P4 medicine approach to obstructive sleep apnoea

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.

An Overview of Systematic Reviews on the Surgical Management of Obstructive Sleep Apnoea

Background

Obstructive Sleep Apnoea (OSA) is a common sleep disorder marked by partial or total obstruction of the upper airway while a person is asleep leading to breathing difficulty, reduced oxygenation and frequent awakenings. This condition affects the general health significantly compromising quality of life. The objective of this overview  is to thoroughly assess the systematic reviews on current surgical therapies for the management of OSA in terms of patient outcomes.

Methods

A thorough literature search was performed from inception till 31st December 2022 using PubMed, and Cochrane databases. Studies evaluating the effectiveness and safety of different surgical techniques for the management of OSA were considered. The quality of articles was assessed using AMSTAR (A MeaSurement Tool to Assess systematic Reviews) and Glenny et al. checklist.

Results

Out of eighteen studies, only seven studies met the inclusion and exclusion criteria. Results showed that the majority of studies were in the pediatric age group except one systematic  review which assessed the adult age group.

Conclusion

None of the published articles had compared all surgical procedures based on the standard evaluating procedure nor followed all reporting guidelines in the primary studies. For better implementation, further multi center studies are warranted with unique reporting criteria and guidelines about pre- as well as post-operative phases. Future research should concentrate on contrasting strategies, combination therapies, and evaluating long-term effects.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12663-023-02051-x.

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.

Prevalence of mental illness in patients with obstructive sleep apnea – A cross-sectional study from Kashmir, India

Aim

The study aimed to evaluate the prevalence of mental illness in obstructive sleep apnea (OSA) and to examine whether patients with obstructive sleep apnea require screening for mental illness.

Methods

We performed polysomnography studies of patients that were referred from various subspecialty clinics in Kashmir from Jan 2020–December 2020. using the Mini-International Neuropsychiatric Interview (MINI plus) scale to make a psychiatric diagnosis. We administered the General Health Questionnaire – 28 (GHQ – 28), Hamilton Depression Rating Scale (HAM-D), and Hamilton Anxiety Rating Scale (HAM-A) to patients. Descriptive statistics and correlations were used for data analysis.

Results

182 patients underwent polysomnography, 85 (46.7%) of which were suffering from mental illness Based on the Apnea-Hypopnea Index score, 8 (4.39%) patients had mild, 35 (41.1%) had moderate and 42 (49.4%) had severe OSA. The mean GHQ -28 score was significantly higher in patients with Obstructive sleep apnea (p < 0.001) (11.34 ± 8.2) as compared to non-Obstructive sleep apnea patients (1.98 ± 4.38).

Conclusions

This study demonstrates the increased prevalence of anxiety and depression in patients with OSA. Therefore, we recommend timely screening for any mental health issues in patients with OSA and necessary interventions to address the issues, thus preventing mental health morbidities in patients with OSA this would help subsequently in an improved lifestyle.

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This study highlighted the prevalence of mental illness in obstructive sleep apnea (OSA).

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Almost one-half of the patients diagnosed with OSA had mental illness.

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Appropriate measures should be undertaken to control prevalence of anxiety and depression in patients with OSA.

Current and Novel Treatment Options for OSA

Obstructive sleep apnoea is a challenging medical problem due to its prevalence, its impact on quality of life and performance in school and professionally, the implications for risk of accidents, and comorbidities and mortality. Current research has carved out a broad spectrum of clinical phenotypes and defined major pathophysiological components. These findings point to the concept of personalised therapy, oriented on both the distinct clinical presentation and the most relevant pathophysiology in the individual patient. This leads to questions of whether sufficient therapeutic options other than positive airway pressure (PAP) alone are available, for which patients they may be useful, if there are specific indications for single or combined treatment, and whether there is solid scientific evidence for recommendations. This review describes our knowledge on PAP and non-PAP therapies to address upper airway collapsibility, muscle responsiveness, arousability and respiratory drive. The spectrum is broad and heterogeneous, including technical and pharmaceutical options already in clinical use or at an advanced experimental stage. Although there is an obvious need for more research on single or combined therapies, the available data demonstrate the variety of effective options, which should replace the unidirectional focus on PAP therapy.

