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Brain and Behavior Jun 2024Rapid eye movement (REM)-dependent obstructive sleep apnea syndrome (OSAS) is a specific subtype of OSAS having some phenotypic characteristics like a preference for a...
OBJECTIVE
Rapid eye movement (REM)-dependent obstructive sleep apnea syndrome (OSAS) is a specific subtype of OSAS having some phenotypic characteristics like a preference for a younger age, female gender, and milder severity. Such favorable features could make it possible to consider an overall benign course for this phenotype. However, accumulating data introduced its association with several cardiometabolic and vascular disorders recently. The primary objective of this study was to address the disease from the inflammation perspective and evaluate the potential inflammatory status in this variant via two accessible blood parameters: platelet distribution width (PDW) and systemic immune-inflammation index (SII). The secondary aim was to investigate whether this status, together with other disease characteristics, demonstrates consistency under different definitions of REM-dependent OSAS published previously.
PATIENTS AND METHODS
The medical records of 35 patients with mild-to-moderate REM-dependent OSAS, 35 age- and sex-matched patients with REM-independent OSAS, and 25 non-OSA controls were retrospectively analyzed. Baseline features, polysomnographic characteristics, PDW, and SII were compared between the groups. Secondly, the analyses were repeated using different definitions of REM-dependent OSAS. Bivariate analyses were performed, and a multiple stepwise regression model was applied to adjust for body mass index (BMI) and cardiovascular risk (CVR) factors. RESULTS: Mean PDW and SII were increased in patients with REM-dependent OSAS as compared to non-OSA controls (p = .022 and .029). The significance remained stable after adjustment for BMI and CVRs and was consistent according to different definitions. The Comparison of patients with REM-independent OSAS and non-OSA controls, as well as the two different subtypes of OSAS, did not yield significance.
CONCLUSION
Based on the current findings, patients with REM-dependent OSAS appear to be susceptible to inflammation and should be carefully monitored for the negative consequences of that issue. To our knowledge, this study is the first to evaluate SII and PDW in REM-dependent OSAS.
Topics: Humans; Sleep Apnea, Obstructive; Male; Female; Middle Aged; Inflammation; Adult; Retrospective Studies; Sleep, REM; Polysomnography; Aged; Body Mass Index
PubMed: 38844423
DOI: 10.1002/brb3.3546 -
Frontiers in Psychiatry 2024Causal relationships between psychopathological symptoms, personality traits, coping mechanisms, and sleep bruxism (SB) were studied in the past, giving inconsistent...
Why am I grinding and clenching? Exploration of personality traits, coping strategies, oral parafunctional behaviors, and severe sleep bruxism in a polysomnographic study.
INTRODUCTION
Causal relationships between psychopathological symptoms, personality traits, coping mechanisms, and sleep bruxism (SB) were studied in the past, giving inconsistent results mostly based on self-assessment evaluations. This polysomnography-based cross-sectional study aimed to explore the relationships between severe SB, personality traits (according to the Big Five model), and coping strategies with objective polysomnographic verification.
METHODOLOGY
The study included 66 participants divided into severe SB (SSB) (n=32) and no or mild SB (n=34) groups based on video-polysomnography performed in the sleep laboratory. Questionnaire assessment included the use of the Beck Depression Inventory, Beck Anxiety Inventory, Mini-COPE, International Personality Item Pool Big Five Markers 20-Item version, and Oral Behavior Checklist.
RESULTS
Participants with SSB presented with fewer self-reported anxiety (p=0.008) and depressive (p=0.01) symptoms than the non- or mild-SB groups. The SSB group scored significantly higher in Big Five personal traits such as extraversion (p=0.007), emotional stability (p=0.013), and intellect (p=0.004), while regarding coping strategies, the SSB group was less likely to use negative strategies: self-distraction (p=0.036), denial (p=0.006), venting (p=0.03), behavioral disengagement (p=0.046), and self-blame (p=0.003), and turning to religion (p=0.041). The intensity of oral parafunctional behaviors was comparable in both groups (p=0.054). Emotional stability was a moderate protective factor (p=0.004), and the self-blame strategy was a strong risk factor (p<0.001) for increased oral parafunctional behavior intensity. Phasic activity negatively correlated with anxiety symptom severity (p=0.005), whereas tonic (p=0.122) and mixed (p=0.053) phenotypes did not. SB intensity was a protective factor against anxiety symptoms (p=0.016).
