0.035). Multiple linear regression indicated that SE (-7.49 [-9.77 to -5.21], p < 0.001) and LF/HF ratio (0.37 [0.13-0.6], p = 0.004) were independent factors affecting PSQI scores in patients with AE. DISCUSSION: Sleep disorders with autonomic dysfunction are common in patients with AE. Improvements in the PSQI score and SE precede the restoration of sleep microstructural disruption in the remission stage. CPC parameters may be useful in predicting sleep disorders in patients with AE.

The effect of compliance with PAP therapy on biochemical parameters in patients with obstructive sleep apnea syndrome: A 6-month follow-up study.

Introduction: The gold standard treatment for obstructive sleep apnea syndrome (OSAS) is positive airway pressure therapy (PAP) treatments. PAP treatments reduce complications by reducing apnea and hypopnea attacks by creating airflow at a determined pressure. In our study, we aimed to examine the effect of treatment compliance on kidney and liver functions, apneahypopnea (AHI) index, and lipid profile of patients diagnosed with OSAS and started PAP treatment. Materials and Methods: Patients who were admitted to the sleep laboratory of our hospital between September 2022 and September 2023 and started PAP treatment after PSG were included in our study. Patients who were called for follow-up six months after the initiation of PAP treatment were divided into two groups according to their compliance with PAP treatment. Patients who used the device for at least four hours per night and more than 70% at night were grouped as PAP-compliant patients, while the other patients were grouped as non-PAP-compliant patients. Result: It was observed that uric acid, BUN, triglyceride, total cholesterol, ALT, GGT, ALP, and AHI levels of the patients who started PAP treatment decreased after six months (p= 0.001, 0.006, <0.001, 0.006, 0.01, <0.001, <0.001, <0.001 with). It was observed that HDL cholesterol levels increased (p≤ 0.001). It was observed that the change in uric acid, AHI, total cholesterol, and GGT levels in group 1 (n= 36) patients who were compliant with PAP treatment was statistically higher than in group 2 (n= 30) patients (p< 0.001, <0.03, <0.001, 0.008, respectively). Conclusions: Uric acid, total cholesterol and GGT are biomarkers that may increase in OSAS due to intermittent hypoxia with the involvement of other systems. Since a decrease in these biomarkers can be observed in the early period depending on treatment compliance, these biomarkers can be used practically in the follow-up of treatment compliance and treatment efficacy.

Adenotonsillectomy for Obstructive Sleep Apnea in Children.

Obstructed breathing is the most common indication for tonsillectomy in children. Although tonsillectomy is performed frequently worldwide, the surgery is associated with a number of significant complications such as bleeding and respiratory failure. Complication risk depends on a number of complex factors, including indications for surgery, demographics, patient comorbidities, and variations in perioperative techniques. While polysomnography is currently accepted as the gold standard diagnostic tool for obstructive sleep apnea, studies evaluating outcomes following surgery suggest that more research is needed on the identification of more readily available and accurate tools for the diagnosis and follow-up of children with obstructed breathing.