A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study

Novel physiologic predictors of positive airway pressure effectiveness (NICEPAP) study: rationale, design and methods

PURPOSE

Continuous positive airway pressure (CPAP) is the primary therapy for obstructive sleep apnea (OSA); however the effectiveness of CPAP remains suboptimal. We describe the Novel PhysIologiC prEdictors of Positive Airway Pressure Effectiveness (NICEPAP) study. Its purpose is to determine whether physiological traits of OSA contribute to CPAP effectiveness.

METHODS

NICEPAP (NCT05067088) is a prospective, observational cohort study conducted at an academic sleep center. Adults newly diagnosed with OSA (n = 267) are assessed for OSA traits of loop gain, arousal threshold, pharyngeal collapsibility, and muscle compensation from baseline polysomnography. We perform a comprehensive assessment of covariates relevant to CPAP adherence, efficacy, and patient-centered outcomes. Participants are followed for 12 months. Primary outcomes include (1) CPAP adherence (hours/night), (2) CPAP efficacy (apneas-hypopneas/hour), and (3) quality of life at six months measured by objective CPAP data and Functional Outcomes of Sleep Questionnaire. Secondary outcomes include sleep quality, sleepiness, insomnia, and neurocognitive function.

RESULTS

Data on covariates, including demographics, sleep symptoms, medical history, medications, sleep quality, OSA and treatment self-efficacy, decisional balance, and socio-economic and social and partner support, are collected using validated instruments. The analysis for primary outcomes includes a generalized linear mixed model for an outcome (e.g., CPAP adherence) with OSA traits as exposures followed by the addition of relevant covariates.

CONCLUSION

The findings of the NICEPAP study will inform research aimed to enhance CPAP effectiveness. Understanding the role of physiological OSA traits in CPAP effectiveness is a crucial step toward a precision medicine approach to OSA.

REM sleep breathing: Insights beyond conventional respiratory metrics

Breathing and sleep state are tightly linked. The traditional approach to evaluation of breathing in rapid eye movement sleep has been to focus on apneas and hypopneas, and associated hypoxia or hypercapnia. However, rapid eye movement sleep breathing offers novel insights into sleep physiology and pathology, secondary to complex interactions of rapid eye movement state and cardiorespiratory biology. In this review, morphological analysis of clinical polysomnogram data to assess respiratory patterns and associations across a range of health and disease is presented. There are several relatively unique insights that may be evident by assessment of breathing during rapid eye movement sleep. These include the original discovery of rapid eye movement sleep and scoring of neonatal sleep, control of breathing in rapid eye movement sleep, rapid eye movement sleep homeostasis, sleep apnea endotyping and pharmacotherapy, rapid eye movement sleep stability, non-electroencephalogram sleep staging, influences on cataplexy, mimics of rapid eye movement behaviour disorder, a reflection of autonomic health, and insights into cardiac arrhythmogenesis. In summary, there is rich clinically actionable information beyond sleep apnea encoded in the respiratory patterns of rapid eye movement sleep.

Posttraumatic Stress Disorder and Obstructive Sleep Apnea in Twins

Importance

Obstructive sleep apnea (OSA) is a common condition in older adult (aged >65 years) populations, but more mechanistic research is needed to individualize treatments. Previous evidence has suggested an association between OSA and posttraumatic stress disorder (PTSD) but is limited by possible selection bias. High-quality research on this association with a careful evaluation of possible confounders may yield important mechanistic insight into both conditions and improve treatment efforts.

Objective

To investigate the association of current PTSD symptoms and PTSD diagnosis with OSA.

Design, Setting, and Participants

This cross-sectional study of twin pairs discordant for PTSD, which allows for adjustment for familial factors, was conducted using in-laboratory polysomnography from March 20, 2017, to June 3, 2019. The study sample comprised male veteran twins recruited from the Vietnam Era Twin Registry. The data analysis was performed between June 11, 2022, and January 30, 2023.

Exposure

Symptoms of PTSD in twins who served in the Vietnam War. Diagnosis of PTSD was a secondary exposure.

Main Outcomes and Measures

Obstructive sleep apnea was assessed using the apnea-hypopnea index (AHI) (≥4% oxygen saturation criterion as measured by events per hour) with overnight polysomnography. Symptoms of PTSD were assessed using the PTSD Checklist (PCL) and structured clinical interview for PTSD diagnosis.