CONCLUSION
In terms of psychopathology, severe sleep bruxers tend to present less severe anxiety and depressive symptoms, while some of their personality traits (extraversion, emotional stability, and intellect) were more strongly pronounced. SSB is possibly related to the lesser use of the "maladaptive" coping strategies and there were no specific coping strategies preferred by SSB participants, compared to the other group. These observations require further studies, as it should be determined whether SB (especially phasic activity) might be a form of a somatization/functional disorder. Further research should focus on the psychogenic background of oral parafunctional behaviors, which occur more often in less emotionally stable personalities and in people using self-blame coping strategies.
PubMed: 38840944
DOI: 10.3389/fpsyt.2024.1362429 -
Australian Journal of General Practice Jun 2024Obstructive sleep apnoea (OSA) is a highly prevalent condition associated with significant adverse health consequences affecting multiple organ systems. As the first...
BACKGROUND
Obstructive sleep apnoea (OSA) is a highly prevalent condition associated with significant adverse health consequences affecting multiple organ systems. As the first point of contact for most patients with OSA, general practitioners (GPs) have an important role in the diagnosis of this common sleep disorder.
OBJECTIVE
The aim of this paper is to improve awareness of common risk factors for and clinical presentation of OSA in primary care to improve patient health outcomes. We seek to understand how screening tools, such as the OSA50 questionnaire and the Epworth Sleepiness Scale, can help GPs identify patients who are at high risk for OSA with significant daytime sleepiness.
DISCUSSION
Patients at high risk of symptomatic moderate-severe OSA should proceed to further investigation with sleep study testing. Referral to a sleep physician should be considered for patients with complex presentations or other suspected sleep disorders, commercial drivers, and those who fail to comply with or respond to initial OSA treatments.
Topics: Humans; Sleep Apnea, Obstructive; Primary Health Care; Risk Factors; Surveys and Questionnaires; Polysomnography
PubMed: 38840373
DOI: 10.31128/AJGP-03-23-6740 -
BMC Health Services Research Jun 2024Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to...
BACKGROUND
Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS.
MATERIALS AND METHODS
Clinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938.
CONCLUSION
We established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options.
Topics: Humans; Machine Learning; Sleep Apnea, Obstructive; Male; Female; Middle Aged; Adult; Retrospective Studies; Risk Factors; Risk Assessment; Polysomnography
PubMed: 38840121
DOI: 10.1186/s12913-024-11081-1 -
Clinical Case Reports Jun 2024This study suggests that severe obstructive sleep apnea can present as sleep-related epileptic or non-epileptic seizures. A detailed history and physical examination,...
KEY CLINICAL MESSAGE
This study suggests that severe obstructive sleep apnea can present as sleep-related epileptic or non-epileptic seizures. A detailed history and physical examination, along with polysomnography and video electroencephalography findings can lead to the correct diagnosis.
ABSTRACT
Obstructive sleep apnea (OSA) is defined by recurrent episodes of the upper airway complete or partial collapse while sleeping. The obstructive episodes result in gradual suffocation that increases breathing attempts till the person is awakened. The main manifestations are excessive daytime sleepiness, snoring, observed episodes of stopped breathing, and abrupt awakenings accompanied by gasping or choking. Nevertheless, there are very few reports of patients with OSA, manifesting other symptoms such as seizure-like movements. Differentiating OSA with nocturnal seizures could be challenging due to their overlapping features. A 53-year-old man presented to the clinic, experiencing seizure-like involuntary movements during nocturnal sleep for the past 2 years with a frequency of 2-3 times per night. Neurologic examinations were normal. Further evaluation with polysomnography revealed impaired arousal followed by seizure-like movements during sleep. Video electroencephalography (EEG) did not show any epileptiform discharges, ruling out the nocturnal seizure diagnosis. The patient was diagnosed with OSA. Subsequently, continuous positive airway pressure (CPAP) treatment resolved all symptoms.