The analysis of individual pathophysiological composition opens new directions towards personalised treatment of OSA, focusing not only on pharyngeal dilation, but also on technical or pharmaceutical interventions on muscle function or breathing regulationhttps://bit.ly/3sayhkd

COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States

The aim of our study was to determine COVID-19 syndromic phenotypes in a data-driven manner using the survey results based on survey results from Carnegie Mellon University’s Delphi Group. Monthly survey results (>1 million responders per month; 320,326 responders with a certain COVID-19 test status and disease duration <30 days were included in this study) were used sequentially in identifying and validating COVID-19 syndromic phenotypes. Logistic Regression-weighted multiple correspondence analysis (LRW-MCA) was used as a preprocessing procedure, in order to weigh and transform symptoms recorded by the survey to eigenspace coordinates, capturing a total variance of >75%. These scores, along with symptom duration, were subsequently used by the Two Step Clustering algorithm to produce symptom clusters. Post-hoc logistic regression models adjusting for age, gender, and comorbidities and confirmatory linear principal components analyses were used to further explore the data. Model creation, based on August’s 66,165 included responders, was subsequently validated in data from March−December 2020. Five validated COVID-19 syndromes were identified in August: 1. Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS); 2. Febrile (100%) Multisymptomatic (FMS); 3. Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS); 4. Oligosymptomatic with additional self-described symptoms (100%; OSDS); 5. Olfaction/Gustatory Impairment Predominant (100%; OGIP). Our findings indicate that the COVID-19 spectrum may be undetectable when applying current disease definitions focusing on respiratory symptoms alone.

The role of orthodontics in management of obstructive sleep apnea

Dental sleep medicine is the field of dental practice that deals with the management of sleep-related breathing disorders, which includes obstructive sleep apnea (OSA) in adults and children. Depending on the developmental age of the patient and the cause of the apnea dental treatment options may vary. For adult patients, treatment modalities may include oral appliance therapy (OAT), orthognathic surgery and surgical or miniscrew supported palatal expansion. While for children, treatment may include non-surgical maxillary expansion and orthodontic functional appliances. Many physicians and dentists are unaware of the role dentistry, particularly orthodontics, may play in the interdisciplinary management of these disorders. This review article is an attempt to compile evidence-based relevant information on the role of orthodontists/sleep dentists in the screening, diagnosis, and management of sleep apnea. Oral sleep appliance mechanisms of action, selective efficacy, and the medical physiological outcomes are discussed. The purpose of this review is to provide a comprehensive understanding of how orthodontists and sleep physicians can work in tandem to maximize the benefits and minimize the side effects while treating patients with OSA.

Obstructive Sleep Apnea compliance: verifications and validations of personalized interventions for PAP therapy

The Positive Airway Pressure (PAP) therapy is the most capable therapy against Obstruction Sleep Apnea (OSA). PAP therapy prevents the narrowing and collapsing of the soft tissues of the upper airway. A patient diagnosed with OSA is expected to use their CPAP machines every night for at least more than 4h for experiencing any clinical improvement. However, for the last two decades, trials were carried out to improve compliance and understand factors impacting compliance, but there were not enough conclusive results. With the advent of big data analytic and real-time monitoring, new opportunities open up to tackle this compliance issue. This paper's significant contribution is a novel framework that blends multiple external verification and validation carried out by different healthcare stakeholders. We provide a systematic verification and validation process to push towards explainable data analytic and automatic learning processes. We also present a complete mHealth solution that includes two mobile applications. The first application is for delivering tailored interventions directly to the patients. The second application is bound to different healthcare stakeholders for the verification and validation process.

Treatment usage patterns of oral appliances for obstructive sleep apnea over the first 60 days: a cluster analysis

STUDY OBJECTIVES

Oral appliance (OA) therapy usage can be objectively measured through temperature-sensing data chips embedded in the appliance. Initial reports of group data for short-term treatment usage suggest good nightly hours of usage. However, individual variability in treatment usage patterns has not been assessed. We aimed to identify OA treatment usage subtypes in the first 60 days and the earliest predictors of these usage patterns.

METHODS

OSA patients were recruited for a study of OA therapy with an embedded compliance chip (DentiTrac, Braebon, Canada). Fifty-eight participants with 60 days of downloadable treatment usage data (5-minute readings) were analyzed. A hierarchical cluster analysis was used to group participants with similar usage patterns. A random forest classification model was used to identify the minimum number of days to predict usage subtype.

RESULTS

Three user groups were identified and named "consistent users" (48.3%), "inconsistent users" (32.8%) and "non-users" (19.0%). The first twenty days provided optimal data to predict which treatment usage group a patient would belong to at 60-days (90% accuracy). The strongest predictors of user group were downloaded usage data; average wear time and number of days missed.

