Bidirectional Association Among the Type of Atrial Fibrillation, Sleep-Disordered Breathing Severity, Heart Failure Progression, and Left Atrial Enlargement, in Patients with Atrial Fibrillation.

This study aimed to clarify (1) the association among the atrial fibrillation (AF) type, sleep-disordered breathing (SDB), heart failure (HF), and left atrial (LA) enlargement, (2) the independent predictors of LA enlargement, and (3) the effects of ablation on those conditions in patients with AF. The study's endpoint was LA enlargement (LA volume index [LAVI] ≥ 78 mL/m2).Of 423 patients with nonvalvular AF, 236 were enrolled. We evaluated the role of the clinical parameters such as the AF type, SDB severity, and HF in LA enlargement. Among them, 141 patients exhibiting a 3% oxygen desaturation index (ODI) of ≥ 10 events/hour underwent polysomnography to evaluate the SDB severity measured by the apnea-hypopnea index (AHI). The LA enlargement and HF were characterized by the LA diameter/LAVI, an increase in the B-type natriuretic peptide level, and a lower left ventricular ejection fraction.This study showed that non-paroxysmal AF (NPAF) rather than paroxysmal AF (PAF), the SDB severity, LA enlargement, and HF progression had bidirectional associations and exacerbated each other, which generated a vicious cycle that contributed to the LA enlargement. NPAF (OR = 4.55, P < 0.001), an AHI of ≥ 25.10 events/hour (OR = 1.55, P = 0.003), and a 3% ODI of ≥ 15.43 events/hour (OR = 1.52, P = 0.003) were independent predictors of an acceleration of the LA enlargement. AF ablation improved the HF and LA enlargement.To break this vicious cycle, AF ablation may be the basis for suppressing the LA enlargement and HF progression subsequently eliminating the substrates for AF and SDB in patients with AF.

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.

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.

Different heart rate variability profile during sleep in mid-later life adults with remitted early-onset versus late-onset depression.

BACKGROUND: In mid-later life adults, early-onset and late-onset (i.e., onset ≥50 years) depression appear to be underpinned by different pathophysiology yet have not been examined in relation to autonomic function. Sleep provides an opportunity to examine the autonomic nervous system as the physiology changes across the night. Hence, we aimed to explore if autonomic profile is altered in mid-later life adults with remitted early-onset, late-onset and no history of lifetime depression. METHODS: Participants aged 50-90 years (n = 188) from a specialised clinic underwent a comprehensive clinical assessment and completed an overnight polysomnography study. General Linear Models were used to examine the heart rate variability differences among the three groups for four distinct sleep stages and the wake after sleep onset. All analyses controlled for potential confounders - age, sex, current depressive symptoms and antidepressant usage. RESULTS: For the wake after sleep onset, mid-later life adults with remitted early-onset depression had reduced standard deviation of Normal to Normal intervals (SDNN; p = .014, d = -0.64) and Shannon Entropy (p = .004, d = -0.46,) than those with no history of lifetime depression. Further, the late-onset group showed a reduction in high-frequency heart rate variability (HFn.u.) during non-rapid eye movement sleep stage 2 (N2; p = .005, d = -0.53) and non-rapid eye movement sleep stage 3 (N3; p = .009, d = -0.55) when compared to those with no lifetime history. LIMITATIONS: Causality between heart rate variability and depression cannot be derived in this cross-sectional study. Longitudinal studies are needed to examine the effects remitted depressive episodes on autonomic function. CONCLUSION: The findings suggest differential autonomic profile for remitted early-onset and late-onset mid-later life adults during sleep stages and wake periods. The differences could potentially serve as peripheral biomarkers in conjunction with more disease-specific markers of depression to improve diagnosis and prognosis.

Multimodal data integration for enhanced longitudinal prediction for cardiac and cerebrovascular events following initial diagnosis of obstructive sleep apnea syndrome.

