Relationship of symptoms with sleep-stage abnormalities in obstructive sleep apnea-hypopnea syndrome

Outcome Reporting in Prospective Studies Evaluating Neurostimulation for Obstructive Sleep Apnea

OBJECTIVE

Due to the controversy surrounding the appropriate outcomes in neurostimulation, we sought to systematically describe ways in which polysomnography and apnea-hypopnea index are reported in prospective studies involving unilateral hypoglossal nerve stimulation.

DATA SOURCES

MEDLINE (Ovid), Embase (Ovid), Cochrane Library, and Scopus.

REVIEW METHODS

Following the Preferred Reporting items of Systematic Reviews and Meta-analysis (PRISMA) Statement guidelines, a systematic two-reviewer system was used for study screening and quality assessment. Articles that met inclusion criteria were included. Quality was evaluated with either the Newcastle-Ottawa Quality Assessment Scale or the Covidence risk-of-bias tool.

RESULTS

Fifteen studies met the inclusion criteria, which included 14 prospective cohort studies and one randomized controlled trial. Titration polysomnography was the primary sleep study used to acquire data in five of the studies compared to only three studies employing exclusively non-titration polysomnography to report outcomes. Three studies compiled data from two or more sleep studies to report a single apnea-hypopnea index. Within the 15 studies, non-titration apnea-hypopnea index was the most reported type (five studies). Titration apnea-hypopnea index was used to report outcomes in one study. Five studies did not specify what type of apnea-hypopnea index was employed to report treatment effectiveness.

CONCLUSION

The reported sleep studies and corresponding apnea-hypopnea indices were highly variable across the studies. Because of the high degree of heterogeneity, future research would benefit from consistent use of a standardized apnea-hypopnea index to report outcomes related to hypoglossal nerve stimulation.

LEVEL OF EVIDENCE

NA Laryngoscope, 2024.

The Potential Effect of Changing Patient Position on Snoring: A Systematic Review

Approximately 45% of adults snore occasionally, and 25% snore regularly, with a higher prevalence in men and an increase among postmenopausal women due to hormonal changes. Snoring is a health concern linked to vascular disease and decreased quality of life for both snorers and their bed partners. Effective snoring treatment, which aims to reduce or eliminate the sound, is challenging and depends on factors like age, comorbidities, disease severity, and anatomical features. This review aims to provide a systematic overview of the current literature on the effects of positional therapy (PT) on snoring. Various devices facilitate PT, including anti-snoring pillows and vibration alarms. PT devices maintain head and neck alignment to keep airways open, while head of bed elevation (HOBE) solutions reduce upper airway collapses by elevating the head and trunk. Studies show that PT and HOBE reduce snoring by increasing airway cross-sectional area and decreasing closing pressure. Despite their benefits, these non-surgical treatments have limitations, such as discomfort in certain sleeping positions and intolerance to prolonged head elevation. While reducing snoring intensity is critical for health reasons, further comparative studies between the different devices are needed to enhance snoring management.

Cannabinoid Use and Obstructive Sleep Apnea: A Retrospective Cohort Study

BACKGROUND

Obstructive sleep apnea (OSA) is a sleep disorder with no widely accepted pharmacological therapy. Cannabinoids have been suggested to reduce OSA severity in small human studies. The purpose of this retrospective cohort study was to explore the association of self-reported cannabis use on OSA severity and sleep parameters in a large cohort of adults undergoing in-laboratory polysomnography.

METHODS

Sleep and medication data were collected for all consecutive adults who completed diagnostic polysomnography at Sunnybrook Health Sciences Centre from 2010 to 2022. Multivariable linear regression models were employed that adjusted for age, sex, and BMI (minimally adjusted model), as well as medication and comorbidity data (maximally adjusted model). An exploratory subgroup analysis was additionally run in patients with moderate to severe OSA.

RESULTS

Of 6,958 individuals (mean age 54.7 ± 16.3, BMI 29.1 ± 6.8, 51.0% female), 71 reported cannabis use. In our minimally adjusted models, cannabis use predicted a reduced respiratory disturbance index (RDI) (β: -4.8 [95% CI: -9.4, -0.2]; p = 0.042); this association became nonsignificant in the fully adjusted models. In an exploratory analysis of patients with moderate to severe OSA (n = 613), cannabis use (n = 7) predicted increased stage N3 sleep (β: 33.5 [95% CI: 15.6, 51.4]; p < 0.001) and decreased REM sleep (β: 16.0 [95% CI: 0.3, 31.7]; p = 0.046).

CONCLUSION

Self-reported cannabis use was not associated with OSA severity after adjusting for confounders. In an exploratory subgroup analysis of patients with moderate to severe OSA, cannabis use impacted sleep architecture. Future studies should further explore these findings.

