The Role of Big Data in the Management of Sleep-Disordered Breathing

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.

Big data curation framework: Curation actions and challenges

Big data curation represents an emerging topic of inquiry but still in an early phase along its adoption curve. The term big data itself is a nebulous concept, and the differences between small data curation and big data curation are nuanced. The goal of this research is to provide a theoretical framework that identifies big data curation actions and associated curation challenges. This study is based on the practices of big data research and data curation by systematically examining literature. The outcome of the study includes the big data curation framework that provides overview of curation activities and concerns that are essential to perform such activities. The study also provides practical implications for libraries, archives, data repositories and other information organisations that concerns the issue of big data curation as big data presents a multidimensional array of exigencies in relation to the mission of those organisations.

Telemedicine in Sleep-Disordered Breathing: Expanding the Horizons

Interest in telemedicine has increased exponentially. There is a growing body of published evidence on the use of telemedicine for patients using continuous positive airway pressure. Telemedicine-ready devices can support the transmission on use time, apnea-hypopnea index, and leakage. This approach enables early activation of troubleshooting. Automated, personalized feedback for patients and patient access to their own data provide unprecedented opportunities for integrating comanagement approaches, multiactor interactions, and patient empowerment. Telemedicine is likely cost effective, but requires better evidence. Notwithstanding barriers for implementation that remain, telemedicine has to be embraced, leaving the physician and patient to accept it or not.

Incidence of hypertension in obstructive sleep apnea using hypopneas defined by 3 percent oxygen desaturation or arousal but not by only 4 percent oxygen desaturation

STUDY OBJECTIVES

This analysis determined ∼5-year incident hypertension rates using the 2017 American College of Cardiology/American Heart Association blood pressure (BP) guidelines in individuals with obstructive sleep apnea (OSA) with hypopneas defined by a ≥ 3% oxygen desaturation or arousal but not by a hypopnea criterion of ≥ 4% oxygen desaturation (4% only).

METHODS

Data were analyzed from participants in the Sleep Heart Health Study exam 2 (n = 1219) who were normotensive (BP ≤ 120/80 mm Hg) at exam 1. The AHI at exam 1 was classified into 4 categories of OSA severity: < 5, 5 ≤ 15, 15 ≤ 30, and ≥ 30 events/h using both the 3% oxygen desaturation or arousal and the 4% only definitions. Three definitions of hypertension-elevated BP (> 120/80 mm Hg), stage 1 (> 130/80 mm Hg), and stage 2 (> 140/90 mm Hg)-were used to determine incidence rates at exam 2.

RESULTS

Five-year follow-up was available for 476 participants classified as having OSA by the 3% oxygen desaturation or arousal criterion but not by the 4% only standard at exam 1. Incident hypertension using American College of Cardiology/American Heart Association-defined BP categories in these discordantly classified individuals were 15% (elevated BP), 15% (stage 1), and 6% (stage 2). Hypertensive medications were used in 4% of participants who were normotensive. The overall incidence rate of at least an elevated BP was 40% (191/476) in those with OSA defined using the 3% oxygen desaturation or arousal criterion but not by the 4% only criterion.

CONCLUSIONS

Use of the 4% only hypopnea definition resulted in the failure to identify a significant number of individuals with OSA who eventually developed hypertension and could have benefited from earlier diagnosis and treatment.

Small sleepers, big data: leveraging big data to explore sleep-disordered breathing in infants and young children

STUDY OBJECTIVES

Infants represent an understudied minority in sleep-disordered breathing (SDB) research and yet the disease can have a significant impact on health over the formative years of neurocognitive development that follow. Herein we report data on SDB in this population using a big data approach.

METHODS

Data were abstracted using the Cerner Health Facts database. Demographics, sleep diagnoses, comorbid medication conditions, healthcare utilization, and economic outcomes are reported.

RESULTS

In a cohort of 68.7 million unique patients, over a 9-year period, there were 9,773 infants and young children with a diagnosis of SDB (obstructive sleep apnea [OSA], nonobstructive sleep apnea, and "other" sleep apnea) who met inclusion criteria, encompassing 17,574 encounters, and a total of 27,290 diagnoses across 62 U.S. health systems, 172 facilities, and 3 patient encounter types (inpatient, clinic, and outpatient). Thirty-nine percent were female. Thirty-nine percent were ≤1 year of age (6,429 infants), 50% were 1-2 years of age, and 11% were 2 years of age. The most common comorbid diagnoses were micrognathia, congenital airway abnormalities, gastroesophageal reflux, chronic tonsillitis/adenoiditis, and anomalies of the respiratory system. Payor mix was dominated by government-funded entities.