Background: Obstructive sleep apnea syndrome (OSAS), a prevalent condition, often coexists with intricate metabolic issues and is frequently associated with negative cardiovascular outcomes. We developed a longitudinal prediction model integrating multimodal data for cardiovascular risk stratification of patients with an initial diagnosis of OSAS. Methods: We reviewed the data of patients with new-onset OSAS who underwent diagnostic polysomnography between 2018-19. Patients were treated using standard treatment regimens according to clinical practice guidelines. Results: Over a median follow-up of 32 months, 98/729 participants (13.4%) experienced our composite outcome. At a ratio of 7:3, cases were randomly divided into development (n = 510) and validation (n = 219) cohorts. A prediction nomogram was created using six clinical factors - sex, age, diabetes mellitus, history of coronary artery disease, triglyceride-glucose index, and apnea-hypopnea index. The prediction nomogram showed excellent discriminatory power, based on Harrell's C-index values of 0.826 (95% confidence interval (CI) = 0.779-0.873) for the development cohort and 0.877 (95% CI = 0.824-0.93) for the validation cohort. Moreover, comparing the predicted and observed major adverse cardiac and cerebrovascular events in both development and validation cohorts indicated that the prediction nomogram was well-calibrated. Decision curve analysis demonstrated the good clinical applicability of the prediction nomogram. Conclusions: Our findings demonstrated the construction of an innovative visualisation tool that utilises various types of data to predict poor outcomes in Chinese patients diagnosed with OSAS, providing accurate and personalised therapy. Registration: Chinese Clinical Trial Registry ChiCTR2300075727.

"Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data.

BACKGROUND: Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. NEW METHOD: Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. RESULTS: Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. COMPARISON WITH EXISTING METHODS: Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. CONCLUSIONS: The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

Independent relationship between sleep apnea-specific hypoxic burden and glucolipid metabolism disorder: a cross-sectional study.

OBJECTIVES: Obstructive sleep apnea (OSA) is associated with abnormal glucose and lipid metabolism. However, whether there is an independent association between Sleep Apnea-Specific Hypoxic Burden (SASHB) and glycolipid metabolism disorders in patients with OSA is unknown. METHODS: We enrolled 2,173 participants with suspected OSA from January 2019 to July 2023 in this study. Polysomnographic variables, biochemical indicators, and physical measurements were collected from each participant. Multiple linear regression analyses were used to evaluate independent associations between SASHB, AHI, CT90 and glucose as well as lipid profile. Furthermore, logistic regressions were used to determine the odds ratios (ORs) for abnormal glucose and lipid metabolism across various SASHB, AHI, CT90 quartiles. RESULTS: The SASHB was independently associated with fasting blood glucose (FBG) (ß = 0.058, P = 0.016), fasting insulin (FIN) (ß = 0.073, P < 0.001), homeostasis model assessment of insulin resistance (HOMA-IR) (ß = 0.058, P = 0.011), total cholesterol (TC) (ß = 0.100, P < 0.001), total triglycerides (TG) (ß = 0.063, P = 0.011), low-density lipoprotein cholesterol (LDL-C) (ß = 0.075, P = 0.003), apolipoprotein A-I (apoA-I) (ß = 0.051, P = 0.049), apolipoprotein B (apoB) (ß = 0.136, P < 0.001), apolipoprotein E (apoE) (ß = 0.088, P < 0.001) after adjustments for confounding factors. Furthermore, the ORs for hyperinsulinemia across the higher SASHB quartiles were 1.527, 1.545, and 2.024 respectively, compared with the lowest quartile (P < 0.001 for a linear trend); the ORs for hyper-total cholesterolemia across the higher SASHB quartiles were 1.762, 1.998, and 2.708, compared with the lowest quartile (P < 0.001 for a linear trend) and the ORs for hyper-LDL cholesterolemia across the higher SASHB quartiles were 1.663, 1.695, and 2.316, compared with the lowest quartile (P < 0.001 for a linear trend). Notably, the ORs for hyper-triglyceridemia{1.471, 1.773, 2.099} and abnormal HOMA-IR{1.510, 1.492, 1.937} maintained a consistent trend across the SASHB quartiles. CONCLUSIONS: We found SASHB was independently associated with hyperinsulinemia, abnormal HOMA-IR, hyper-total cholesterolemia, hyper-triglyceridemia and hyper-LDL cholesterolemia in Chinese Han population. Further prospective studies are needed to confirm that SASHB can be used as a predictor of abnormal glycolipid metabolism disorders in patients with OSA. TRIAL REGISTRATION: ChiCTR1900025714 { http://www.chictr.org.cn/ }; Prospectively registered on 6 September 2019; China.