Results

A total of 181 male twins (mean [SD] age, 68.4 [2.0] years) including 66 pairs discordant for PTSD symptoms and 15 pairs discordant for a current PTSD diagnosis were evaluated. In models examining the PCL and OSA within pairs and adjusted for body mass index (BMI) and other sociodemographic, cardiovascular, and psychiatric risk factors (including depression), each 15-point increase in PCL was associated with a 4.6 (95% CI, 0.1-9.1) events-per-hour higher AHI. Current PTSD diagnosis was associated with an adjusted 10.5 (95% CI, 5.7-15.3) events-per-hour higher AHI per sleep-hour. Comparable standardized estimates of the association of PTSD symptoms and BMI with AHI per SD increase (1.9 events per hour; 95% CI, 0.5-3.3 events per hour) were found.

Conclusions and Relevance

This cross-sectional study found an association between PTSD and sleep-disordered breathing. The findings have important public health implications and may also enhance understanding of the many factors that potentially affect OSA pathophysiology.

Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?

Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.

Endotypic traits of supine position and supine-predominant obstructive sleep apnoea in Asian patients

BACKGROUND

Over half of all cases of obstructive sleep apnoea (OSA) are classified as supine-related OSA; however, the pathological endotype during supine position is not fully understood. This study aims to investigate the endotypic traits of supine-predominant OSA and explore the variations in endotypic traits between the supine and lateral positions.

METHODS

We prospectively recruited 689 adult patients with OSA from a single sleep centre between April 2020 and December 2022. Endotypic traits, namely arousal threshold, collapsibility, loop gain and upper airway muscle compensation, were retrieved from polysomnographic signals. We identified spOSA by a supine to non-supine apnoea-hypopnoea index (AHI) ratio >2. We cross-sectionally compared demographic and endotypic traits between supine-predominant OSA and non-positional OSA and examined the associations between supine-predominant OSA and endotypic traits. Additionally, we compared the changes in endotypic traits between supine and lateral positions in patients with supine-predominant OSA and non-positional OSA.

RESULTS

In our study sample, 75.8% of patients were identified as having supine-predominant OSA. Compared to non-positional OSA, supine-predominant OSA was associated with low collapsibility (β= -3.46 %eupnoea, 95% CI -5.93- -1.00 %eupnoea) and reduced compensation (β= -6.79 %eupnoea, 95% CI -10.60- -2.99 %eupnoea). When transitioning from the lateral to supine position, patients with supine-predominant OSA had a substantial decrease in compensation compared to those with non-positional OSA (-11.98 versus -6.28 %eupnoea).

CONCLUSIONS

Supine-predominant OSA is the prevalent phenotype of OSA in Asian patients. Inadequate upper airway compensation appears to be a crucial underlying pathology in patients with supine-predominant OSA.

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

Review and perspective on sleep-disordered breathing research and translation to clinics

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

Continuous positive airway pressure and adherence in patients with different endotypes of obstructive sleep apnea

Determining the endotypes of obstructive sleep apnea (OSA) has potential implications for precision interventions. Here we assessed whether continuous positive airway pressure (CPAP) treatment outcomes differ across endotypic subgroups. We conducted a retrospective analysis of data obtained from 225 patients with moderate-to-severe OSA from a single sleep centre. Polysomnographic and CPAP titration study data were collected between May 2020 and January 2022. One-month CPAP treatment adherence was followed. Obstructive sleep apnea endotypes, namely arousal threshold, collapsibility, loop gain, and upper airway gain were estimated from polysomnography and dichotomised as high versus low. We examined associations between endotypic subgroups and (1) optimal CPAP titration pressure, (2) CPAP-related improvements in sleep architecture (proportions of slow-wave and rapid eye movement (REM) sleep), and (3) CPAP adherence. We observed that patients with high collapsibility required a higher CPAP pressure than those with low collapsibility (∆ = 0.4 cmH2 O, 95% confidence interval [CI] = 0.3-1.7). A larger increase in slow-wave sleep and in REM sleep proportions after CPAP treatment were observed in patients with a high arousal threshold, high collapsibility, high loop gain, or high upper airway gain than in those with low levels of endotypes. High loop gain and high collapsibility were independently associated with longer CPAP use hours per night (∆ = 0.6 h, 95% CI = 0.2-1.5 and ∆ = 0.3 h, 95% CI = 0.03-1.5, respectively). In conclusion, different endotypic subgroups of OSA exhibit a difference in outcomes of CPAP treatment. Knowledge of endotypes may help clinicians to understand which patients are expected to benefit most from CPAP therapy prior to its administration.

Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach

INTRODUCTION

Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea-hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization.

METHODS

Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016-2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range).

RESULTS

981 subjects were studied: 41% females, age 56 years (45-66), overall AHI 23events/h (13-42) and body mass index (BMI) 30kg/m2 (27-34). Three PSG clusters were identified: Cluster 1: "Supine and obstructive apnea predominance" (433 patients, 44%). Cluster 2: "Central, REM and shorter-hypopnea predominance" (374 patients, 38%). Cluster 3: "Severe hypoxemic burden and higher wake after sleep onset" (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI≥30events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15≤AHI<30events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5≤AHI<15events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3.

CONCLUSIONS

The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.

Relationship between Symptom Profiles and Endotypes among Patients with Obstructive Sleep Apnea: A Latent Class Analysis

Rationale: Obstructive sleep apnea (OSA) is a heterogeneous syndrome with various endotypic traits and symptoms. A link among symptoms, endotypes, and disease prognosis has been proposed but remains unsupported by empirical data. Objectives: To link symptom profiles and endotypes by clustering endotypic traits estimated using polysomnographic signals. Methods: We recruited 509 patients with moderate to severe OSA from a single sleep center. Polysomnographic data were collected between May 2020 and January 2022. Endotypic traits, namely arousal threshold, upper airway collapsibility, loop gain, and upper airway muscle compensation, were retrieved using polysomnographic signals during non-rapid eye movement periods. We used latent class analysis to group participants into endotype clusters. Demographic and polysomnographic parameter differences were compared between clusters, and associations between endotype clusters and symptom profiles were examined using logistic regression analyses. Results: Three endotype clusters were identified, characterized by high collapsibility/loop gain, low arousal threshold, and low compensation, respectively. Patients in each cluster exhibited similar demographic characteristics, but those in the high collapsibility/loop gain cluster had the highest proportion of obesity and severe oxygen desaturation observed in polysomnographic studies. The low compensation cluster was characterized by fewer sleepy symptoms and exhibited a lower rate of diabetes mellitus. Compared with the excessively sleepy group, disturbed sleep symptoms were associated with the low arousal threshold cluster (odds ratio, 1.89; 95% confidence interval, 1.16-3.10). Excessively sleepy symptoms were associated with the high collapsibility/loop gain cluster (odds ratio, 2.16; 95% confidence interval, 1.39-3.37) compared with the minimally symptomatic group. Conclusions: Three pathological endotype clusters were identified among patients with moderate to severe OSA, each exhibiting distinct polysomnographic characteristics and clinical symptom profiles.

Targets for obstructive sleep apnea pharmacotherapy: principles, approaches, and emerging strategies

INTRODUCTION

Obstructive sleep apnea (OSA) is a common and serious breathing disorder. Several pathophysiological factors predispose individuals to OSA. These factors are quantifiable, and modifiable pharmacologically.

AREAS COVERED

Four key pharmacotherapeutic targets are identified and mapped to the major determinants of OSA pathophysiology. PubMed and Clinicaltrials.gov were searched through April 2023.

EXPERT OPINION

Target #1: Pharyngeal Motor Effectors. Increasing pharyngeal muscle activity and responsivity with noradrenergic-antimuscarinic combination is central to recent breakthrough OSA pharmacotherapy. Assumptions, knowledge gaps, future directions, and other targets are identified. #2: Upper Airway Sensory Afferents. There is translational potential of sensitizing and amplifying reflex pharyngeal dilator muscle responses to negative airway pressure via intranasal delivery of new potassium channel blockers. Rationales, advantages, findings, and potential strategies to enhance effectiveness are identified. #3: Chemosensory Afferents and Ventilatory Control. Strategies to manipulate ventilatory control system sensitivity by carbonic anhydrase inhibitors are supported in theory and initial studies. Intranasal delivery of agents to stimulate central respiratory activity are also introduced. #4: Sleep-Wake Mechanisms. Arousability is the fourth therapeutic target rationalized. Evolving automated tools to measure key pathophysiological factors predisposing to OSA will accelerate pharmacotherapy. Although not currently ready for general clinical settings, the identified targets are of future promise.

Endotyping, phenotyping and personalised therapy in obstructive sleep apnoea: are we there yet?

