Sleep Medicine Research Research Articles

A novel deep learning model for obstructive sleep apnea diagnosis: hybrid CNN-Transformer approach for radar-based detection of apnea-hypopnea events.

The demand for cost-effective and accessible alternatives to polysomnography (PSG), the conventional diagnostic method for obstructive sleep apnea (OSA), has surged. In this study, we have developed and validated a deep learning model for detecting apnea-hypopnea events using radar data. We conducted a single-center prospective cohort study, dividing participants with suspected sleep-disordered breathing into development and temporally independent test sets. Utilizing a hybrid CNN-Transformer architecture, we performed fivefold cross-validation on the development set to develop and subsequently validate the model. Evaluation metrics included sensitivity for event detection, mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (r) for apnea-hypopnea index (AHI) estimation. Linearly weighted kappa statistics (κ) assessed OSA severity. The development set comprised 54 participants (July 2021-May 2022), while the test set included 35 participants (June 2022-June 2023). In the test set, our model achieved an event detection sensitivity of 67.2% (95% CI = 65.8% to 68.5%) and demonstrated a MAE of 7.54 (95% CI = 5.36 to 9.72), indicating good agreement (ICC = 0.889 [95% CI = 0.792 to 0.942]) and a strong correlation (r = 0.892 [95% CI = 0.795 to 0.945]) with the ground truth for AHI estimation. Furthermore, OSA severity estimation showed substantial agreement (κ = 0.780 [95% CI = 0.658 to 0.903]). Our study highlights radar sensors and advanced AI models' potential to improve OSA diagnosis, paving the path for future radar-based diagnostic models in sleep medicine research.

Expert-level sleep staging using an electrocardiography-only feed-forward neural network

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification—further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.

Reader's digest version of scientific writing: comparative evaluation of summarization capacity between large language models and medical students in analyzing scientific writing in sleep medicine.

As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably. A collection of three AI-generated and human-generated summaries of sleep medicine research article abstracts were shared with 19 study participants (medical students) attending a sleep medicine conference. Participants were blind as to which summary was human or LLM generated. After reading both human and AI-generated research summaries participants completed a 1-5 Likert scale survey on the readability of the extracted writings. Participants also answered article-specific multiple-choice questions evaluating their comprehension of the summaries, as a representation of the quality of content retained by the AI-generated summaries. An independent sample t-test between the AI-generated and human-generated summaries comprehension by study participants revealed no significant difference between the Likert readability ratings (p = 0.702). A chi-squared test of proportions revealed no significant association (χ 2 = 1.485, p = 0.223), and a McNemar test revealed no significant association between summary type and the proportion of correct responses to the comprehension multiple choice questions (p = 0.289). Some limitations in this study were a small number of participants and user bias. Participants attended at a sleep conference and study summaries were all from sleep medicine journals. Lastly the summaries did not include graphs, numbers, and pictures, and thus were limited in material extraction. While the present analysis did not demonstrate a significant difference among the readability and content quality between the AI and human-generated summaries, limitations in the present study indicate that more research is needed to objectively measure, and further define strengths and weaknesses of AI models in condensing medical literature into efficient and accurate summaries.

Modeling the Impact of Inter-Rater Disagreement on Sleep Statistics using Deep Generative Learning.

Sleep staging is the process by which an overnight polysomnographic measurement is segmented into epochs of 30 seconds, each of which is annotated as belonging to one of five discrete sleep stages. The resulting scoring is graphically depicted as a hypnogram, and several overnight sleep statistics are derived, such as total sleep time and sleep onset latency. Gold standard sleep staging as performed by human technicians is time-consuming, costly, and comes with imperfect inter-scorer agreement, which also results in inter-scorer disagreement about the overnight statistics. Deep learning algorithms have shown promise in automating sleep scoring, but struggle to model inter-scorer disagreement in sleep statistics. To that end, we introduce a novel technique using conditional generative models based on Normalizing Flows that permits the modeling of the inter-rater disagreement of overnight sleep statistics, termed U-Flow. We compare U-Flow to other automatic scoring methods on a hold-out test set of 70 subjects, each scored by six independent scorers. The proposed method achieves similar sleep staging performance in terms of accuracy and Cohen's kappa on the majority-voted hypnograms. At the same time, U-Flow outperforms the other methods in terms of modeling the inter-rater disagreement of overnight sleep statistics. The consequences of inter-rater disagreement about overnight sleep statistics may be great, and the disagreement potentially carries diagnostic and scientifically relevant information about sleep structure. U-Flow is able to model this disagreement efficiently and can support further investigations into the impact inter-rater disagreement has on sleep medicine and basic sleep research.

Use of extended reality in sleep health, medicine, and research: a scoping review.

