Improvement in obstructive sleep apnea diagnosis and management wait times: A retrospective analysis of home management pathway for obstructive sleep apnea

Performance of wearable finger ring trackers for diagnostic sleep measurement in the clinical context

Ring-trackers are a growing consumer wearable category that provide a number of sleep metrics, yet their measurement accuracy remains poorly understood. Previous validation studies have mainly focused on healthy individuals, while a significant part of the potential present and future value lies in applications on non-healthy subjects. To enable applications in research and medical applications, rigorous evaluation of performance in clinical settings against the gold-standard polysomnography is needed. To address this knowledge gap, we investigated how the measurements of three commercially available ring trackers (Oura, SleepOn, Circul) perform against polysomnography in a university sleep lab population with a diverse set sleep-related disorders as well as sleep-unrelated medical conditions. We evaluated individual-level and group-level discrepancies of standard sleep measures and conducted an epoch-by-epoch analysis of sleep staging classification performance using a standardized analysis framework. While average group-level sleep measures are similar (e.g., TST differences between rings and gold standard were below 12 min for the Oura ring), individual-level differences often remained large. Ring-derived sleep metrics were characterized by complex bias, indicating that their correction is non-trivial. Sleep/Wake distinction of the Oura and SleepOn rings reached similar performance as previously reported for healthy individuals (~ 85% accuracy), but was worse for the Circul ring (~ 65% accuracy). Sleep stage classification (Wake, Light, Deep, REM sleep) sensitivity ranged from 0.14 (REM sleep classification of the Circul ring) to 0.58 (Light sleep classification of the SleepOn ring). Across all sleep stages, the Oura and SleepOn ring performed similarly (53.18% and 50.48% accuracy), whereas the Circul ring performed worse (35.06% accuracy). Our findings confirm recent descriptions of device-related bias and additionally uncover practical limitations in the application in a real-world sleep laboratory patient cohort. Critically, while some devices may demonstrate reasonable agreement with PSG on average, this agreement masks substantial individual-level inaccuracies, prohibiting their use in clinical sleep medicine, as accurate assessment of individual nights, including both nights with exceptionally low or high sleep quality and quantity, is essential for patient care.

A Study of Volatile Organic Compounds in Patients with Obstructive Sleep Apnea

Background: Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder characterized by intermittent upper airway obstruction, leading to significant health consequences. Traditional diagnostic methods, such as polysomnography, are time-consuming and resource-intensive. Objectives: This study explores the potential of proton-transfer-reaction mass spectrometry (PTR-MS) in identifying volatile organic compound (VOC) biomarkers for the non-invasive detection of OSA. Methods: Breath samples from 89 participants, including 49 OSA patients and 40 controls, were analyzed using PTR-MS. Significance analysis was performed between OSA patients and controls to identify potential biomarkers for OSA. To as-sess the differences in VOC concentrations between OSA patients and control subjects, the Wilcoxon rank-sum test was employed. partial least squares discriminant analysis (PLS-DA) analysis and heatmap plot was conducted to visualize the differentiation between OSA patients and control subjects based on their VOC profiles.In order to further investigate the correlation between identified biomarkers and the severity of OSA measured by Apnea-Hypopnea Index (AHI), regression analysis was conducted between biomarkers and AHI Index. Results: The results identified specific VOCs, including m045 (acetaldehyde), m095.950, and m097.071, which showed significant differences between OSA patients and controls. Advanced statistical analyses, including PLS-DA and correlation mapping, highlighted the robustness of these biomarkers, with m045 (acetaldehyde) specifically emerging as a potential biomarker associated with the AHI Index. Conclusions: This study underscores the potential of VOCs as biomarkers for identifying patients with severe AHI levels. The analysis of VOCs using PTR-MS presents a rapid, non-invasive, and cost-effective method that could be seamlessly integrated into clinical practice, allowing clinicians to better stratify patients based on their need for polysomnography and prioritize those requiring earlier testing. Future studies are necessary to validate these findings in larger cohorts and to explore the integration of PTR-MS with other diagnostic modalities for improved accuracy and clinical utility.

Longer-term impacts of the COVID-19 pandemic on obstructive sleep apnoea (OSA)-related healthcare: a province-based study

RATIONALE

Following marked reductions in sleep medicine care early in the COVID-19 pandemic, there is limited information about the recovery of these services. We explored long-term trends in obstructive sleep apnoea (OSA) health services and service backlogs during the pandemic compared with pre-pandemic levels in Ontario (the most populous province of Canada).