Somno-Art Software identifies pathology-induced changes in sleep parameters similarly to polysomnography

Polysomnographic sleep architecture parameters are commonly used to diagnose or evaluate treatment of sleep disorders. Polysomnography (PSG) having practical constraints, the development of wearable devices and algorithms to monitor and stage sleep is rising. Beside pure validation studies, it is necessary for a clinician to ensure that the conclusions drawn with a new generation wearable sleep scoring device are consistent to the ones of gold standard PSG, leading to similar interpretation and diagnosis. This paper reports on the performance of Somno-Art Software for the detection of differences in sleep parameters between patients suffering from obstructive sleep apnea (OSA), insomniac or major depressive disorder (MDD) compared to healthy subjects. On 244 subjects (n = 26 healthy, n = 28 OSA, n = 66 insomniacs, n = 124 MDD), sleep staging was obtained from PSG and Somno-Art analysis on synchronized electrocardiogram and actimetry signals. Mixed model analysis of variance was performed for each sleep parameter. Possible differences in sleep parameters were further assessed with Mann-Whitney U-test between the healthy subjects and each pathology group. All sleep parameters, except N1+N2, showed significant differences between the healthy and the pathology group. No significant differences were observed between Somno-Art Software and PSG, except a 3.6±2.2 min overestimation of REM sleep. No significant interaction 'group'*'technology' was observed, suggesting that the differences in pathologies are independent of the technology used. Overall, comparable differences between healthy subjects and pathology groups were observed when using Somno-Art Software or polysomnography. Somno-Art proposes an interesting valid tool as an aid for diagnosis and treatment follow-up in ambulatory settings.

Obstructive sleep apnea and epilepsy: understanding the pathophysiology of the comorbidity

Obstructive sleep apnea (OSA) is a sleep disorder of significant health concern with a high prevalence in the general population. It has been found to exhibit a high incidence of comorbidity with epilepsy, the exact underlying pathophysiology of which still remains poorly understood. OSA is characterized by apnea/hypopnea spells and arousals, leading to intermittent hypoxemia and sleep deprivation. Both sleep deprivation and hypoxemia adversely affect the cortical excitability and favor epileptogenesis and worsening of pre-existing epilepsy, if any. In patients with OSA, deprivation of rapid eye movement sleep (REMS) phase (known for its strong antiepileptic influence) is relatively more than that non rapid eye movement sleep phase leading to postulation of REMS deprivation as a significant factor in the development of epilepsy as a comorbidity in patients with OSA. Furthermore, OSA and epilepsy both have shown to exercise a bidirectional influence on one another and are also likely to exacerbate each other through a positive feedback mechanism. This is especially based on the reports of improved control of epilepsy upon treatment of comorbid OSA. This brief paper attempts to present an underlying pathophysiological basis of the comorbidity of OSA and epilepsy based upon sleep deprivation and hypoxemia that are characteristic features observed in patients with OSA.

The association between sleep architecture, quality of life, and hypertension in patients with obstructive sleep apnea

OBJECTIVE

The aim of this study was to investigate the association between hypertension and overnight polysomnography measures of sleep duration, sleep architecture, and quality of life (QoL) in patients with obstructive sleep apnea (OSA).

METHODS

Participants were patients suspected of having OSA with or without hypertension. All patients underwent overnight polysomnography and completed the Epworth Sleepiness Scale (ESS), Self-Rating Anxiety Scale, Self-Rating Depression Scale, and 12-item Short-Form Health Survey.

RESULTS

Of 128 patients (mean age 46.2 ± 12.5 years), 53% had hypertension. The average total sleep duration was 344 min (standard deviation 90) or 5.7 h and sleep efficiency was < 70%. There was no significant difference between patients with OSA with/without hypertension in total sleep duration, sleep architecture, anxiety, depression, ESS scores, or QoL. In patients with OSA, nocturnal minimum oxygen saturation was significantly negatively correlated with bodily pain and physical component summary (PCS) scores; mean nocturnal saturation was negatively correlated with bodily pain and social function; anxiety showed a significant negative correlation with role emotional; and depression was significantly negatively correlated with physical function, role physical, general health, role emotional, PCS, and mental component summary (MCS) scores. In the group with OSA and hypertension, N3 duration was negatively correlated with social function, mental health, and MCS scores. Anxiety was significantly negatively correlated with physical function, role physical, vitality, mental health, role emotional, PCS, and MCS scores. Depression was significantly negatively correlated with physical function, role physical, vitality, mental health, role emotional, PCS, and MCS scores. In patients with mild, moderate, and severe OSA, QoL was associated with depression. In mild OSA, PCS was correlated with ESS and anxiety. In moderate OSA, MCS was correlated with apnea-hypopnea index scores. In severe OSA, MCS and PCS were correlated with anxiety.