Assessing sleep-related breathing disorders among newly diagnosed rheumatoid and psoriatic arthritis patients: a cross-sectional study.

OBJECTIVES: This cross-sectional study aimed to determine the prevalence and risk factors for sleep-related breathing disorders (SRBD) in newly diagnosed, untreated rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients, and to develop a screening algorithm for early detection. METHODS: We evaluated newly diagnosed RA or PsA patients using the Epworth Sleepiness Scale (ESS) questionnaire, cardiorespiratory polygraphy (RPG), and clinical and laboratory assessments. Sleep apnea syndrome (SAS) was diagnosed based on pathological RPG findings excessive daytime sleepiness, defined as ESS score above 10. RESULTS: The study included 39 patients (22 RA, 17 PsA) and 23 controls. In RPG, SRBD was identified in 38.5% of arthritis patients compared to 39.1% of controls (p = 1.00), with male gender (p = .004) and age (p < .001) identified as risk factors. Excessive daytime sleepiness was noted in 36.4% of RA patients, 17.6% of PsA patients, and 21.7% of controls. Of the 24 patients diagnosed with SRBD, 41.6% met the criteria for SAS. SAS prevalence was 31.8% among RA patients, 0% in PsA patients, and 13% in controls. A significant association was observed between excessive daytime sleepiness and SRBD (p = .036). CONCLUSION: Our findings reveal a high prevalence of SRBD in newly diagnosed, untreated RA and PsA patients in ESS and RPG, with excessive daytime sleepiness being a reliable predictor of SRBD. Patients with RA exhibited a higher predisposition to SAS. We therefore suggest incorporating ESS and RPG as screening tools in RA or PsA for early detection and management of SRBD.

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

BACKGROUND: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. RESEARCH QUESTION: To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. STUDY DESIGN AND METHODS: A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. RESULTS: Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. INTERPRETATION: We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.

Sleep Delta power, age, and sex effects in treatment-resistant depression.

Electroencephalographic (EEG) deficits in slow wave activity or Delta power (0.5-4 Hz) indicate disturbed sleep homeostasis and are hallmarks of depression. Sleep homeostasis is linked to restorative sleep and potential antidepressant response via non-rapid eye movement (NREM) slow wave sleep (SWS) during which neurons undergo essential repair and rejuvenation. Decreased Low Delta power (0.5-2 Hz) was previously reported in individuals with depression. This study investigated power levels in the Low Delta (0.5-<2 Hz), High Delta (2-4 Hz), and Total Delta (0.5-4 Hz) bands and their association with age, sex, and disrupted sleep in treatment-resistant depression (TRD). Mann-Whitney U tests were used to compare the nightly progressions of Total Delta, Low Delta, and High Delta in 100 individuals with TRD and 24 healthy volunteers (HVs). Polysomnographic parameters were also examined, including Total Sleep Time (TST), Sleep Efficiency (SE), and Wake after Sleep Onset (WASO). Individuals with TRD had lower Delta power during the first NREM episode (NREM1) than HVs. The deficiency was observed in the Low Delta band versus High Delta. Females with TRD had higher Delta power than males during the first NREM1 episode, with the most noticeable sex difference observed in Low Delta. In individuals with TRD, Low Delta power correlated with WASO and SE, and High Delta correlated with WASO. Low Delta power deficits in NREM1 were observed in older males with TRD, but not females. These results provide compelling evidence for a link between age, sex, Low Delta power, sleep homeostasis, and non-restorative sleep in TRD.