METHODS

In this retrospective population-based study using Ontario (Canada) health administrative data on adults, we compared rates of polysomnograms (PSGs), outpatient visits and positive airway pressure (PAP) therapy purchase claims during the pandemic (March 2020 to December 2022) to pre-pandemic rates (2015-2019). We calculated projected rates using monthly seasonal time series auto-regressive integrated moving-average models based on similar periods in previous years. Service backlogs were estimated from the difference between projected and observed rates.

RESULTS

Compared with historical data, all service rates decreased at first during March to May 2020 and subsequently increased. By December 2022, observed service rates per 100 000 persons remained lower than projected for PSGs (September to December 2022: 113 vs 141, 95% CI: 121 to 163) and PAP claims (September to December 2022: 50 vs 60, 95% CI: 51 to 70), and returned to projected for outpatient OSA visits. By December 2022, the service backlog was 193 078 PSGs (95% CI: 139 294 to 253 075) and 57 321 PAP claims (95% CI: 27 703 to 86 938).

CONCLUSION

As of December 2022, there was a sustained reduction in OSA-related health services in Ontario, Canada. The resulting service backlog has likely worsened existing problems with underdiagnosis and undertreatment of OSA and supports the adoption of flexible care delivery models for OSA that include portable technologies.

Correlation between cone-beam computed tomographic findings and the apnea-hypopnea index in obstructive sleep apnea patients: A cross-sectional study

Purpose

The aim of this study was to explore the correlations of cone-beam computed tomographic findings with the apnea-hypopnea index in patients with obstructive sleep apnea.

Materials and Methods

Forty patients with obstructive sleep apnea were selected from the ear-nose-throat (ENT) outpatient clinic, Faculty of Medicine, Mansoura University. Cone-beam computed tomography was performed for each patient at the end of both inspiration and expiration. Polysomnography was carried out, and the apnea-hypopnea index was obtained. Linear measurements, including cross-sectional area and the SNA and SNB angles, were obtained. Four oral and maxillofacial radiologists categorized pharyngeal and retropalatal airway morphology and calculated the airway length and volume. Continuous data were tested for normality using the Kolmogorov-Smirnov test and reported as the mean and standard deviation or as the median and range. Categorical data were presented as numbers and percentages, and the significance level was set at P<0.05.

Results

The minimal value of the cross-sectional area, SNB angle, and airway morphology at the end of inspiration demonstrated a statistically significant association (P<0.05) with the apnea-hypopnea index, with excellent agreement. No statistically significant difference was found in the airway volume, other linear measurements, or retropalatal airway morphology.

Conclusion

Cone-beam computed tomographic measurements in obstructive sleep apnea patients may be used as a supplement to a novel radiographic classification corresponding to the established clinical apnea-hypopnea index classification.

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review

Obstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.

The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review

BACKGROUND

Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.

OBJECTIVE

This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population.

METHODS

We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.

RESULTS

We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.

CONCLUSIONS

These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.

TRIAL REGISTRATION

PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.

Patient-reported Differences in Obstructive Sleep Apnea Care between Jurisdictions with and without Government Funding for Continuous Positive Airway Pressure

Rationale: Funding for obstructive sleep apnea (OSA) treatment may impact how patients access care, wait times, and costs of care. Objectives: The aim of this study was to compare differences in diagnosis and treatment of OSA between Canadian jurisdictions with and without public funding for continuous positive airway pressure (CPAP). Methods: We administered an anonymous internet survey to Canadian adults reporting a physician diagnosis of OSA. Responses were categorized on the basis of whether the respondent's province provided full or partial funding for CPAP therapy for all patients. We assessed wait times for diagnosis and treatment, patient-borne costs, and model of care delivery compared between jurisdictions with and without universal CPAP funding. Results: We received 600 responses representing all Canadian provinces and territories. The median (interquartile range) age was 59 (49-66) years; 57% were male, and 21% were from rural settings. Patients living in provinces without public CPAP funding (n = 419) were more likely to be diagnosed using home sleep apnea testing (69% vs. 20%; P = 0.00019). Wait times were similar after adjustment for demographics, disease characteristics, and model of care. Although patient-borne costs of care were similar between jurisdictions, patients from regions without CPAP funding reported that cost had a greater influence on the choice of therapy. Sleep specialists were more commonly involved in OSA care in regions with CPAP funding. There was no difference in the current use of therapy between jurisdictions with and without public funding. Conclusions: This survey study demonstrates that public funding for CPAP therapy impacts how Canadians access OSA care but is not associated with differences in wait times or costs. Future research is required to determine the impact of different funding models for OSA care on clinical outcomes.

Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea

Background

Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, requires specialists to utilize, expensive, and has accessibility challenges. There are also challenges with awareness and identification of obstructive sleep apnea in the primary care setting. Artificial intelligence systems offer the opportunity for a new diagnostic approach that addresses the limitations of polysomnography and ultimately benefits patients by streamlining the diagnostic expedition.

Main body

The purpose of this project is to elucidate the barriers that exist in the implementation of artificial intelligence systems into the diagnostic context of obstructive sleep apnea. It is essential to understand these challenges in order to proactively create solutions and establish an efficient adoption of this new technology. The literature regarding the evolution of the diagnosis of obstructive sleep apnea, the role of artificial intelligence in the diagnosis, and the barriers in artificial intelligence implementation was reviewed and analyzed.

Conclusion

The barriers identified were categorized into different themes including technology, data, regulation, human resources, education, and culture. Many of these challenges are ubiquitous across artificial intelligence implementation in any medical diagnostic setting. Future research directions include developing solutions to the barriers presented in this project.

Graphical abstract

Waist-To-Hip Ratio Predicts Abnormal Overnight Oximetry in Men Independent of Body Mass Index

Background: Ambulatory overnight oximetry (OXI) has emerged as a cost-effective initial test for sleep disordered breathing. Obesity is closely associated with obstructive sleep apnea (OSA); however, whether body mass index (BMI) or waist-to-hip ratio (WHR) predicts abnormal overnight OXI remains unknown. Methods: We performed a retrospective cross-sectional study of 393 men seen in the Executive Health Program at Mayo Clinic in Rochester, Minnesota who underwent ambulatory overnight OXI ordered by preventive medicine physicians between January 1, 2004 through December 31, 2010. We compared participant/spouse-reported symptoms (sleepiness, snoring), physician indications for OXI (obesity, fatigue), Epworth Sleepiness Scale scores, anthropomorphic measurements (WHR, BMI), and comorbid medical conditions (hypertension, diabetes) with OXI results. Results: 295 of the 393 men who completed OXI had abnormal results. During multivariate analysis, the strongest independent predictor of abnormal OXI for men was WHR (≥1.0, OR = 5.59) followed by BMI (≥30.0 kg/m2, OR = 2.75), age (≥55 yrs, OR = 2.06), and the presence of snoring (OR = 1.91, P < 0.05 for all). A strong association was observed between WHR and abnormal OXI in obese (BMI ≥ 30.0 kg/m2, OR = 6.28) and non-obese (BMI < 29.9 kg/m2, OR = 6.42, P < 0.01 for both) men. Furthermore, 88 men with abnormal OXI underwent polysomnography with 91% being subsequently diagnosed with OSA. Conclusions: In ambulatory, predominantly middle-aged men undergoing preventive services evaluation many physician indications for OXI were not predictors of abnormal results; however, WHR strongly predicted abnormal OXI in obese and non-obese men. As such, we suggest middle-aged men who snore and have a WHR ≥1.0 should be directly referred to a sleep clinic for polysomnography.

Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

Assessment of Patient Journey Metrics for Users of a Digital Obstructive Sleep Apnea Program: Single-Arm Feasibility Pilot Study

Background

Despite the importance of diagnosis and treatment, obstructive sleep apnea (OSA) remains a vastly underdiagnosed condition; this is partially due to current OSA identification methods and a complex and fragmented diagnostic pathway.

Objective

This prospective, single-arm, multistate feasibility pilot study aimed to understand the journey in a nonreferred sample of participants through the fully remote OSA screening and diagnostic and treatment pathway, using the Primasun Sleep Apnea Program (formally, Verily Sleep Apnea Program).

