Prediction in obstructive sleep apnoea: diagnosis, comorbidity risk, and treatment outcomes

Morphometric measures and desaturations: Proposal for an index with improved accuracy for obstructive sleep apnea screening

STUDY OBJECTIVE

To evaluate the sensitivity and specificity of the combined Kushida morphometric model (KMM) and the oxygen desaturation index (ODI) for screening individuals with obstructive sleep apnea.

METHODS

Diagnostic test study with adults >18 years, both sexes, polysomnography, body mass index, neck circumference and intraoral measurements.

RESULTS

144 patients were invited; of these, 75 met the exclusion criteria. 55 individuals presented AHI ≥5 ev/h and 14, an AHI <5 ev/h. Three AHI cut-off points were evaluated: AHI ≥5, ≥15, ≥30 ev/h. When adopting the cut-off point of AHI ≥5 ev/h, the KMM showed sensitivity (SE) = 60.0 %, specificity (SP) = 71.4 % and 95 % confidence interval of the area under the curve (95 % CI of AUC) = 0.655; the combination of KMM and ODI (KMM + ODI) revealed SE = 73.0 %, SP = 71.4 % (95 % CI of AUC = 0.779) and the ODI showed SE = 76.4 % and SP = 92.9 % (95 % CI of AUC = 0.815). At the cut-off point of AHI ≥15 ev/h, the KMM presented SE = 64.1 %, SP = 76.7 % (95 % CI of AUC = 0.735); the KMM + ODI showed SE = 82.1 %, SP = 83.3 % (95 % CI of AUC = 0.895); and the ODI presented SE = 76.9 %, SP = 100.0 % (95 % CI of AUC = 0.903). For the cut-off point of AHI ≥30 ev/h, the KMM showed SE = 56.0 %, SP = 77.2 % (95 % CI of AUC = 0.722); the KMM + ODI revealed SE = 92.0 %, SP = 79.5 % (95 % CI of AUC = 0.926); and the ODI showed SE = 92.0 %, SP = 90.9 % (95 % CI of AUC = 0.941).

CONCLUSION

The combination of oxygen desaturation index and Kushida morphometric model improved the sensitivity and specificity of this model regardless of obstructive sleep apnea severity suggesting greater effectiveness in risk prediction.

Frequency of Sleep Disorders in Rheumatoid Arthritis Patients

BACKGROUND

The quality of life of people with rheumatoid arthritis (RA), a chronic inflammatory illness, is greatly impacted by sleep disturbances that are often experienced in conjunction with the condition.

OBJECTIVE

The purpose of this research was to ascertain the prevalence of sleep problems in RA patients as well as their relationship to patient demographics and disease severity.

METHODOLOGY

A cross-sectional study was conducted from January to December 2023. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were used to evaluate sleep disturbances and structured questionnaires were used to gather data. Demographic characteristics and the severity of the RA condition were also assessed. using a significance threshold set at p<0.05, Chi-square tests were used to evaluate the relationship between sleep problems and the severity of RA illness in the statistical studies carried out using IBM SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, NY, USA).

RESULTS

A total of 385 RA patients were included in the study. Among them, 38.96% (n=150) reported insomnia, 18.18% (n=70) suffered from sleep apnea, and 15.58% (n=60) experienced restless legs syndrome. Poor sleep quality was prevalent in 75.32% (n=290) of participants, as assessed by the PSQI. Notably, a significant association was found between insomnia and moderate disease activity (n=55; p<0.001), while sleep apnea was most frequently observed in patients with low disease activity (n=27; p=0.003). Additionally, restless legs syndrome was predominantly seen in those with moderate disease activity (n=26; p=0.002), highlighting the complex relationship between RA severity and sleep disturbances.

CONCLUSION

The high prevalence of sleep disorders in RA patients, particularly insomnia and sleep apnea, is significantly associated with disease severity, highlighting the need for targeted management strategies.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals

Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician's workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.

Focus of dental sleep medicine on obstructive sleep apnea in older adults: A narrative review

PURPOSE

To review dental sleep medicine in older adults based on the literature.

STUDY SELECTION

This narrative review focuses on sleep physiology, common sleep disorders, and obstructive sleep apnea (OSA) in older adults and their management.