This scoping review explores the use of extended reality (virtual, augmented, and mixed reality) within sleep health, sleep medicine, and sleep research. It aims to provide insight into current uses and implementation considerations whilst highlighting directions for future research. A systematic scoping review was undertaken informed by the preferred reporting items for systematic reviews and meta-analyses for scoping reviews and Johanna Briggs Institute. The use of virtual reality (VR) as a research tool in the investigation of areas such as dreaming and memory reactivation is growing. Thirty-one articles were identified in total with 20 utilizing VR to improve sleep as a clinical intervention. Research exploring the utility of VR as a clinical intervention in various patient populations and clinical settings is therefore warranted. Researchers and clinicians should ensure that extended reality interventions are developed based on clinical reasoning and informed by evidence of both sleep medicine and the effects of virtual and augmented reality. Where possible future research should utilize up-to-date technology and reporting frameworks to assist in the translation of research into clinical practice.

Selected Bibliography of Recent Research in Sleep (2020-2021).

"Selected Bibliography of Recent Research in Sleep Medicine (2020-2021)." American Journal of Respiratory and Critical Care Medicine, 0(ja), pp. –

Descriptive characteristics of continuous oximetry measurement in moderate to severe covid-19 patients

Non-invasive oxygen saturation (SpO2) is a central vital sign used to shape the management of COVID-19 patients. Yet, there have been no report quantitatively describing SpO2 dynamics and patterns in COVID-19 patients using continuous SpO2 recordings. We performed a retrospective observational analysis of the clinical information and 27 K hours of continuous SpO2 high-resolution (1 Hz) recordings of 367 critical and non-critical COVID-19 patients hospitalised at the Rambam Health Care Campus, Haifa, Israel. An absolute SpO2 threshold of 93% most efficiently discriminated between critical and non-critical patients, regardless of oxygen support. Oximetry-derived digital biomarker (OBMs) computed per 1 h monitoring window showed significant differences between groups, notably the cumulative time below 93% SpO2 (CT93). Patients with CT93 above 60% during the first hour of monitoring, were more likely to require oxygen support. Mechanical ventilation exhibited a strong effect on SpO2 dynamics by significantly reducing the frequency and depth of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected, up to several hours before the initiation of the mechanical ventilation. In summary, OBMs, traditionally used in the field of sleep medicine research, are informative for continuous assessment of disease severity and response to respiratory support of hospitalised COVID-19 patients. In conclusion, OBMs may improve risk stratification and therapy management of critical care patients with respiratory impairment.

A comparison of cognitive behavioral therapy for insomnia to standard of care in an outpatient substance use disorder clinic embedded within a therapeutic community: a RE-AIM framework evaluation

BackgroundRates of substance use disorders (SUDs) continue to rise in the USA with parallel rises in admissions to outpatient SUD treatment programs. Insomnia symptoms reduce treatment adherence, trigger relapse, and generally undermine SUD recovery efforts. Cognitive-behavioral therapy for insomnia (CBT-I) is the first-line treatment recommended for chronic insomnia. No study has examined the effectiveness of CBT-I for individuals who recently entered an outpatient SUD treatment program embedded within a therapeutic community (i.e., long-term drug-free residential setting).MethodsA randomized controlled trial conducted at a SUD program embedded in a therapeutic community aimed to compare group-based CBT-I (gCBT-I) (N = 10) with the standard of care (SOC) (N = 11) among individuals who have SUDs and comorbid insomnia. We present a RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework evaluation to provide empirical data on gCBT-I feasibility and facilitators and barriers of conducting an insomnia-focused clinical effectiveness study within a therapeutic community.ResultsParticipants in both study arms reported moderately severe insomnia symptoms at admission and reductions in insomnia symptoms over time. Among participants who completed the Insomnia Severity Index (ISI) beyond admission, ISI decreased to ≤ 8 (the clinical cutoff for mild insomnia) in 80% of individuals in the gCBT-I group compared with 25% of individuals in the SOC group. A RE-AIM framework evaluation showed initial success with Reach and Adoption while Implementation, and Maintenance were limited. Effectiveness was inconclusive because of challenges with recruitment, intervention integrity, and missing data that precluded meeting the planned recruitment and study aims and led to study termination. Coordination and communication with staff and leadership facilitated gCBT-I implementation, yet well-known CBT-I barriers including time- and resource-intensive sleep medicine training for interventionalists and maintenance of treatment integrity during an 8-week intervention limited gCBT-I sustainability.ConclusionsThis analysis supports the feasibility of conducting behavioral sleep medicine research in outpatient SUD treatment programs embedded within therapeutic communities. Implementation of an insomnia-focused intervention was widely accepted by patients and providers and has potential to address insomnia symptoms in early SUD recovery. Addressing patient- and organizational-level implementation barriers may enhance the sustainability and scalability of sleep interventions and provide new hope to effectively treat insomnia among people living with SUDs.Trial registrationClinicaltrials.gov: NCT03208855. Registered July 6, 2017https://clinicaltrials.gov/ct2/show/NCT03208855?term=NCT03208855&draw=2&rank=1

Sex differences in polysomnographic findings in patients with obstructive sleep apnea