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.

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.

Comparative evaluation of the sleep quality metrics between a cardboard bed and a camp cot: a randomized controlled crossover study.

Background: Health-beneficial emergency bedding has become increasingly important for dealing with natural disasters such as the anticipated Nankai Trough earthquake in Japan. When the Great East Japan Earthquake occurred, cardboard beds were provided to evacuees. However, there were concerns about lower back pain and sleep disturbances, as cardboard beds offer insufficient pressure distribution. This study aimed to compare the effects of cardboard beds with those of foldable camp cots on sleep quality. Methods: A randomized controlled crossover study involving 20 healthy participants aged 18-45 years was conducted between June 2022 and January 2023. Participants were asked to sleep for one night on a camp cot and for another night on a cardboard bed, with a minimum three-day washout period between the two nights. Body pressure distribution and sleep metrics obtained from polysomnography (PSG) and questionnaires were compared between the two-bed types (P < 0.05). Results: The camp cot exhibited better body pressure distribution than a cardboard bed, leading to improved sleep satisfaction, bedding comfort, and reduced morning sleepiness. Nevertheless, polysomnography revealed no notable differences in sleep metrics or sleep architecture between the two types of beds. Conclusions: Our findings indicate that cardboard beds have lower pressure dispersion capabilities than camp cots, leading to an increased number of position changes during sleep. Additionally, subjective sleep quality, such as alertness on waking, sleep comfort, and sleep satisfaction, was lower for cardboard beds, suggesting that camp cots might offer a more comfortable bedding option for evacuees. However, there were no discernible differences between the two-bed types in terms of objective sleep metrics derived from PSG. The potential for sleep disturbances caused by lower back pain from a hard mattress has been noted, and it is possible that a single night's experience in healthy individuals might not be enough for sleep issues to manifest.

The use of pulse transit time in diagnostics of sleep-disordered breathing in children.

INTRODUCTION: Sleep is the physiological state of the body where proper morphology and duration are indispensable for human functions throughout both, physical and mental spheres. Disordered breathing during sleep impairs its morphology and results in major disorders in any age group. Adverse effects of Obstructive Sleep Apnea Syndrome in children and poor availability of centers offering children's polysomnography call for a reliable and easily accessible screening method. AIM: The aim of the study were to evaluate the usefulness of pulse transit time in the diagnostics of disordered sleep breathing in children and to attempt to employ the parameter in screening tests. Pulse transit time is a physiological parameter determining the time needed for the pulse wave to travel between two measurement points. MATERIAL AND METHODS: Enrolled in the retrospective study were 153 patients (100 boys and 53 girls) suspected of obstructive sleep apnea syndrome who underwent polysomnography at I. Moscicki ENT Hospital in Chorzów. RESULTS: Statistically significant relations between apnea/hypopnea index and pulse transit time were observed in both, individual age groups and all of the patients. Pulse transit time results proved a negative correlation with apnea/hypopnea index values commonly accepted as a parameter concluding the polysomnography procedures. CONCLUSIONS: The results of the study indicate that pulse transit time measurements may find application in screening tests of sleep-disordered breathing in children.

Orofacial myofunctional and polysomnographic characteristics of children with Down syndrome and obstructive sleep apnea: a pilot study. / Características miofuncionais orofaciais e polissonográficas de crianças com Síndrome de Down e apneia obstrutiva do sono: estudo piloto.