RESULTS

Sleep physiology differs between older and younger adults, with sleep disturbances occurring more frequently in older adults. The prevalence of insomnia increases in older adults due to age-related changes in sleep physiology. Insomnia, sleep-disordered breathing, periodic limb movement disorder, restless legs syndrome, and rapid eye movement (REM) sleep behavior disorder are common sleep disorders in older adults. OSA is more prevalent in older adults, and its effects on them are considered more substantial than those on younger adults. The treatment of older patients with mandibular advancement devices may be less effective and more complex owing to potential impairments in oral and dental health. Furthermore, the prevalence of edentulism in older adults is decreasing while life expectancy is increasing.

CONCLUSIONS

As older adults have comorbidities that affect sleep quality, dentists should consider the effects of sleep physiology and sleep disorders in these patients. OSA may decrease the quality of life and increase the risk of developing other diseases. Therefore, dentists proposing treatment with mandibular advancement devices need to inform patients of their potential lack of efficacy and the requirement for careful follow-up owing to known and unknown side effects.

Upper airway morphology in adults with positional obstructive sleep apnea

PURPOSE

To compare the anatomical balance and shape of the upper airway in the supine position between adults with positional obstructive sleep apnea (POSA) and adults with non-positional OSA (NPOSA).

METHODS

Adults diagnosed with OSA (apnea-hypopnea index (AHI) > 10 events/h) were assessed for eligibility. POSA was defined as the supine AHI more than twice the AHI in non-supine positions; otherwise, patients were classified as NPOSA. Cone beam computed tomography (CBCT) imaging was performed for every participant while awake in the supine position. The anatomical balance was calculated as the ratio of the tongue size to the maxillomandibular enclosure size. The upper airway shape was calculated as the ratio of the anteroposterior dimension to the lateral dimension at the location of the minimal cross-sectional area of the upper airway (CSAmin-shape).

RESULTS

Of 47 participants (28 males, median age [interquartile range] 56 [46 to 63] years, median AHI 27.8 [15.0 to 33.8]), 34 participants were classified as having POSA (72%). The POSA group tended to have a higher proportion of males and a lower AHI than the NPOSA group (P = 0.07 and 0.07, respectively). After controlling for both sex and AHI, the anatomical balance and CSAmin-shape were not significantly different between both groups (P = 0.18 and 0.73, respectively).

CONCLUSION

Adults with POSA and adults with NPOSA have similar anatomical balance and shape of their upper airway in the supine position.

TRIAL REGISTRATION

This study was registered with the Australian New Zealand Clinical Trials Registry (ANZCTR Trial ACTRN12611000409976).

Pharmacological inhibition of SK-channels with AP14145 prevents atrial arrhythmogenic changes in a porcine model for obstructive respiratory events

BACKGROUND

Obstructive sleep apnea (OSA) creates a complex substrate for atrial fibrillation (AF), which is refractory to many clinically available pharmacological interventions. We investigated atrial antiarrhythmogenic properties and ventricular electrophysiological safety of small-conductance Ca²⁺ -activated K⁺ (SK)-channel inhibition in a porcine model for obstructive respiratory events.

METHODS

In spontaneously breathing pigs, obstructive respiratory events were simulated by intermittent negative upper airway pressure (INAP) applied via a pressure device connected to the intubation tube. INAP was applied for 75 s, every 10 min, three times before and three times during infusion of the SK-channel inhibitor AP14145. Atrial effective refractory periods (AERP) were acquired before (pre-INAP), during (INAP) and after (post-) INAP. AF-inducibility was determined by a S1S2 atrial pacing protocol. Ventricular arrhythmicity was evaluated by heart rate adjusted QT-interval duration (QT-paced) and electromechanical window (EMW) shortening.

RESULTS

During vehicle infusion, INAP transiently shortened AERP (pre-INAP: 135 ± 10 ms vs. post-INAP 101 ± 11 ms; p = .008) and increased AF-inducibility. QT-paced prolonged during INAP (pre-INAP 270 ± 7 ms vs. INAP 275 ± 7 ms; p = .04) and EMW shortened progressively throughout INAP and post-INAP (pre-INAP 80 ± 4 ms; INAP 59 ± 6 ms, post-INAP 46 ± 10 ms). AP14145 prolonged baseline AERP, partially prevented INAP-induced AERP-shortening and reduced AF-susceptibility. AP14145 did not alter QT-paced at baseline (pre-AP14145 270 ± 7 ms vs. AP14145 268 ± 6 ms, p = .83) or QT-paced and EMW-shortening during INAP.