CONCLUSIONS

We have used a novel resource, large-scale aggregate, de-identified EHR data, to examine SDB. In this population, SDB is multifactorial, closely linked to comorbid medical conditions and may contribute to a significant burden of healthcare costs. Further research focusing on infants at highest risk for SDB can help target resources and facilitate personalized management.

Evaluation and Management of Adults with Obstructive Sleep Apnea Syndrome

Obstructive sleep apnea syndrome (OSAS) is a common and underdiagnosed medical condition characterized by recurrent sleep-dependent pauses and reductions in airflow. While a narrow, collapsible oropharynx plays a central role in the pathophysiology of OSAS, there are other equally important nonanatomic factors including sleep-stage dependent muscle tone, arousal threshold, and loop gain that drive obstructive apneas and hypopneas. Through mechanisms of intermittent hypoxemia, arousal-related sleep fragmentation, and intrathoracic pressure changes, OSAS impacts multiple organ systems. Risk factors for OSAS include obesity, male sex, age, specific craniofacial features, and ethnicity. The prevalence of OSAS is rising due to increasing obesity rates and improved sensitivity in the tools used for diagnosis. Validated questionnaires have an important but limited role in the identification of patients that would benefit from formal testing for OSA. While an in-laboratory polysomnography remains the gold standard for diagnosis, the widespread availability and accuracy of home sleep apnea testing modalities increase access and ease of OSAS diagnosis for many patients. In adults, the most common treatment involves the application of positive airway pressure (PAP), but compliance continues to be a challenge. Alternative treatments including mandibular advancement device, hypoglossal nerve stimulator, positional therapies, and surgical options coupled with weight loss and exercise offer possibilities of an individualized personal approach to OSAS. Treatment of symptomatic patients with OSAS has been found to be beneficial with regard to sleep-related quality of life, sleepiness, and motor vehicle accidents. The benefit of treating asymptomatic OSA patients, particularly with regard to cardiovascular outcomes, is controversial and more data are needed.

Outpatient health care utilization for sleep disorders in the Cerner Health Facts database

STUDY OBJECTIVES

Sleep disorders are common in the general population. This study aimed to identify direct health care utilization for sleep disorders using big data through the Cerner Health Facts database.

METHODS

The Cerner Health Facts database has 68.7 million patients in the data warehouse, documenting approximately 506.9 million encounters from 100 nonaffiliated health care systems. To identify sleep-related health care utilization, we examined the frequency of outpatient encounters related to sleep disorders between the years 2000 and 2017. Sleep disorders were grouped-based on the International Classification of Sleep Disorders-Third Edition.

RESULTS

Approximately 20.5 million patients were identified with a total of 127.4 million outpatient encounters. In pediatric patients (ages 0-18 years), health care utilization for major sleep diagnoses was measured per 100,000 encounters. Sleep-related breathing disorders ranked first among common sleep disorders for pediatric patients followed by parasomnia, insomnia, sleep movement disorders, hypersomnolence, then circadian rhythm disorders (820.1, 258.1, 181.6, 68.3, 48.1, and 16.2 per 100,000 encounters, respectively). However, in adult patients, the ranking was slightly different, with sleep-related breathing disorders ranked first, followed by insomnia, sleep-related movement disorders, hypersomnolence, parasomnia, then circadian rhythm disorders (1352.6, 511.6, 166.3, 79.1, 25.7, and 4.2 per 100,000 encounters, respectively). In general, there was a bimodal pattern with a clear dip in sleep-related health care utilization in young adults age (age 19-29 years), with the exception of insomnia.

CONCLUSIONS

Patients with sleep disorders show relatively low health care utilization despite a known high prevalence of sleep disorders in the general population. This finding may highlight under-recognition of sleep problems or decreased access to health care for sleep disorders. In addition, this study highlights the effect of age-based variation on different sleep disorders, which may have an impact on allocating resources.