CONCLUSIONS

Granular analysis of OA usage data suggests the existence of treatment user subtypes (consistent, inconsistent and non-users). Our data suggest that 60-day usage patterns can be identified in the first twenty days of treatment using downloaded treatment usage data. Understanding initial treatment usage patterns provide an opportunity for early intervention to improve long-term usage and outcomes.

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.

Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Unsupervised clustering models have been widely used for multimetric phenotyping of complex and heterogeneous diseases such as diabetes and obstructive sleep apnea (OSA) to more precisely characterize the disease beyond simplistic conventional diagnosis standards. However, the number of clusters and key phenotypic features have been subjectively selected, reducing the reliability of the phenotyping results. Here, to minimize such subjective decisions for highly confident phenotyping, we develop a multimetric phenotyping framework by combining supervised and unsupervised machine learning. This clusters 2277 OSA patients to six phenotypes based on their multidimensional polysomnography (PSG) data. Importantly, these new phenotypes show statistically different comorbidity development for OSA-related cardio-neuro-metabolic diseases, unlike the conventional single-metric apnea–hypopnea index-based phenotypes. Furthermore, the key features of highly comorbid phenotypes were identified through supervised learning rather than subjective choice. These results can also be used to automatically phenotype new patients and predict their comorbidity risks solely based on their PSG data. The phenotyping framework based on the combination of unsupervised and supervised machine learning methods can also be applied to other complex, heterogeneous diseases for phenotyping patients and identifying important features for high-risk phenotypes.

Molecular determinants of obstructive sleep apnea

Obstructive sleep apnea (OSA) is characterized as recurrent episodes of obstruction in the upper airway during the period of sleep. The condition occurs in approximately 11% and 4% of middle-aged men and middle-aged women, respectively. Polysomnography is a diagnostic procedure that involves the constant observation of oxygen saturation and unsaturation during sleep. Usually, positive airway pressure is considered a benchmark treatment for OSA. This review summarizes the recent developments and emerging evidence from molecular biology-based research studies that show that genetic factors have an influence on OSA. The genetic aspects of OSA that have been identified include heritability and other phenotypic co-factors such as anatomical morphology. It also draws attention to the results of a polymorphic-based study that was conducted to determine the causative single nucleotide mutations associated with obesity and adverse cardiovascular risk in OSA. However, the role of such mutations and their linkage to OSA can not yet be established. Nonetheless, a large body of evidence supports a strong association between inflammatory cytokine polymorphism and obesity in the development of OSA. There are also probable intermediate factors with several gene-gene interactions. Therefore, advanced applications and modern techniques should be applied to facilitate new findings and to minimize the risk of developing OSA.

Personalized and Patient-Centered Strategies to Improve Positive Airway Pressure Adherence in Patients with Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a common sleep disorder characterized by repeated pauses in breathing during sleep, is effectively treated with positive airway pressure (PAP) therapy. The magnitude of improvements in daily functioning and reduced negative health risks are dependent on maintaining PAP adherence, which is a significant challenge. Evidence-based interventions to improve PAP use are not easily translated to clinical practice because they are labor-intensive and require specialty expertise. Further, to date, individualized care, inclusive of personalized medicine and patient- and person-centered care have been marginally incorporated in the field's understanding of OSA and PAP adherence. This integrative review describes current PAP adherence assessment processes, interventions to improve adherence, and outlines future opportunities to advance the field, particularly as it relates to individualizing care and the use of implementation science to apply evidence to practice.

[Digital respiratory sleep medicine-part I: diagnosis]