Obstructive sleep apnoea (OSA) was traditionally thought to be mainly caused by obesity and upper airway crowding, and hence OSA management was not personalised according to particular characteristics, with most symptomatic patients receiving continuous positive airway pressure therapy. Recent advances in our understanding have identified additional potential and distinct causes of OSA (endotypes), and subgroups of patients (phenotypes) with increased risk of cardiovascular complications. In this review, we discuss the evidence to date as to whether there are distinct clinically useful endotypes and phenotypes of OSA, and the challenges to the field in moving towards delivering personalised therapy in OSA.

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective

BACKGROUND

The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI.

METHOD

The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature.

RESULTS

Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice.

CONCLUSION

Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

A Scoping Review of Sleep Apnea: Where Do We Stand?

Obstructive sleep apnea (OSA), a condition in which there is a recurrent collapse of the upper airway while sleeping, is a widespread disease affecting 5% to 10% people worldwide. Despite several advances in the treatment modalities for OSA, morbidity and mortality remain a concern. Common symptoms include loud snoring, gasping for air during sleep, morning headache, insomnia, hypersomnia, attention deficits, and irritability. Obese individuals, male gender, older age (65+), family history, smoking, and alcohol consumption are well recognized risk factors of OSA. This condition holds the ability to increase inflammatory cytokines, cause metabolic dysfunction, and increase the sympathetic output, all of which exacerbate OSA due to their effect on the cardiovascular system. In this review, we discuss its brief history, risk factors, complications, treatment modalities, and the role of clinicians in curbing its risk.

Role of precision medicine in obstructive sleep apnoea

Obstructive sleep apnoea is a substantial clinical and public health problem because it contributes to harmful effects on quality of life, daytime symptoms, road traffic incidents, and cardiometabolic disease. Increasingly, obstructive sleep apnoea is recognised as a heterogeneous disease, and patients have varied susceptibility to long term complications and different responses to treatment. This narrative review summarises the current knowledge of precision medicine in obstructive sleep apnoea, particularly the role of symptom clusters, polysomnogram phenotypes, physiological endotypes, and circulating biomarkers in defining subtypes. In the near future, the prognostic accuracy of these measures in predicting long term complications in obstructive sleep apnoea will likely be improved, together with better matching of treatments to disease subtypes.

The effect of hyperoxia on ventilation during recovery from general anesthesia: A randomized pilot study for a parallel randomized controlled trial

STUDY OBJECTIVE

While supplemental O₂ inhalation corrects hypoxemia, its effect on post-anesthesia ventilation remains unknown. This pilot trial tested the hypothesis that hyperoxia increases the time spent with a transcutaneous PCO₂ (TcPCO₂) > 45 mmHg, compared with standard O₂ supplementation.

DESIGN

Single-blinded, parallel two-arm randomized pilot trial.

SETTING

University hospital.

PATIENTS

20 patients undergoing robotic-assisted laparoscopic nephrectomy.

MEASUREMENTS

After institutional approval and informed consent, patients were randomized to receive O₂ titrated to arterial saturation (SpO₂): 90-94% (Conservative O₂, N =10), or to SpO₂ > 96% (Liberal O₂, N = 10) for up to 90 min after anesthesia. Continuous TcPCO₂, respiratory inductance plethysmography (RIP), and SpO₂, were recorded. We calculated the percentage of time at TcPCO₂ > 45 mmHg for each patient and compared the two groups using analysis of covariance, adjusting for sex, age, and body mass index. We also estimated the sample size required to detect the between-group difference observed in this pilot trial. RIP signals were used to calculate apnea/hypopnea index (AHI), which was then compared between two groups.

MAIN RESULTS

The mean percentage of time with a TcPCO₂ > 45 mmHg was 80.6% for the Conservative O₂ (N=9) and 61.2% for the Liberal O₂ (N=10) group [between-group difference of 19.4% (95% CI: -18.7% to 57.6%), P = 0.140]. With an observed effect size of 0.73, we estimated that 30 participants per group are required, to demonstrate this difference with a power of 80% at a two-sided alpha of 5%. Means SpO₂ were 94.5% and 99.9% for the Conservative O₂ and the Liberal O₂ groups, respectively. AHI was significantly higher in the Conservative O₂, compared with the Liberal O₂ group (median AHI: 16 vs. 3; P = 0.0014).

CONCLUSIONS

Hyperoxia in the post-anesthesia period reduced the time spent at TcPCO₂ > 45 mmHg and significantly decreased AHI, while mean SpO₂ ranged inside the a priori defined limits.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04723433.