[Respiratory Disease in the Era of Big Data]

One of the key elements of medicine in the second decade of the 21st century is the exponential growth of patient-produced information, due not only to the transition to the digitization of medical records, but also to the emergence of new sources of information and the capacity for analysis and interpretation of existing ones. The amount of medical information is expected to double every 2 years, which means that there will be 50 times more information available in 2020 than in 2011. In this setting, these large amounts of data or «big data» must be properly managed to implement new initiatives that improve the diagnosis, treatment, and prognosis of patients on the path to personalized medicine.The concept of personalization or precision medicine is of special interest in chronic respiratory disease. In recent years, research in entities such as asthma, COPD, cancer, or SAHS has focused on the identification of genomic, molecular, metabolic, and protein changes (biomarkers). Big data analysis tools can be used to move on from models based on the mean response to treatment, which are suboptimal for most patients, to focus on the individualized response. Part of this journey involves systems medicine, which also integrates clinical and population data to provide a multidimensional view of the disease and help identify causal associations that are usually only evident on big data analysis.

Understanding the pathophysiological mechanisms of cardiometabolic complications in obstructive sleep apnoea: towards personalised treatment approaches

In January 2019, a European Respiratory Society research seminar entitled "Targeting the detrimental effects of sleep disturbances and disorders" was held in Dublin, Ireland. It provided the opportunity to critically review the current evidence of pathophysiological responses of sleep disturbances, such as sleep deprivation, sleep fragmentation or circadian misalignment and of abnormalities in physiological gases such as oxygen and carbon dioxide, which occur frequently in respiratory conditions during sleep. A specific emphasis of the seminar was placed on the evaluation of the current state of knowledge of the pathophysiology of cardiovascular and metabolic diseases in obstructive sleep apnoea (OSA). Identification of the detailed mechanisms of these processes is of major importance to the field and this seminar offered an ideal platform to exchange knowledge, and to discuss pitfalls of current models and the design of future collaborative studies. In addition, we debated the limitations of current treatment strategies for cardiometabolic complications in OSA and discussed potentially valuable alternative approaches.

Telehealth, Telemedicine, and Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) telehealth management may improve initial and chronic care access, time to diagnosis and treatment, between-visit care, e-communications and e-education, workflows, costs, and therapy outcomes. OSA telehealth options may be used to replace or supplement none, some, or all steps in the evaluation, testing, treatments, and management of OSA. All telehealth steps must adhere to OSA guidelines. OSA telehealth may be adapted for continuous positive airway pressure (CPAP) and non-CPAP treatments. E-data collection enhances uses for individual and group analytics, phenotyping, testing and treatment selections, high-risk identification and targeted support, and comparative and multispecialty therapy studies.

Prevalence and patterns of tongue deformation in obstructive sleep apnea: A whole-night simultaneous ultrasonographic and polysomnographic study

Tongue deformation during whole-night natural sleep in adult patients with obstructive sleep apnea has not been well evaluated. Through simultaneous ultrasonography and polysomnography during whole-night sleep, we examined the prevalence and patterns of tongue depth changes and their relationship with the severity of obstructive sleep apnea. Sixty consecutive eligible adults presenting with symptoms suggesting obstructive sleep apnea were enrolled. We observed that 88.4% (38/43) of patients with obstructive sleep apnea exhibited a significant increase in the maximum ultrasonographic tongue depth when hypopnea or apnea occurred during sleep. A mixed-model analysis of variance demonstrated that compared with patients with primary snoring or mild obstructive sleep apnea, those with moderate to severe obstructive sleep apnea have significantly greater maximum ultrasonographic tongue depth during respiratory events (p = .0047). We identified three different ultrasonographic patterns of tongue deformation, namely en bloc, tongue body and tongue base. Approximately 82% (27/33) of patients with moderate to severe obstructive sleep apnea demonstrated an en bloc tongue deformation. By contrast, 70% (19/27) of primary snorers or patients with mild obstructive sleep apnea showed a tongue body obstruction. Recognizing the prevalence and patterns of tongue deformation during sleep may provide insights into pathogenesis and treatment decisions in patients with obstructive sleep apnea. Future studies are warranted to verify the treatment results of various tongue procedures by using this approach.

Weighted Epworth sleepiness scale predicted the apnea-hypopnea index better

Background

The relationship between the Epworth sleepiness scale (ESS) and the apnea-hypopnea index (AHI) is uncertain and even poor. The major problem associated with the ESS might be a lack of consideration of weight in prediction in clinical practice. Would awarding different item-scores to the four scales of ESS items to develop a weighted ESS scoring system improve the accuracy of the AHI prediction? It is warranted to explore the intriguing hypotheses.