CONCLUSION

In a pig model for obstructive respiratory events, the SK-channel-inhibitor AP14145 prevented INAP-associated AERP-shortening and AF-susceptibility without impairing ventricular electrophysiology. Whether SK-channels represent a target for OSA-related AF in humans warrants further study.

Pediatric obstructive sleep apnea-Dental professionals can play a crucial role

The significant contribution of dental professionals to the management of selected adult obstructive sleep apnea (OSA) cases is understood. Among children, it has also been suggested that dental professionals may also help screen and manage this morbidity in selected cases. It has also been noted that our understanding of pediatric OSA lags significantly behind adult OSA. During the screening process for potential pediatric OSA cases, dental professionals may be quite helpful as specific craniofacial abnormalities have been previously associated with pediatric OSA, including Class II malocclusion, vertical facial growth and maxillary transversal deficiency. As dental professionals assess children more frequently than physicians, they can help screen sleep-disordered breathing signs and symptoms using validated questionnaires. In more advanced cases, orthodontists may be leading contributors to the management of selected cases where a craniofacial involvement is suspected. Rapid maxillary expansion and mandibular or maxillary anterior repositioning devices have been proposed as managing alternatives. So far, there is no substantial evidence if these approaches can be adopted to treat OSA fully or if the reported OSA signs and symptoms improvements observed in a selected group of patients are stable long-term. Nevertheless, dentists and orthodontists' integration into a transdisciplinary team should be encouraged to play a significant role. This review discusses dentists or orthodontists' potential contribution to screen and manage selective pediatric OSA patients as part of a transdisciplinary team.

Investigation into the association between obstructive sleep apnea and incidence of all-type cancers: a systematic review and meta-analysis

Obstructive sleep apnea (OSA) is one of the most common sleep-related breathing disorders and is featured by complete or partial obstruction of the upper airway using sleep. Conflicting reports regarding the association between obstructive sleep apnea (OSA) and cancer incidence are existing in different studies. The aim of this study is to determine whether OSA is independently associated with incidence of all-type cancers by using the meta-analysis. Medline, Embase, PubMed, Ovid, the Cochrane Library database, Web of Science, and Google Scholar were searched by two independent reviewers until 31 January 2021. Studies that evaluated OSA and the cancer incidence were included. Pooled risk ratios (RR) and corresponding 95% confidence intervals (CI) were calculated. Twelve studies, involved 184,915 participants, were pooled in this meta-analysis. Fixed-effects model analysis showed that patients with OSA had an increased risk of cancer incidence (RR: 1.52, 95% CI: 1.39-1.66, P < 0.001). The subgroup analysis showed that the pooled RRs of cancer incidence were 1.14 (95% CI: 1.04-1.25, P = 0.006) for mild OSA, 1.36 (95% CI: 1.32-1.92; P < 0.001) for moderate OSA and 1.59 (95% CI: 1.45-1.74; P < 0.001) for severe OSA, respectively. Patients with moderate and severe OSA were identified to have an increased risk of cancer incidence when compared to patients with mild OSA. In addition, patients with severe OSA also showed an increased risk of incident cancer (RR: 1.18, 95% CI: 1.08-1.28, P < 0.001) when compared to patients with moderate OSA. In conclusion, from most updated literatures, our meta-analysis results indicated that OSA was independently associated with incidence of all-type cancers when stratified the severity of OSA. However, further detailed analysis and clinical studies are warranted to decipher the association between OSA and cancer prevalence.

Defining the Heterogeneity of Sleep Apnea Syndrome: A Cluster Analysis With Implications for Patient Management

INTRODUCTION

Obstructive sleep apnea (OSA) is a complex pathology with heterogeneity that has not been fully characterized to date. Our objective is to identify groups of patients with common clinical characteristics through cluster analysis that could predict patient prognosis, the impact of comorbidities and/or the response to a common treatment.

METHODS

Cluster analysis was performed using the hierarchical cluster method in 2025 patients in the apnea-HUGU cohort. The variables used for building the clusters included general data, comorbidity, sleep symptoms, anthropometric data, physical exam and sleep study results.