Topical Potassium Channel Blockage Improves Pharyngeal Collapsibility: A Translational, Placebo-Controlled Trial

BACKGROUND

Potassium channel inhibition has been identified in animal models as a potential target to increase pharyngeal dilator muscle activity and to treat OSA. However, these findings have not yet been translated to humans.

RESEARCH QUESTION

Does a novel, potent, TWIK-related acid-sensitive K⁺ (TASK) 1/3 channel antagonist, BAY2586116, improve pharyngeal collapsibility in pigs and humans, and secondarily, what is the optimal dose and method of topical application?

STUDY DESIGN AND METHODS

In the preclinical study, pharyngeal muscle activity and upper-airway collapsibility via transient negative pressure application was quantified in 13 anesthetized pigs during administration of placebo, 0.3 μg, 3 μg, and 30 μg nasal drops of BAY2586116. In the clinical study, 12 people with OSA instrumented with polysomnography equipment, an epiglottic pressure catheter, pneumotachograph, and nasal mask to monitor sleep and breathing performed up to four detailed upper airway sleep physiology studies. Participants received BAY2586116 or placebo nasal spray (160 μg) before sleep via a double-masked, randomized, crossover design. Most participants also returned for three additional overnight visits: (1) nasal drops (160 μg), (2) half-dose nasal spray (80 μg), and (3) direct endoscopic application (160 μg). The upper-airway critical closing pressure during sleep was quantified at each visit.

RESULTS

Consistent and sustained improvements in pharyngeal collapsibility to negative pressure were found with 3 and 30 μg of BAY2586116 vs placebo in pigs. Similarly, BAY2586116 improved pharyngeal collapsibility by an average of approximately 2 cm H₂O vs placebo, regardless of topical application method and dose (P < .008, mixed model) in participants with OSA.

INTERPRETATION

Acute topical application of BAY2586116 improves upper-airway collapsibility in anesthetized pigs and sleeping humans with OSA. These novel physiologic findings highlight the therapeutic potential to target POTASSIUM channel mechanisms to treat OSA.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT04236440; URL: www.

CLINICALTRIALS

gov.

Point-of-care prediction model of loop gain in patients with obstructive sleep apnea: development and validation

Background

High loop gain (unstable ventilatory control) is an important—but difficult to measure—contributor to obstructive sleep apnea (OSA) pathogenesis, predicting OSA sequelae and/or treatment response. Our objective was to develop and validate a clinical prediction tool of loop gain.

Methods

A retrospective cohort of consecutive adults with OSA (apnea–hypopnea index, AHI > 5/hour) based on in-laboratory polysomnography 01/2017–12/2018 was randomly split into a training and test-set (3:1-ratio). Using a customized algorithm (“reference standard”) loop gain was quantified from raw polysomnography signals on a continuous scale and additionally dichotomized (high > 0.7). Candidate predictors included general patient characteristics and routine polysomnography data. The model was developed (training-set) using linear regression with backward selection (tenfold cross-validated mean square errors); the predicted loop gain of the final linear regression model was used to predict loop gain class. More complex, alternative models including lasso regression or random forests were considered but did not meet pre-specified superiority-criteria. Final model performance was validated on the test-set.

Results

The total cohort included 1055 patients (33% high loop gain). Based on the final model, higher AHI (beta = 0.0016; P < .001) and lower hypopnea-percentage (beta = −0.0019; P < .001) predicted higher loop gain values. The predicted loop gain showed moderate-to-high correlation with the reference loop gain (r = 0.48; 95% CI 0.38–0.57) and moderate discrimination of patients with high versus low loop gain (area under the curve = 0.73; 95% CI 0.67–0.80).

Conclusion

To our knowledge this is the first prediction model of loop gain based on readily-available clinical data, which may facilitate retrospective analyses of existing datasets, better patient selection for clinical trials and eventually clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01950-y.

Early objective adherence to hypoglossal nerve stimulation therapy

STUDY OBJECTIVES

To assess early adherence to therapy with hypoglossal nerve stimulation therapy.

METHODS

This is a prospective study of consecutive patients with moderate to severe obstructive sleep apnea who underwent implantation of hypoglossal nerve stimulation therapy within a single academic practice and attended a follow-up appointment after greater than 30 days of therapy use. Objective adherence data were extracted from an objective monitoring database and compared to patient characteristics.