Methods

Seven hundred fifty-six adult patients with suspicion of obstructive sleep apnoea syndrome (OSAS) were prospectively recruited to a derivation cohort. This was tested against a prospective validation cohort of 810 adult patients with suspected OSAS. Each ESS item’s increased odds ratio for the corresponding AHI was calculated using univariate logistic regression. The receiver operating characteristic curves were created and the areas under the curves (AUCs) were calculated to illustrate and compare the accuracy of the indices.

Results

The higher the ESS item-score, the closer the relationship with the corresponding AHI. The odds ratios decreased as a result of the increased AHI. The ESS items were of unequal weight in predicting the corresponding AHI and a weighted ESS was developed. The coincidence rates with the corresponding AHI, body mass indices, and neck circumferences rose as the scores increased, whereas nocturnal nadir oxygen saturations decreased, and the weighted ESS was more strongly associated with these indices, compared with the ESS. The capability in predicting the patients without OSAS or with severe OSAS was strong, especially the latter, and the weighted ESS orchestrated manifest improvement in screening the patients with simple snoring. The patterns of sensitivities, specificities, and Youden’s indices of the four ranks of weighted ESS for predicting the corresponding AHI were better than those of the ESS, and the AUCs of weighted ESS were greater than the corresponding areas of ESS in the two cohorts.

Conclusions

The weighted ESS orchestrated significant improvement in predicting the AHI, indicating that the capability in predicting the patients without OSAS or with severe OSAS was strong, which might have implications for clinical triage decisions to prioritize patients for polysomnography.

Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks

STUDY OBJECTIVES

Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming.

METHODS

A sleep staging classifier trained using deep learning methods scored PSG data from the Sleep Heart Health Study (SHHS). The training set was composed of 42 560 hours of PSG data from 5213 patients. To capture higher-order data, spectrograms were generated from electroencephalography, electrooculography, and electromyography data and then passed to the neural network. A holdout set of 580 PSGs not included in the training set was used to assess model accuracy and discrimination via weighted F1-score, per-stage accuracy, and Cohen's kappa (K).

RESULTS

The optimal neural network model was composed of spectrograms in the input layer feeding into convolutional neural network layers and a long short-term memory layer to achieve a weighted F1-score of 0.87 and K = 0.82.

CONCLUSIONS

The deep learning sleep stage classifier demonstrates excellent accuracy and agreement with expert sleep stage scoring, outperforming human agreement on sleep staging. It achieves comparable or better F1-scores, accuracy, and Cohen's kappa compared to literature for automated sleep stage scoring of PSG epochs. Accurate automated scoring of other PSG events may eventually allow for fully automated PSG scoring.

Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea

STUDY OBJECTIVES

We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States.

METHODS

Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ≥ 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated.

RESULTS

The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates ≥ 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of ≥ 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases.

CONCLUSIONS

A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research.

Obstructive Sleep Apnea: The Role of Gender in Prevalence, Symptoms, and Treatment Success

The purpose of this article is to provide an overview of known similarities and differences between genders relative to presenting symptoms, demographics, and severity of obstructive sleep apnea. There is a relationship of risk of disease occurrence relative to stages of reproductive life of a woman, indicating that chronologic age might not be as important as timing of pregnancy and menopausal transition. The current understanding of gender differences in treatment success and compliance with oral appliance therapy is limited and requires further investigation.

SleepOMICS: How Big Data Can Revolutionize Sleep Science

Sleep disorders have reached epidemic proportions worldwide, affecting the youth as well as the elderly, crossing the entire lifespan in both developed and developing countries. "Real-life" behavioral (sensor-based), molecular, digital, and epidemiological big data represent a source of an impressive wealth of information that can be exploited in order to advance the field of sleep research. It can be anticipated that big data will have a profound impact, potentially enabling the dissection of differences and oscillations in sleep dynamics and architecture at the individual level ("sleepOMICS"), thus paving the way for a targeted, "one-size-does-not-fit-all" management of sleep disorders ("precision sleep medicine").

Does remote monitoring change OSA management and CPAP adherence?