RESULTS

Four clusters were identified: (1) young male without comorbidity with moderate apnea and otorhinolaryngological malformations; (2) middle-aged male with very severe OSA with comorbidity without cardiovascular disease; (3) female with mood disorder; and (4) symptomatic male with established cardiovascular disease and severe OSA.

CONCLUSIONS

The characterization of these four clusters in OSA can be decisive when identifying groups of patients who share a special risk or common therapeutic strategies, orienting us toward personalized medicine and facilitating the design of future clinical trials.

Educational, supportive and behavioural interventions to improve usage of continuous positive airway pressure machines in adults with obstructive sleep apnoea

BACKGROUND

Although highly effective in the treatment of obstructive sleep apnoea (OSA), continuous positive airway pressure (CPAP) is not universally accepted by users. Educational, supportive and behavioural interventions may help people with OSA initiate and maintain regular and continued use of CPAP.

OBJECTIVES

To assess the effectiveness of educational, supportive, behavioural, or mixed (combination of two or more intervention types) strategies that aim to encourage adults who have been prescribed CPAP to use their devices.

SEARCH METHODS

Searches were conducted on the Cochrane Airways Group Specialised Register of trials. Searches are current to 29 April 2019.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) that assessed intervention(s) designed to inform participants about CPAP/OSA, to support them in using CPAP, or to modify their behaviour to increase use of CPAP devices.

DATA COLLECTION AND ANALYSIS

We assessed studies to determine their suitability for inclusion in the review. Data were extracted independently and were entered into RevMan for analysis. 'Risk of bias' assessments were performed, using the updated 'Risk of bias 2' tool, for the primary outcome, CPAP usage. Study-level 'Risk of bias' assessments were performed using the original 'Risk of bias' tool. GRADE assessment was performed using GRADEpro.

MAIN RESULTS

Forty-one studies (9005 participants) are included in this review; 16 of these studies are newly identified with updated searches. Baseline Epworth Sleepiness Scale (ESS) scores indicate that most participants suffered from excessive daytime sleepiness. The majority of recruited participants had not used CPAP previously. When examining risk of bias for the primary outcome of hourly machine usage/night, 58.3% studies have high overall risk (24/41 studies), 39.0% have some concerns (16/41 studies), and 2.4% have low overall risk (1/41 studies). We are uncertain whether educational interventions improve device usage, as the certainty of evidence was assessed as very low. We were unable to perform meta-analyses for number of withdrawals and symptom scores due to high study heterogeneity. Supportive interventions probably increase device usage by 0.70 hours/night (95% confidence interval (CI) 0.36 to 1.05, N = 1426, 13 studies, moderate-certainty evidence), and low-certainty evidence indicates that the number of participants who used their devices ≥ 4 hours/night may increase from 601 to 717 per 1000 (odds ratio (OR), 1.68, 95% CI 1.08 to 2.60, N = 376, 2 studies). However, the number of withdrawals may also increase from 136 to 167 per 1000 (OR 1.27, 95% CI 0.97 to 1.66, N = 1702, 11 studies, low-certainty evidence). Participants may experience small improvements in symptoms (ESS score -0.32 points, 95% CI -1.19 to 0.56, N = 470, 5 studies, low-certainty evidence), and we are uncertain whether quality of life improves with supportive interventions, as the certainty of evidence was assessed as very low. When compared with usual care, behavioural interventions produce a clinically-meaningful increase in device usage by 1.31 hours/night (95% CI 0.95 to 1.66, N = 578, 8 studies, high-certainty evidence), probably increase the number of participants who used their machines ≥ 4 hours/night from 371 to 501 per 1000 (OR 1.70, 95% CI 1.20 to 2.41, N = 549, 6 studies, high-certainty evidence), and reduce the number of study withdrawals from 146 to 101 per 1000 (OR 0.66, 95% CI 0.44 to 0.98, N = 939, 10 studies, high-certainty evidence). Behavioural interventions may reduce symptoms (ESS score -2.42 points, 95% CI -4.27 to -0.57, N = 272, 5 studies, low-certainty evidence), but probably have no effect on quality of life (Functional Outcomes of Sleep Questionnaire (FOSQ), standardised mean difference (SMD) 0.00, 0.95% CI -0.26 to 0.26, N = 228, 3 studies, moderate-certainty evidence). We are uncertain whether behavioural interventions improve apnoea hypopnoea index (AHI), as the certainty of evidence was assessed as very low. We are uncertain if mixed interventions improve device usage, increase the number of participants using their machines ≥ 4 hours/night, reduce study withdrawals, improve quality of life, or reduce anxiety symptoms, as the certainty of evidence for these outcomes was assessed to be very low. Symptom scores via the ESS could not be measured due to considerable heterogeneity between studies.