RESULTS

The study population was 79 participants who were 29.1% female with a mean age of 58.7 ± 12.8 years old, body mass index of 28.9 ± 3.4 kg/m², and baseline apnea-hypopnea index of 33.8 ± 17.6 events/h. In the first 7 days after device activation, average use was 7.8 h/night, with 91.9% of nights with greater than 4 hours of therapy use and an average of 0.2 pauses in therapy per night. These figures remained stable after 30 days of use: 7.7 h/night, 91.0% of nights longer than 4 hours, and 0.3 pauses per night. Objective evidence of difficulty with acclimatization was associated with age less than 60 years (odds ratio 2.8, 95% confidence interval 1.1-7.1, P = .03) and a history of prior upper airway surgery (3.9, 1.2-11.9, P = .015). Insomnia was present in 31 patients and was not associated with objective evidence of difficulty tolerating therapy.

CONCLUSIONS

Early adherence to hypoglossal nerve stimulation is excellent (92.4% >4 hours on >70% of nights), suggesting that the acclimatization period is straightforward in most. Younger age and a history of prior upper airway surgery appear to be associated with an increased risk of difficulty with acclimatization.

CITATION

Huyett P. Early objective adherence to hypoglossal nerve stimulation therapy. J Clin Sleep Med. 2022;18(2):631-636.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).

The noradrenergic agent reboxetine plus the antimuscarinic hyoscine butylbromide reduces sleep apnoea severity: a double-blind, placebo-controlled, randomised crossover trial

KEY POINTS

Recent animal and human physiology studies indicate that noradrenergic and muscarinic processes are key mechanisms that mediate pharyngeal muscle control during sleep. The noradrenergic agent reboxetine combined with the anti-muscarinic hyoscine butylbromide has recently been shown to improve upper airway function during sleep in healthy individuals. However, whether these findings translate to the clinically relevant patient population of people with obstructive sleep apnoea (OSA), and the effects of the agents on OSA severity, are unknown. We found that reboxetine plus hyoscine butylbromide reduced OSA severity, including overnight hypoxaemia, via increases in pharyngeal muscle responsiveness, improvements in respiratory control and airway collapsibility without changing the respiratory arousal threshold. These findings provide mechanistic insight into the role of noradrenergic and anti-muscarinic agents on upper airway stability and breathing during sleep and are important for pharmacotherapy development for OSA.

ABSTRACT

The noradrenergic agent reboxetine combined with the anti-muscarinic hyoscine butylbromide has recently been shown to improve upper airway function during sleep in healthy individuals. However, the effects of this drug combination on obstructive sleep apnoea (OSA) severity are unknown. Accordingly, this study aimed to determine if reboxetine plus hyoscine butylbromide reduces OSA severity. Secondary aims were to investigate the effects on key upper airway physiology and endotypic traits. Twelve people with OSA aged 52 ± 13 years, BMI = 30 ± 5 kg/m² , completed a double-blind, randomised, placebo-controlled, crossover trial (ACTRN12617001326381). Two in-laboratory sleep studies with nasal mask, pneumotachograph, epiglottic pressure sensor and bipolar fine-wire electrodes into genioglossus and tensor palatini muscles were performed separated by approximately 1 week. Each participant received either reboxetine (4 mg) plus hyoscine butylbromide (20 mg), or placebo immediately prior to sleep. Polysomnography, upper airway physiology and endotypic estimates of OSA were compared between conditions. Reboxetine plus hyoscine butylbromide reduced the apnoea/hypopnoea index by (mean ± SD) 17 ± 17 events/h from 51 ± 30 to 33 ± 22 events/h (P = 0.005) and nadir oxygen saturation increased by 6 ± 5% from 82 ± 5 to 88 ± 2% (P = 0.002). The drug combination increased tonic genioglossus muscle responsiveness during non-REM sleep (median [25th, 75th centiles]: -0.007 [-0.0004, -0.07] vs. -0.12 [-0.02, -0.40] %maxEMG/cmH₂ O, P = 0.02), lowered loop gain (0.43 ± 0.06 vs. 0.39 ± 0.07, P = 0.01), and improved airway collapsibility (90 [69, 95] vs. 93 [88, 96] %eupnoea, P = 0.02), without changing the arousal threshold (P = 0.39). These findings highlight the important role that noradrenergic and muscarinic processes have on upper airway function during sleep and the potential for pharmacotherapy to target these mechanisms to treat OSA.