PURPOSE: To investigate oropharyngeal structures and functions in a pediatric population with Down Syndrome (DS) and obstructive sleep apnea (OSA) and to correlate with the apnea/hypopnea index (AHI) and sleep questionnaires. METHODS: 12 Children with DS and OSA, between the age of 4 and 12 years old, underwent polysomnography (PSG); sleep questionnaires, Pediatric Sleep Questionnaire (PSQ) and Obstructive Sleep Apnea-18 (OSA-18); and speech-language evaluation using the Short Evaluation of Orofacial Myofunctional Protocol (ShOM). RESULTS: There was a positive correlation between ShoM higher scores and the apnea-hypopnea index (AHI) and between ShoM and the number of hypopneas. The orofacial myofunctional alterations observed in the studied group were: oral breathing, alteration in lip tonus and competence, tongue posture at rest and in swallowing, and occlusal alteration. There was also an increased risk for OSA according to the sleep questionnaires, as well as the presence of obesity and overweight, but without correlation with the severity of OSA. CONCLUSION: All DS children show alterations in orofacial characteristics, higher scores being associated to severe OSA. Orofacial myofunctional evaluation may help to identify different phenotypes in Down syndrome children with Obstructive sleep Apnea, enhancing the need for a multidisciplinary approach.

OBJETIVO: Investigar as estruturas e funções orofaríngeas de uma população pediátrica com Síndrome de Down (SD) e apneia obstrutiva do sono (AOS) e correlacionar com o índice de apneia/hipopneia (IAH) e questionários do sono. MÉTODO: 12 Crianças com SD e AOS, entre 4 e 12 anos, foram submetidas à polissonografia (PSG); questionários do sono, Pediatric Sleep Questionnaire (PSQ) e Obstructive Sleep Apnea-18 (OSA-18); e triagem fonoaudiológica por meio do Short Evaluation of Orofacial Myofunctional Protocol (ShOM). RESULTADOS: Verificou-se uma correlação positiva entre pontuações mais elevadas no ShOM e o índice de apneia hipopneia (IAH) e entre o ShOM e número de hipopneias. As alterações miofuncionais orofaciais observadas no grupo estudado foram: respiração oral, alteração no tônus e competência labial, na postura de língua em repouso e na deglutição e alteração oclusal. Verificou-se também, um risco aumentado para AOS conforme os questionários do sono, bem como presença de obesidade e sobrepeso, mas sem correlação com a gravidade da AOS. CONCLUSÃO: Todas as crianças apresentaram alterações miofuncionais orofaciais, sendo que escores mais altos no ShOM, ou seja, um maior comprometimento miofuncional orofacial, estavam associados à maior gravidade de AOS, sugerindo que a avaliação miofuncional orofacial dentro de uma abordagem multidisciplinar pode auxiliar na identificação de fatores de risco para AOS em crianças com SD.

Role of Sleep-Disordered Breathing and Epicardial Connections in the Recurrence of Atrial Fibrillation.

The presence of epicardial connections (ECs) between the pulmonary veins (PVs) and atrium may contribute to atrial fibrillation (AF) recurrence. This study aimed to determine the impact of sleep-disordered breathing (SDB) on the presence of ECs and the interplay between SDB and ECs on AF recurrence.We retrospectively reviewed 400 consecutive non-valvular AF patients. Among them, 235 patients exhibiting a 3% oxygen desaturation index (ODI) of ≥ 10 events/hour underwent polysomnography to evaluate the SDB severity, measured by the apnea-hypopnea index (AHI). To facilitate the ablation of AF and ECs, a high-density mapping catheter (HDMC) was employed. AF recurrence was evaluated over a 12-month period post-AF ablation.The key findings included: 1) 63% of AF patients with ECs had SDB with an AHI ≥ 20 events/hour. 2) Despite achieving complete PV isolations and precise EC ablation using an HDMC, SDB presence was associated with an increased AF recurrence. 3) Continuous positive airway pressure therapy for SDB improved AF recurrence among the AF patients with both ECs and SDB (57% versus 73%; P = 0.016). 4) AHI (odds ratio [OR] = 1.91, ≥ 28.4 events/hour) and left atrial volume (LAV) (OR = 1.42, ≥ 128.3 mL) were independent predictors of the presence of ECs, and AHI (OR = 1.44, ≥ 27.8 events/hour) was an independent predictor of the presence of AF recurrence.It is essential for physicians to recognise the potential complexity of ECs and SDB in AF patients. Thus, screening and treating SDB in AF patients presenting with ECs might play a pivotal role in suppressing AF recurrence.

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.