AUTHORS' CONCLUSIONS

In CPAP-naïve people with OSA, high-certainty evidence indicates that behavioural interventions yield a clinically-significant increase in hourly device usage when compared with usual care. Moderate certainty evidence shows that supportive interventions increase usage modestly. Very low-certainty evidence shows that educational and mixed interventions may modestly increase CPAP usage. The impact of improved CPAP usage on daytime sleepiness, quality of life, and mood and anxiety scores remains unclear since these outcomes were not assessed in the majority of included studies. Studies addressing the choice of interventions that best match individual patient needs and therefore result in the most successful and cost-effective therapy are needed.

Continuous intraocular pressure monitoring in patients with obstructive sleep apnea syndrome using a contact lens sensor

Purpose

To analyse nocturnal intraocular pressure (IOP) fluctuations in patients with obstructive sleep apnea syndrome (OSAS) using a contact lens sensor (CLS) and to identify associations between the OSAS parameters determined by polysomnographic study (PSG) and IOP changes.

Method

Prospective, observational study. Twenty participants suspected of having OSAS were recruited. During PSG study, IOP was monitored using a CLS placed in the eye of the patient. The patients were classified according to the apnea-hypopnea index (AHI) in two categories, severe (>30) or mild/moderate (<30) OSAS. We evaluated several parameters determined by the IOP curves, including nocturnal elevations (acrophase) and plateau times in acrophase (PTs) defined by mathematical and visual methods.

Results

The IOP curves exhibited a nocturnal acrophase followed by PTs of varying extents at which the IOP remained higher than daytime measurement with small variations. We found significant differences in the length of the PTs in patients with severe OSAS compared to those with mild/moderate disease (P = 0.032/P = 0.028). We found a positive correlation between PTs and OSAS severity measured by the total number of apneic events (r = 0.681/0.751 P = 0.004/0.001) and AHI (r = 0.674/0.710, P = 0.004/0.002). Respiratory-related arousal and oxygen saturation also were associated significantly with the IOP PT length.

Conclusions

Periods of nocturnal IOP elevation lasted longer in severe OSAS patients than those with mild/moderate OSAS and correlate with the severity of the disease. The length of the nocturnal PT is also associated to respiratory parameters altered in patients with OSAS.

Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine

Current strategies for the management of OSA reflect a one-size-fits-all approach. Diagnosis and severity of OSA are based on the apnea-hypopnea index and treatment initiated with CPAP, followed by trials of alternatives (eg, oral appliances) if CPAP "fails." This approach does not consider the heterogeneity of individuals with OSA, reflected by varying risk factors, pathophysiological causes, clinical manifestations, and consequences. Recently, studies using analytic approaches such as cluster analysis have taken advantage of this heterogeneity to identify OSA phenotypes, or subtypes of patients with unique characteristics, that may enable more personalized approaches to prognostication and treatment. Examples include symptom-based subtypes such as "excessively sleepy" and "disturbed sleep" with differing impact of CPAP on symptoms and health-related quality of life. Polysomnographic subtypes, distinguished by respiratory event association with hypoxemia, arousals, or both, exhibit varying risks of cardiovascular disease and response to therapy. This review summarizes the findings from recent cluster analysis studies in sleep apnea and synthesizes common themes to describe the potential role (and limitations) of phenotypic subtypes in precision medicine for OSA. It also highlights future directions, including linking of phenotypes to clinically relevant outcomes, rigorous and transparent assessment of phenotype reproducibility, and need for tools that categorize patients into subtypes, to prospectively validate phenotype-based prognostication and treatment approaches. Finally, we highlight the critical need to include women and more racially/ethnically diverse populations in this area of research if we are to leverage the heterogeneity of OSA to improve patient lives.