Methods

Participants were recruited online from North Carolina and Texas to participate in the study entirely virtually. Eligible participants were invited to schedule a video telemedicine appointment with a board-certified sleep physician who could order a home sleep apnea test (HSAT) to be delivered to the participant's home. The results were interpreted by the sleep physician and communicated to the participant during a second video telemedicine appointment. The participants who were diagnosed with OSA during the study and prescribed a positive airway pressure (PAP) device were instructed to download an app that provides educational and support-related content and access to personalized coaching support during the study’s 90-day PAP usage period. Surveys were deployed throughout the study to assess baseline characteristics, prior knowledge of sleep apnea, and satisfaction with the program.

Results

For the 157 individuals who were ordered an HSAT, it took a mean of 7.4 (SD 2.6) days and median 7.1 days (IQR 2.0) to receive their HSAT after they completed their first televisit appointment. For the 114 individuals who were diagnosed with OSA, it took a mean of 13.9 (SD 9.6) days and median 11.7 days (IQR 10.1) from receiving their HSAT to being diagnosed with OSA during their follow-up televisit appointment. Overall, the mean and median time from the first televisit appointment to receiving an OSA diagnosis was 21.4 (SD 9.6) days and 18.9 days (IQR 9.2), respectively. For those who were prescribed PAP therapy, it took a mean of 8.1 (SD 9.3) days and median 6.0 days (IQR 4.0) from OSA diagnosis to PAP therapy initiation.

Conclusions

These results demonstrate the possibility of a highly efficient, patient-centered pathway for OSA workup and treatment. Such findings support pathways that could increase access to care, reduce loss to follow-up, and reduce health burden and overall cost. The program’s ability to efficiently diagnose patients who otherwise may have not been diagnosed with OSA is important, especially during a pandemic, as the United States shifted to remote care models and may sustain this direction. The potential economic and clinical impact of the program’s short and efficient journey time and low attrition rate should be further examined in future analyses. Future research also should examine how a fast and positive diagnosis experience impacts success rates for PAP therapy initiation and adherence.

Trial Registration

ClinicalTrials.gov NCT04599803; https://clinicaltrials.gov/ct2/show/NCT04599803

A Fuzzy Neural Network Model for Rapid Prediction of Optimal Positive Airway Pressures in OSAS Patients

Manual titration of positive airway pressure (PAP) is a gold standard to provide an optimal pressure for the treatment of obstructive sleep apnea-hypopnea syndrome (OSAS). Since manual titration studies were costly and time-consuming, many statistical models for predicting effective PAPs were reported. However, the prediction accuracies of the models associated with nocturnal parameters still remain low. This study proposes a fuzzy neural prediction network (FNPN) with input candidate variables, selected among easily available measurements (e.g., body mass index (BMI), waist circumstance (WC), and body composition) and OSAS related questionnaires, to rapidly predict an optimal PAP. The FNPN comprises fuzzy rules and is characterized with the ability of automatic rule growing and pruning from training data. A total of 147 participants from April 2018 to April 2019 were enrolled in Taichung Veterans General Hospital, Taiwan. After two selection processes for feature extraction, WC and BMI were the significant variables for entering the FNPN to predict optimal PAP. Experimental results showed that the average successful prediction rate of the proposed method was 71.8%. This study also found that Epworth sleepiness scales (ESS) and body composition, such as visceral fat area and percent body fat, were excluded in the final prediction model. Compared with existing models, the proposed prediction approach provided a rapid prediction of optimal PAP with higher accuracy.

Nurse-led triage of new sleep referrals is associated with lower risk of potentially contraindicated sleep testing: a retrospective cohort study

BACKGROUND

The volume of specialty care referrals often outstrips specialists' capacity. The Department of Veterans Affairs launched a system of referral coordination to augment our workforce, empowering registered nurses to use decision support tools to triage specialty referrals. While task shifting may improve access, there is limited evidence regarding the relative quality of nurses' triage decisions to ensure such management is safe.

OBJECTIVE

Within the specialty of sleep medicine, we compared receipt of contraindicated testing for obstructive sleep apnoea (OSA) between patients triaged to sleep testing by nurses in the referral coordination system (RCS) relative to our traditional specialist-led system (TSS).

METHODS

Patients referred for OSA evaluation can be triaged to either home sleep apnoea testing (HSAT) or polysomnography, and existing guidelines specify patients for whom HSAT is contraindicated. In RCS, nurses used a decision support tool to make triage decisions for sleep testing but were instructed to seek specialist oversight in complex cases. In TSS, specialists made triage decisions themselves. We performed a single-centre retrospective cohort study of patients without OSA who were referred to sleep testing between September 2018 and August 2019. Patients were assigned to triage by RCS or TSS in quasirandom fashion based on triager availability at time of referral. We compared receipt of contraindicated sleep tests between groups using a generalised linear model adjusted for day of the week and time of day of referral.