PubMed: 38836112
DOI: 10.1002/ccr3.9004 -
Translational Psychiatry Jun 2024The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in... (Randomized Controlled Trial)
Randomized Controlled Trial
The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in arousal-related sleep metrics between 36 individuals with treatment-resistant major depression (TRD) and 25 healthy volunteers (HVs). It also sought to determine whether ketamine normalizes arousal in individuals with TRD and whether ketamine's effects on arousal mediate its antidepressant and anti-SI effects. This was a secondary analysis of a biomarker-focused, randomized, double-blind, crossover trial of ketamine (0.5 mg/kg) compared to saline placebo. Polysomnography (PSG) studies were conducted one day before and one day after ketamine/placebo infusions. Sleep arousal was measured using spectral power functions over time including alpha (quiet wakefulness), beta (alert wakefulness), and delta (deep sleep) power, as well as macroarchitecture variables, including wakefulness after sleep onset (WASO), total sleep time (TST), rapid eye movement (REM) latency, and Post-Sleep Onset Sleep Efficiency (PSOSE). At baseline, diagnostic differences in sleep macroarchitecture included lower TST (p = 0.006) and shorter REM latency (p = 0.04) in the TRD versus HV group. Ketamine's temporal dynamic effects (relative to placebo) in TRD included increased delta power earlier in the night and increased alpha and delta power later in the night. However, there were no significant diagnostic differences in temporal patterns of alpha, beta, or delta power, no ketamine effects on sleep macroarchitecture arousal metrics, and no mediation effects of sleep variables on ketamine's antidepressant or anti-SI effects. These results highlight the role of sleep-related variables as part of the systemic neurobiological changes initiated after ketamine administration. Clinical Trials Identifier: NCT00088699.
Topics: Humans; Ketamine; Male; Depressive Disorder, Treatment-Resistant; Female; Adult; Double-Blind Method; Cross-Over Studies; Polysomnography; Arousal; Middle Aged; Sleep; Depressive Disorder, Major; Wakefulness; Suicidal Ideation; Antidepressive Agents; Young Adult
PubMed: 38834540
DOI: 10.1038/s41398-024-02956-2 -
Psychiatry Research Aug 2024Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general...
Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general population-based cohort is not well known. This study investigated associations between both polysomnography (PSG)-based or subjective sleep features and incident MDD. Participants representative of the general population who had never had MDD completed sleep questionnaires (n = 2000) and/or underwent PSG (n = 717). Over 8 years' follow-up, participants completed psychiatric interviews enabling the diagnosis of MDD. Survival Cox models were used to analyze associations between sleep features and MDD incidence. A higher Epworth Sleepiness Scale and presence of insomnia symptoms were significantly associated with a higher incidence of MDD (hazard ratio [HR] [95 % confidence interval (CI)]: 1.062 [1.022-1.103], p = 0.002 and 1.437 [1.064-1.940], p = 0.018, respectively). Higher density of rapid eye movements in rapid eye movement (REM) sleep was associated with a higher incidence of MDD in men (HR 1.270 [95 % CI 1.064-1.516], p = 0.008). In women, higher delta power spectral density was associated with a lower MDD incidence (HR 0.674 [95 % CI 0.463-0.981], p = 0.039). This study confirmed the associations between subjective and objective sleep features and the incidence of MDD in a large community dwelling cohort.
Topics: Humans; Male; Depressive Disorder, Major; Female; Adult; Middle Aged; Incidence; Sleep Wake Disorders; Polysomnography; Cohort Studies; Sleep Initiation and Maintenance Disorders; Proportional Hazards Models; Surveys and Questionnaires; Risk Factors
PubMed: 38833937
DOI: 10.1016/j.psychres.2024.115934 -
Nature and Science of Sleep 2024Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous...
PURPOSE
Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy.
PATIENTS AND METHODS
Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed.
RESULTS
Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, =0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm, <0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, =0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], =0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm, <0.001) and better response to treatment (41.2% and 82.6%, =0.004).
CONCLUSION
Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.
PubMed: 38831958
DOI: 10.2147/NSS.S455705 -
Nature and Science of Sleep 2024This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in...
PURPOSE
This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses.
PATIENTS AND METHODS
A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician.
RESULTS
U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement.
CONCLUSION
Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings.
PubMed: 38827394
DOI: 10.2147/NSS.S455649 -
AMIA Joint Summits on Translational... 2024Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the...
Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Recently, there have been numerous studies demonstrating the application of artificial intelligence to detect apnea in real time. But the majority of these studies apply data pre-processing and feature extraction techniques resulting in a longer inference time that makes the real-time detection system inefficient. This study proposes a single convolutional neural network architecture that can automatically extract spatial features and detect apnea from both electrocardiogram (ECG) and blood-oxygen saturation (SpO) signals. Using segments of 10s, the network classified apnea with an accuracy of 94.2% and 96% for ECG and SpO respectively. Moreover, the overall performance of both models was consistent with an AUC score of 0.99.
PubMed: 38827094
DOI: No ID Found