It is increasingly recognized that the high prevalence of obstructive sleep apnoea (OSA), and its associated cardio-metabolic morbidities make OSA a burden for society. Continuous positive airway pressure (CPAP), the gold standard treatment, needs to be used for more than 4 h/night to be effective, but suffers from relatively poor adherence. Furthermore, CPAP is likely to be more effective if combined with lifestyle changes. Thus, the remote telemonitoring (TM) of OSA patients in terms of CPAP use, signalling of device problems, following disease progression, detection of acute events and monitoring of daily physical activity is an attractive option. In the present review, we aim to summarize the recent scientific data on remote TM of OSA patients, and whether it meets expectations. We also look at how patient education and follow-up via telemedicine is used to improve adherence and we discuss the influence of the profile of the healthcare provider. Then, we consider how TM might be extended to encompass the patient's cardio-metabolic health in general. Lastly, we explore how TM and the deluge of data it potentially generates could be combined with electronic health records in providing personalized care and multi-disease management to OSA patients.

Concurrence of big data analytics and healthcare: A systematic review

BACKGROUND

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care.

PURPOSE

This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges.

DATA SOURCES

A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered.

STUDY SELECTION

Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected.

DATA EXTRACTION

Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare.

RESULTS

A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare.

CONCLUSION

This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.

Heart rate phenotypes and clinical correlates in a large cohort of adults without sleep apnea

BACKGROUND

Normal sleep is associated with typical physiological changes in both the central and autonomic nervous systems. In particular, nocturnal blood pressure dipping has emerged as a strong marker of normal sleep physiology, whereas the absence of dipping or reverse dipping has been associated with cardiovascular risk. However, nocturnal blood pressure is not measured commonly in clinical practice. Heart rate (HR) dipping in sleep may be a similar important marker and is measured routinely in at-home and in-laboratory sleep testing.

METHODS

We performed a retrospective cross-sectional analysis of diagnostic polysomnography in a clinically heterogeneous cohort of n=1047 adults without sleep apnea.

RESULTS

We found that almost half of the cohort showed an increased HR in stable nonrapid eye movement sleep (NREM) compared to wake, while only 13.5% showed a reduced NREM HR of at least 10% relative to wake. The strongest correlates of HR dipping were younger age and male sex, whereas the periodic limb movement index (PLMI), sleep quality, and Epworth Sleepiness Scale (ESS) scores were not correlated with HR dipping. PLMI was however significantly correlated with metrics of impaired HR variability (HRV): increased low-frequency power and reduced high-frequency power. HRV metrics were unrelated to sleep quality or the ESS value. Following the work of Vgontzas et al, we also analyzed the sub-cohort with insomnia symptoms and short objective sleep duration. Interestingly, the sleep-wake stage-specific HR values depended upon insomnia symptoms more than sleep duration.

CONCLUSION

While our work demonstrates heterogeneity in cardiac metrics (HR and HRV), the population analysis suggests that pathological signatures of HR (nondipping and elevation) are common even in this cohort selected for the absence of sleep apnea. Future prospective work in clinical populations will further inform risk stratification and set the stage for testing interventions.

Predictors of sleepiness in obstructive sleep apnoea at baseline and after 6 months of continuous positive airway pressure therapy

We evaluated factors associated with subjective and objective sleepiness at baseline and after 6 months of continuous positive airway pressure (CPAP) therapy in patients with obstructive sleep apnoea (OSA).We analysed data from the Apnoea Positive Pressure Long-term Efficacy Study (APPLES), a prospective 6-month multicentre randomised controlled trial with 1105 subjects with OSA, 558 of who were randomised to active CPAP. Epworth sleepiness scale (ESS) scores and the mean sleep latency (MSL) on the maintenance of wakefulness test at baseline and after 6 months of CPAP therapy were recorded.Excessive sleepiness (ESS score >10) was present in 543 (49.1%) participants. Younger age, presence of depression and higher apnoea-hypopnoea index were all associated with higher ESS scores and lower MSL. Randomisation to the CPAP group was associated with lower odds of sleepiness at 6 months. The prevalence of sleepiness was significantly lower in those using CPAP >4 h·night^-1versus using CPAP ≤4 h·night^-1 Among those with good CPAP adherence, those with ESS >10 at baseline had significantly higher odds (OR 8.2, p<0.001) of persistent subjective sleepiness.Lower average nightly CPAP use and presence of sleepiness at baseline were independently associated with excessive subjective and objective sleepiness after 6 months of CPAP therapy.

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.