RESULTS

RCS triaged 793 referrals for OSA evaluation relative to 1787 by TSS. Patients with RCS triages were at lower risk of receiving potentially contraindicated sleep tests relative risk 0.52 (95% CI 0.29 to 0.93).

CONCLUSION

Our results suggest that incorporating registered nurses into triage decision-making may improve the quality of diagnostic care for OSA.

Alternative Care Pathways for Obstructive Sleep Apnea and the Impact on Positive Airway Pressure Adherence: Unraveling the Puzzle of Adherence

The high burden of obstructive sleep apnea (OSA), combined with inadequate supply of sleep specialists and constraints on polysomnography resources, has prompted interest in alternative models of care to improve access and treatment effectiveness. In appropriately selected patients, ambulatory clinical pathways and use of nonphysicians or primary care providers to manage OSA can improve timely access and costs without compromising adherence or other clinical outcomes. Although initial studies show promising results, there are several potential barriers that must be considered before broad implementation, and further implementation research and economic evaluation studies are required.

OSA in Professional Transport Operations: Safety, Regulatory, and Economic Impact

OSA is common among commercial vehicle operators (CVOs) in all modes of transportation, including truck, bus, air, rail, and maritime operations. OSA is highly prevalent and increases the risk of drowsiness-related crashes in CVOs. Internationally, specific regulations regarding its identification and management vary widely or do not exist; medical examiners and sleep medicine specialists are urged to use available guidance documents in their absence. Education, screening, prompt identification and treatment, and ongoing surveillance to ensure effective therapy can lower the risk of fatigue-related crashes.

Detection Performance Regarding Sleep Apnea-Hypopnea Episodes with Fuzzy Logic Fusion on Single-Channel Airflow Indexes

Obstructive sleep apnea-hypopnea syndrome (OSAHS) affects more than 936 million people worldwide and is the most common sleep-related breathing disorder; almost 80% of potential patients remain undiagnosed. To treat moderate to severe OSAHS as early as possible, the use of fewer sensing channels is recommended to screen for OSAHS and shorten waiting lists for the gold standard polysomnography (PSG). Hence, an effective out-of-clinic detection method may provide a solution to hospital overburden and associated health care costs. Applying single-channel signals to simultaneously detect apnea and hypopnea remains challenging. Among the various physiological signals used for sleep apnea-hypopnea detection, respiratory signals are relatively easy to apply. In this study, a fusion method using fuzzy logic and two single-channel respiratory indexes was proposed. A total of 12,391 apnea or hypopnea episodes were included. The proposed algorithm successfully fused standard deviation of airflow signals (SDA) and amplitude changes of peaks (ACP) indexes to detect apnea-hypopnea events, with overall sensitivity of 74%, specificity of 100%, and accuracy of 80% for mild to moderate OSAHS. For different apnea-hypopnea severity levels, the results indicated that the algorithm is superior to other methods; it also provides risk scores as percentages, which are especially accurate for mild hypopnea. The algorithm may provide rapid screening for early diagnosis and treatment.

Agreement of sleep specialists with registered nurses' sleep study orders in supervised clinical practice

STUDY OBJECTIVES

Incorporating registered nurses (RN-level) into obstructive sleep apnea (OSA) management decisions has the potential to augment the workforce and improve patient access, but the appropriateness of such task-shifting in typical practice is unclear.

METHODS

Our medical center piloted a nurse triage program for sleep medicine referrals. Using a sleep specialist-designed decision-making tool, nurses triaged patients referred for initial sleep studies to either home sleep apnea test (HSAT) or in-laboratory polysomnography (PSG). During the first 5 months of the program, specialists reviewed all nurse triages. We compared agreement between specialists and nurses.

RESULTS

Of 280 consultations triaged by nurses, nurses deferred management decisions to sleep specialists in 6.1% (n = 17) of cases. Of the remaining 263 cases, there was 88% agreement between nurses and specialists (kappa 0.80, 95% confidence interval 0.74-0.87). In the 8.8% (n = 23) of cases where supervising specialists changed sleep study type, specialists changed from HSAT to PSG in 16 cases and from PSG to HSAT in 7. The most common indication for change in sleep study type was disagreement regarding OSA pretest probability (n = 14 of 23). Specialists changed test instructions in 3.0% (n = 8) of cases, with changes either related to the use of transcutaneous carbon dioxide monitoring (n = 4) or adaptive servo-ventilation (n = 4).