Neuartige Methoden aus dem Bereich E‑Health bieten bereits heutzutage smarte Lösungen für aktuell noch unzureichend adressierte Probleme im Bereich der schlafbezogenen Atmungsstörungen. Telemedizinische Konzepte wie z. B. die Videosprechstunde helfen dabei, betroffene Patienten frühzeitig einer schlafmedizinischen Expertise zuzuführen. Cloudbasierte Diagnostik kann helfen, im Rahmen der etablierten Stufendiagnostik bestehende Schnittstellenprobleme zu reduzieren und die interdisziplinäre Kommunikation zu verbessern. Neue Sensorik sowie Applikationen für digitale Endgeräte (Apps) eröffnen Möglichkeiten für ein breites Screening auf die Volkserkrankung Schlafapnoe. Um diese überhaupt klinisch verwenden zu können, müssen diese neuartigen Methoden jedoch als Medizinprodukte zertifiziert werden. Das Digitale Versorgungsgesetz (DVG) eröffnet die Möglichkeit zur Aufnahme von Gesundheits-Apps in das Verzeichnis der Digitalen Gesundheitsanwendungen (DiGA) nach Bewertung durch das Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM). Diese „Apps auf Rezept“ sind dann durch die gesetzliche Krankenversicherung erstattungsfähig. Die erhobenen komplexen Datenmengen können nur mittels computerbasierter Analysemethoden auf Zusammenhänge und Muster untersucht werden. Diese Erkenntnisse sollen nicht die ärztliche Heilkunst ersetzen, sondern den behandelnden Ärzten unterstützend in ihren Entscheidungsfindungen helfen – im Sinne einer wirklichen Präzisionsmedizin unter Partizipation des Patienten. Die Datenhoheit hat dabei zu jeder Zeit der aufgeklärte Patient inne.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

Identifying the Predominant Site of Upper Airway Collapse in Obstructive Sleep Apnoea Patients Using Snore Signals

Knowledge regarding the site of airway collapse could help in choosing an appropriate structure-specific or individualized treatment for obstructive sleep apnoea (OSA). We investigated if the audio signal recorded during hypopnoea (partial obstruction) events can predict the site-of-collapse of the upper airway. In this study, we designed an automatic classifier that predicts the predominant site of upper airway collapse for a patient as "lateral wall", "palate", "tongue-based" related collapse or "multi-level" site-of-collapse by processing of the audio signal. The probable site-of-collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea, which has been reported to correlate with the site of collapse. Audio signal was recorded simultaneously with full-night polysomnography during sleep with a ceiling microphone. Various time and frequency features of the audio signal were extracted to classify the audio signal into lateral wall, palate and tongue-base related collapse. We introduced an unbiased process using nested leave-one patient-out cross-validation to choose the optimal features. The classification was carried out with a multi-class linear discriminant analysis classifier. Performance of the proposed model showed that our automatic system can achieve an overall accuracy of 65% for determining the predominant site-of-collapse for all site-of-collapse classes and an accuracy of 80% for classifying tongue/non-tongue related collapse. Our results indicate that the audio signal recorded during sleep can be helpful in identifying the site-of-collapse and therefore could potentially be used as a new tool for deciding appropriate treatment for OSA.

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.

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.

A New Metric for Precision Medicine: PAP and Hypoglossal Neurostimulation

CITATION

Jacobowitz O, Woodson BT. A new metric for precision medicine: PAP and hypoglossal neurostimulation. J Clin Sleep Med. 2019;15(8):1079-1080.

Further Development of P4 Approach to Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a heterogeneous disorder. Cluster analysis has identified different physiologic subtypes with respect to symptoms. A difference exists in cardiovascular risk from OSA between the 7 subtypes identified. There are 3 basic subtypes replicated in multiple studies: (a) a group where insomnia is the main symptom; (b) an asymptomatic group; (c) a group with marked excessive sleepiness. The symptomatic benefit from treatment with nasal CPAP varies between these 3 subtypes. Data from the Sleep Heart Health Study reveal that the increased risk of cardiovascular disease from OSA occurs only in the excessively sleepy group.

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co-morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information-rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co-morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

Connexins and Atrial Fibrillation in Obstructive Sleep Apnea

PURPOSE OF THE REVIEW

To summarize the potential interactions between obstructive sleep apnea (OSA), atrial fibrillation (AF), and connexins.

RECENT FINDINGS

OSA is highly prevalent in patients with cardiovascular disease, and is associated with increased risk for end-organ substantial morbidities linked to autonomic nervous system imbalance, increased oxidative stress and inflammation, ultimately leading to reduced life expectancy. Epidemiological studies indicate that OSA is associated with increased incidence and progression of coronary heart disease, heart failure, stroke, as well as arrhythmias, particularly AF. Conversely, AF is very common among subjects referred for suspected OSA, and the prevalence of AF increases with OSA severity. The interrelationships between AF and OSA along with the well-known epidemiological links between these two conditions and obesity may reflect shared pathophysiological pathways, which may depend on the intercellular diffusion of signaling molecules into either the extracellular space or require cell-to-cell contact. Connexin signaling is accomplished via direct exchanges of cytosolic molecules between adjacent cells at gap membrane junctions for cell-to-cell coupling. The role of connexins in AF is now quite well established, but the impact of OSA on cardiac connexins has only recently begun to be investigated. Understanding the biology and regulatory mechanisms of connexins in OSA at the transcriptional, translational, and post-translational levels will undoubtedly require major efforts to decipher the breadth and complexity of connexin functions in OSA-induced AF.