CONCLUSIONS

There were no significant associations between the presence of hypertension and total sleep duration, sleep architecture, or QoL in patients with OSA. However, hypertension may affect the influencing factors of QoL in patients with OSA. Further cohort studies are needed to confirm these findings.

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls

Introduction

Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort.

Methods

A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers.

Results

The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75).

Conclusion

The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

Background

OSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.

Method

We collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.

Results

There are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ² = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.

Conclusion

The prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.

Clinical and Polysomnographic Features Associated with Poor Sleep Quality in Patients with Obstructive Sleep Apnea

Background and Objectives: Poor sleep quality in patients with obstructive sleep apnea (OSA) may be associated with different clinical and polysomnographic features. The aim of this study was to identify features associated with poor sleep quality in OSA patients. Materials and Methods: This was a cross-sectional study enrolling patients with OSA confirmed by polysomnography (PSG). In addition to gathering clinical data, patients were assessed using the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Clinical Global Impression Scale. Univariate and multivariable analyses were performed to identify factors associated with an increased risk of poor sleep quality in this population. Results: Among 505 enrolled patients (mean age of 57.1 years, 69.7% male) poor quality of sleep (PSQI score ≥ 5) was confirmed in 68.9% of them. Multivariable analysis revealed the following factors associated with poor sleep quality: chronic heart failure (OR 3.111; 95% CI, 1.083−8.941, p = 0.035), male sex (OR 0.396; 95% CI, 0.199−0.787, p = 0.008), total ESS score (OR 1.193; 95% CI, 1.124−1.266, p < 0.001), minimal saturation during sleep (OR 1.034; 95% CI, 1.002−1.066, p = 0.036), and N3 percentage of total sleep time (OR 1.110; 95% CI, 1.027−1.200, p = 0.009). Conclusions: Our study suggests that both the female sex and coexistence of heart failure are independent risk factors for poor sleep quality. Moreover, we hypothesize that nocturnal hypoxia may lead to a misperception of sleep quality and may explain the counterintuitive association between a higher proportion of deep sleep and poor sleep quality.

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.

Associations of ambient air pollution with overnight changes in body composition and sleep-related parameters

This study aims to investigate the association of air pollution with overnight change in 4body composition and sleep-related parameters. Body composition of 197 subjects in New Taipei city was measured before and after sleep by bioelectric impedance analysis. Air pollutant data were collected from Taiwan Environmental Protection Administration. Sleep parameters were examined by polysomnography. We observed fine particulate matter (PM_2.5) decreased arterial oxygen saturation (SaO₂) and increased apnea-hypopnea index (AHI); NO₂ increased arousal, AHI, and decreased mean SaO₂; and O₃ inmcreased mean SaO₂. We observed 0.99-μg/m³ increase in PM_2.5 was associated with 18.8% increase in changes of right arm fat percentage (95% confidence interval (CI): 0.004, 0.375) and 0.011-kg increase in changes of right arm fat mass (95% CI: 0.000, 0.021). 2.45-ppb increase in NO₂ was associated with 0.181-kg decrease in changes of muscle mass (95% CI: -0.147, -0.001), 0.192-kg decrease in changes of fat free mass (95% CI: -0.155, -0.001), 21.1% increase in changes of right leg fat percentage (95% CI: 0.012, 0.160), and 21.3% increase in changes of left leg fat percentage (95% CI: 0.006, 0.168). 1.56-ppb increase in O₃ was associated with 29.3% decrease in changes of right leg fat percentage (95% CI: -0.363, -0.013), 0.058-kg increase in changes of right leg fat free mass (95% CI: 0.008, 0.066), and 0.059-kg increase in changes of right leg muscle mass (95% CI: 0.010, 0.066). We observed AHI was associated with overnight changes in fat percentage, total fat mass, muscle mass, bone mass, fat free mass, extracellular water, basal metabolic rate, leg fat percentage, leg fat mass, and trunk fat percentage (p < 0.05). In conclusion, exposure to air pollutants was associated with overnight body composition changes and sleep-related parameters. Nocturnal changes in total muscle mass and leg fat percentage likely contribute to the relationship between air pollution and obstructive sleep apnea.