CONCLUSIONS

More than 80% of sleep study triages by registered nurses in a supervised setting required no sleep specialist intervention. Future research should focus on how to integrate nurses into the sleep medicine workforce in a manner that maximizes efficiency while preserving or improving patient outcomes.

Redesigning Care for OSA

Constrained by a limited supply of specialized personnel, health systems face a challenge in caring for the large number of patients with OSA. The complexity of this challenge is heightened by the varied clinical presentations of OSA and the diversity of treatment approaches. Innovations such as simplified home-based care models and the incorporation of nonspecialist providers have shown promise in the management of uncomplicated patients, producing comparable outcomes to the resource-intensive traditional approach. However, it is unclear if these innovations can meet the needs of all patients with OSA, including those with mild disease, atypical presentations, and certain comorbid medical and mental health conditions. This review discusses the diversity of needs in OSA care, the evidence base behind recent care innovations, and the potential limitations of each innovation in meeting the diversity of care needs. We propose how these innovations can fit within the stepped care and hub and spoke models in a way that addresses the full spectrum of OSA, and we discuss future research directions to assess the deployment of these innovations.

Telemedicine in the diagnosis and treatment of sleep apnoea

Telemedicine (TM) is a current tool in the landscape of medicine. It helps to address public health challenges such as increases in chronic disease in an ageing society and the associated burden in healthcare costs. Sleep TM refers to patient data exchange with the purpose of enhancing disease management. Obstructive sleep apnoea (OSA) syndrome is a chronic disorder associated with a significant morbidity, mainly cardiometabolic, and mortality. Obtaining adequate compliance to continuous positive airway pressure (CPAP) remains the greatest challenge related to OSA treatment, and the adoption of TM to support OSA management makes sense. In addition, the prevalence of OSA is growing and OSA is associated with increased healthcare costs that could be streamlined by the application of TM. In OSA, multiple modalities of TM are utilised, such as telediagnostics, teleconsultation, teletherapy and telemonitoring of patients being treated with CPAP. In the present article, I aim to provide an overview of current practice and the recent developments in TM for OSA management. Concerns related to TM use will also be addressed.

International consensus (ICON) on the ENT role in diagnosis of obstructive sleep apnea syndrome

During the 2017 IFOS ENT World Congress, an international expert panel was asked to clarify the role of ENT in the diagnosis process of the obstructive sleep apnea syndrome (OSA) in adults around the world. OSA is a major public health issue throughout the world. OSA is a highly prevalent disease with heavy clinical, social and economical outcomes. This high prevalence raises serious difficulties of diagnosis accessibility if only somnologists are able to confirm OSA diagnosis. First of all, the panellists reviewed the impact of OSA. Secondly, they defined the ENT role stressing ENT legitimacy, professional expertise and academic and institutional tasks. They also defined when somnologists were necessary. For the international panel, the ENT is a major player in the OSA diagnosis process.

Investigating Cost Implications of Incorporating Level III At-Home Testing into a Polysomnography Based Sleep Medicine Program Using Administrative Data

Objective

Obstructive sleep apnea is a common problem, requiring expensive in-lab polysomnography for proper diagnosis. Home monitoring can provide an alternative to in-lab testing for a subset of OSA patients. The objective of this project was to investigate the effect of incorporating home testing into an OSA program at a large, tertiary sleep disorders centre.

Methods

The Sleep Disorders Centre in Saskatoon, Canada, has been incorporating at-home testing into their diagnostic pathways since 2006. Administrative data from 2007 to 2013 were extracted (10030 patients) and the flow of patients through the program was followed from diagnosis to treatment. Costs were estimated using 2014 pricing and were stratified by disease attributes and sensitivity analysis was applied.

Results

The overall costs per patient were $627.40, with $419.20 for at-home testing and $746.20 for in-lab testing. The cost of home management would rise to $515 if all negative tests were required to be confirmed by an in-lab PSG.

Discussion

Our review suggests that at-home testing can be cost-effective alternative to in-lab testing when applied to the correct population, specifically, those with a high pretest probability of obstructive sleep apnea and an absence of significant comorbidities.