SUMMARY

The risk of end-organ morbidities has initiated the search for circulating mechanistic biomarker signatures and the implementation of biomarker-based algorithms for precision-based diagnosis and risk assessment. Here we summarize recent findings in OSA as they relate to AF risk, and also review potential mechanisms linking OSA, AF and connexins.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

OBJECTIVE

In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

SIGNIFICANCE

Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

Improvement in obstructive sleep apnea after a tailored behavioural sleep medicine intervention targeting healthy eating and physical activity: a randomised controlled trial

Purpose

The aim of the present single-centre randomised controlled trial was to assess the effect of a behavioural sleep medicine (BSM) intervention on obstructive sleep apnea (OSA) severity in patients who have been referred for new treatment with continuous positive airway pressure (CPAP).

Methods

After baseline assessment including ventilatory and anthropometric parameters, and physical activity monitoring, 86 patients who were overweight (BMI ≥ 25) and had moderate-severe OSA with apnea-hypopnea index (AHI) ≥ 15 were randomised into a control group (CG; CPAP and advice about weight loss) or an experimental group (ExpG; CPAP and BSM intervention targeting physical activity and eating behaviour). The BSM intervention comprised 10 individual sessions with a dietician and a physiotherapist and included behaviour change techniques such as goal setting and self-monitoring. After 6 months, a new recording of ventilatory parameters was performed without CPAP.

Results

In ExpG, 40% (n = 14) had improved from severe to moderate or mild OSA or from moderate to mild OSA compared to 16.7% in CG (n = 6, p = 0.02). Further, a lower AHI and amount body fat at baseline were correlated with improvement in severity class. Being in ExpG implied a mean improvement in AHI by 9.7 and an odds ratio of 4.5 for improving in severity classification.

Conclusions

The results highlight the clinical importance of lifestyle modifications in conjunction with CPAP treatment in patients with OSA.

Personalised medicine in sleep respiratory disorders: focus on obstructive sleep apnoea diagnosis and treatment

In all fields of medicine, major efforts are currently dedicated to improve the clinical, physiological and therapeutic understanding of disease, and obstructive sleep apnoea (OSA) is no exception. The personalised medicine approach is relevant for OSA, given its complex pathophysiology and variable clinical presentation, the interactions with comorbid conditions and its possible contribution to poor outcomes. Treatment with continuous positive airway pressure (CPAP) is effective, but CPAP is poorly tolerated or not accepted in a considerable proportion of OSA patients. This review summarises the available studies on the physiological phenotypes of upper airway response to obstruction during sleep, and the clinical presentations of OSA (phenotypes and clusters) with a special focus on our changing attitudes towards approaches to treatment. Such major efforts are likely to change and expand treatment options for OSA beyond the most common current choices (i.e CPAP, mandibular advancement devices, positional treatment, lifestyle changes or upper airway surgery). More importantly, treatment for OSA may become more effective, being tailored to each patient's need.

Obstructive sleep apnea: in search of precision

Introduction

Obstructive sleep apnea (OSA) is a highly prevalent condition that is viewed as a major global health concern, while affecting approximately 10% of the middle-aged population. OSA is a chronic disease that has been conclusively associated with poor quality of life, cognitive impairments and mood alterations, enhanced cardiovascular and metabolic morbidity, thereby leading to marked increments in healthcare costs.

Areas covered

The authors have reviewed the current evidence on the pathophysiology of OSA and its consequences, the heterogeneity of its phenotypic expression, the current therapeutic applications and their efficacy, and the implications for diagnosis, treatment and follow-up strategies in the context of the clinical management of OSA.

Expert commentary

Personalized medicine in OSA identifies different needs and approaches: i) phenotyping and defining the different and segregated clusters of OSA patients whose recognition may improve prognostic predictions and guide therapeutic strategies; ii) to further characterize and predict the impact of OSA and its treatment, particularly revolving around mortality and the processes closely related to ageing (cardiovascular diseases, cancer and neurocognitive diseases); iii) the introduction of new technologies including telemedicine that have shown promise in the implementation of personalized medicine approaches.