An Investigation of the Relationship between the Apnoea Hypopnoea Index and Apnoea Hypopnoea Duration per Hour

A number of automated apnoea hypopnoea index (AHI) prediction algorithms first divide the signal(s) of interest into epochs, make a prediction for each epoch, determine the number of epoch predictions per hour and map this to an AHI value. An underlying assumption of this approach is a smooth relationship between the apnoea plus hypopnoea duration and the AHI value. In this study we investigate this relationship to establish if this assumption impacts the final system. We compare two models: one which divides the duration by recording time, and the second which divides the duration by sleep time. Data for study was obtained from 200 scored overnight polysomnogram recordings. Our results show that the relationship is a power-law distribution with an exponent of 0.9 and a multiplicative noise term. Analysis of the variance of the noise term revealed that algorithms that scale the apnoea duration by the recording time will have an inherent 37% error in the AHI estimate, while algorithms that scale by sleep time will have an inherent 25% error in the AHI estimate. Receiver operator curve (ROC) analysis of the duration-based models at the clinically important values of AHI 5 and 15 revealed an area under the ROC of greater than 0.96. We conclude that epoch-based models for AHI estimation do have an inherent error in their estimates, with models that divide the duration by sleep time providing a better estimate. Both models can correctly identify normal and apnoeic patients at the clinically important values with high sensitivity and specificity.

Association of Anxiety and Depression in Obstructive Sleep Apnea Patients: A Systematic Review and Meta-Analysis

Background: Obstructive sleep apnea (OSA) has been associated with mental disorders, but the strength of this association is unknown. The aim of our study was to investigate the association among OSA, depression, and anxiety in adults and to quantitatively summarize the results. Methods: A literature search in Medline, PubMed, PsycInfo, Scopus, and Web of Science was conducted. Seventy-three articles were selected for study. Results: The pooled prevalence of depressive and anxious symptoms in OSA patients was 35% (95% CI, 28-41%) and 32% (95% CI, 22-42%), respectively. Conclusions: The association between OSA, anxiety, and depression indicates the value of an early diagnosis and personalized treatment of OSA to improve mental disorders conditioning compliance to therapy. These conditions share a probably bidirectional relationship.

Oral Appliance Therapy for Obstructive Sleep Apnoea: State of the Art

Obstructive sleep apnoea (OSA) represents a significant global health burden, with impact on cardiometabolic health, chronic disease, productivity loss and accident risk. Oral appliances (OA) are an effective therapy for OSA and work by enlarging and stabilising the pharyngeal airway to prevent breathing obstructions during sleep. Although recommended in clinical guidelines for OSA therapy, they are often considered only as second-line therapy following positive airway pressure (PAP) therapy failure. There has been a long-standing barrier to selecting OA over PAP therapy due to the inability to be certain about the level of efficacy in individual OSA patients. A range of methods to select OSA patients for OA therapy, based on the outcome of a single sleep study night, have been proposed, although none has been widely validated for clinical use. Emergent health outcome data suggest that equivalent apnoea–hypopnea index reduction may not be necessary to produce the same health benefits of PAP. This may be related to the more favourable adherence to OA therapy, which can now be objectively verified. Data on longer term health outcomes are needed, and there are additional opportunities for device improvement and combination therapy approaches. OAs have an important role in precision care of OSA as a chronic disorder through a multi-disciplinary care team. Future studies on real-world health outcomes following OA therapy are needed.

Critical Issues in Dental and Medical Management of Obstructive Sleep Apnea

This critical review focuses on obstructive sleep apnea (OSA) and its management from a dental medicine perspective. OSA is characterized by ≥10-s cessation of breathing (apnea) or reduction in airflow (hypopnea) ≥5 times per hour with a drop in oxygen and/or rise in carbon dioxide. It can be associated with sleepiness and fatigue, impaired mood and cognition, cardiometabolic complications, and risk for transportation and work accidents. Although sleep apnea is diagnosed by a sleep physician, its management is interdisciplinary. The dentist’s role includes 1) screening patients for OSA risk factors (e.g., retrognathia, high arched palate, enlarged tonsils or tongue, enlarged tori, high Mallampati score, poor sleep, supine sleep position, obesity, hypertension, morning headache or orofacial pain, bruxism); 2) referring to an appropriate health professional as indicated; and 3) providing oral appliance therapy followed by regular dental and sleep medical follow-up. In addition to the device features and provider expertise, anatomic, behavioral, demographic, and neurophysiologic characteristics can influence oral appliance effectiveness in managing OSA. Therefore, OSA treatment should be tailored to each patient individually. This review highlights some of the putative action mechanisms related to oral appliance effectiveness and proposes future research directions.