Lesioning of the pedunculopontine nucleus reduces rapid eye movement sleep, but does not alter cardiorespiratory activities during sleep, under hypoxic conditions in rats

To determine how partial lesioning of the pedunculopontine nucleus (PPT) affects sleep, breathing, and blood pressure in rats, ibotenic acid (IBO) was injected bilaterally into the PPT. Sham-injected (saline) and IBO-lesioned rats were first studied under normoxic conditions (40 recordings were obtained from 15 rats, with each recording lasting for 6 daytime hours). Rats were then exposed to intermittent hypoxia for 4 ± 2 days (51 recordings from 12 rats, each lasting 6 daytime hours). The intermittent hypoxia protocol involved an oxygen decline lasting 35 s (to a nadir of 10 %) followed by a 50 s increase to normoxia. The IBO caused an estimated 53 % reduction in PPT neurons. When normoxic, IBO-lesioned rats had remarkedly normal sleep architecture, respiratory rates, and mean arterial pressure. The exposure to intermittent hypoxia evoked tachypnea in both the IBO-lesioned and sham-injected rats. When intermittently hypoxic, IBO-lesioned rats demonstrated a significant reduction in the duration of rapid eye movement (REM) sleep. We conclude that partial lesions of the PPT do not disrupt cardiorespiratory activities, but a reduction in PPT neurons impairs the ability to sustain REM sleep under hypoxic conditions.

Drug-Resistant Epilepsy and Obstructive Sleep Apnea: Exploring a Link Between the Two

Drug-resistant epilepsy accounts for approximately one third of all epilepsy cases; yet its exact etiopathogenesis still remains under intense exploration. Several factors have been advocated for predicting drug resistance in patients with epilepsy. Obstructive sleep apnea (OSA) is a commonly prevalent sleep disorder that exhibits a bidirectional and strong comorbidity with epilepsy. The exact pathophysiology of this comorbidity is not yet clearly explained. This study analyzes the relationship between drug-resistant epilepsy and OSA, and the findings indicate a strong role of rapid eye movement sleep (REMS) in the pathogenesis of this relationship. It also emerges from the study that REMS reduction is a prominent feature of OSA, and drug resistance in patients with epilepsy and treatment of OSA has been shown to restore REMS in several studies with concomitant improvement in seizure control.

Domiciliary use of transcutaneous electrical stimulation for patients with obstructive sleep apnoea: a conceptual framework for the TESLA home programme

Obstructive sleep apnoea (OSA) is a global health problem of increasing prevalence. Effective treatments are available with continuous positive airway pressure (CPAP) therapy and mandibular advancement devices (MAD). However, there is limited long-term adherence to therapy, as CPAP and MAD require permanent usage to avoid recurrence of the symptoms and adverse ill health. Alternative treatments would aid in the treatment cascade to manage OSA effectively whenever standard therapy has been trialled and failed. Hypoglossal nerve stimulation (HNS), an invasive approach to stimulate the pharyngeal dilator muscles of the upper airway during sleep, has been approved for the treatment of OSA by several healthcare systems in recent years. In parallel to the development of HNS, a non-invasive approach has been developed to deliver electrical stimulation. Transcutaneous electrical stimulation in obstructive sleep apnoea (TESLA) uses non-invasive electrical stimulation to increase neuromuscular tone of the upper airway dilator muscles of patients with OSA during sleep. Data from previous feasibility studies and randomised controlled trials have helped to identify a subgroup of patients who are "responders" to this treatment. However, further investigations are required to assess usability, functionality and task accomplishment of this novel treatment. Consideration of these factors in the study design of future clinical trials will strengthen research methodology and protocols, improve patient related outcome measures and assessments, to optimise this emerging therapeutical option. In this review, we will introduce a conceptual framework for the TESLA home programme highlighting qualitative aspects and outcomes.

The Asia-Pacific Clinical Practice Guidelines for the Management of Frailty

OBJECTIVE

To develop Clinical Practice Guidelines for the screening, assessment and management of the geriatric condition of frailty.

METHODS

An adapted Grading of Recommendations, Assessment, Development, and Evaluation approach was used to develop the guidelines. This process involved detailed evaluation of the current scientific evidence paired with expert panel interpretation. Three categories of Clinical Practice Guidelines recommendations were developed: strong, conditional, and no recommendation.

RECOMMENDATIONS

Strong recommendations were (1) use a validated measurement tool to identify frailty; (2) prescribe physical activity with a resistance training component; and (3) address polypharmacy by reducing or deprescribing any inappropriate/superfluous medications. Conditional recommendations were (1) screen for, and address modifiable causes of fatigue; (2) for persons exhibiting unintentional weight loss, screen for reversible causes and consider food fortification and protein/caloric supplementation; and (3) prescribe vitamin D for individuals deficient in vitamin D. No recommendation was given regarding the provision of a patient support and education plan.

CONCLUSIONS

The recommendations provided herein are intended for use by healthcare providers in their management of older adults with frailty in the Asia Pacific region. It is proposed that regional guideline support committees be formed to help provide regular updates to these evidence-based guidelines.