Background and PurposeSex differences in the clinical findings and the polysomnographic presentation of patients with obstructive sleep apnea (OSA) are compelling current research issues. For example, patients suffering from obstructive sleep apnea are predominantly male. While women are older than men and tend to have a higher body mass index, men typically present with a more severe form of obstructive sleep apnea. Using polysomnography, we investigated a German cohort, subdivided per severity levels of obstructive sleep apnea (apnea-hypopnea index: ≥5 to < 15/h (mild), ≥15 to < 30/h (moderate), and ≥30/h (severe)) to provide a detailed analysis of breathing and sleep parameters, accounting for body position effects and severity of illness. A deeper understanding of sex differences may allow targeted diagnosis and treatment adjustment. Patients and methodsThis retrospective study included a cohort of 1242 German patients (940 male, 302 female) who underwent overnight polysomnography at the private sleep laboratory “Intersom Köln”, Center for Sleep Medicine and Sleep Research. In 1125 subjects (878 male, 247 female), obstructive sleep apnea was diagnosed. All patients were examined between January 01, 2018 and December 31, 2020, comparing anthropometric, sleep morphological, and respiratory polysomnographic findings. ResultsFemale patients with obstructive sleep apnea were significantly older than male patients (60.9 ± 12.3 vs. 56.9 ± 12.5 years, P < .001), also among OSA subgroups per OSA severity. The body mass index was similar in male and female patients (29.6 ± 5.1 vs. 29.2 ± 7.3 kg/m2, P > .05), including the three subgroups. Men were more likely to have severe obstructive sleep apnea (46.9%) than women (35.2%). Women exhibited a higher proportion of slow-wave sleep than men (129.4 ± 52.8 vs. 104.2 ± 53.2 min; P < .001). The apnea-hypopnea index of total sleep time was significantly greater in male than female patients (32.9 ± 21.2 vs. 27.2 ± 20.2 per hour; P < .001). Female patients had a higher apnea-hypopnea index during rapid-eye-movement (REM) sleep (34.0 ± 23.8 vs. 31.8 ± 22.3 per hour; P = .171). A statistically significant difference in the apnea-hypopnea index during REM sleep between sexes was found when the obstructive sleep apnea severity was considered. Women had a lower apnea-hypopnea index in non-rapid eye-movement (NREM) sleep than men (25.7 ± 21.1 vs. 32.7 ± 22.3 per hour; P < .001). The oxygen desaturation index (29.9 ± 20.3 vs. 22.4 ± 19.4%; P < .001) and an oxygen desaturation below 90% (9.4 ± 14.0 vs. 6.8 ± 11.7%; P = .003) was greater in men than in women. In severe obstructive sleep apnea, the oxygen desaturation index was similar between the sexes (45.0 ± 17.8 vs. 41.1 ± 20.9%; P = .077). Male patients showed a higher supine apnea-hypopnea-index than female patients. (45.7 ± 26.7 vs 36.1 ± 22.7 per hour; P < .001). ConclusionThe present noninvasive, retrospective registry study is the first to examine sex differences in OSA in such a large German population in terms of respiratory and sleep parameters, taking into account the effects of body position and severity of the disease. We could confirm and extend observations from previous studies. Female patients were significantly older than the male patients. The apnea-hypopnea index was higher in male than in female patients. Women showed a higher apnea-hypopnea index in REM sleep and a lower one in NREM sleep. Men were desaturated more often and were more affected by supine-dependent obstructive sleep apnea than women. Contrary to the literature, there were no significant differences in body mass index (BMI) between the sexes. With increasing age and BMI, the gender differences become less significant.

Awards for outstanding research in sleep medicine

Awards for outstanding research in sleep medicine

Socioeconomic Position and Excessive Daytime Sleepiness: A Systematic Review of Social Epidemiological Studies.

The objectives of this empirical study are to describe and discuss the current literature available on the relationship between excessive daytime sleepiness (EDS) and the socioeconomic position (SEP) as well as to provide recommendations for consideration of SEP in sleep medicine and biomedical research. Databases Medline/PubMed, Web of Science, Google scholar and Scopus were screened from January 1990 to December 2020 using PRISMA guidelines and 20 articles were included in the final synthesis. Nineteen studies were cross-sectional and one study was longitudinal. Among these studies, 25.00% (n = 5) are focused on children and adolescent and the remaining 75.00% (n = 15) focused on adults and seniors. Ages ranged from 8 to 18 years old for children/adolescent and ranged from 18 to 102 years old for adults. Main SEP measures presented in these studies were education, income, perceived socioeconomic status and employment. The sample size in these studies varied from N = 90 participants to N = 33,865 participants. Overall, a lower educational level, a lower income and full-time employment were associated with EDS. Symptoms of EDS are prevalent in women, especially those with a low income or no job; and children and adolescents with difficult living conditions or working part time reported more sleep disturbances. SEP is already considered as an important determinant for many health outcomes, but if SEP is embedded in the experimental design in psychosomatic research, biomedical research and clinical practice as a constant variable regardless of outcome; it will move forward future investigations.

Is Sleep Disordered Breathing Confounding Rehabilitation Outcomes in Spinal Cord Injury Research?

The purpose of this article is to highlight the importance of considering sleep-disordered breathing (SDB) as a potential confounder to rehabilitation research interventions in spinal cord injury (SCI). SDB is highly prevalent in SCI, with increased prevalence in individuals with higher and more severe lesions, and the criterion standard treatment with continuous positive airway pressure remains problematic. Despite its high prevalence, SDB is often untested and untreated in individuals with SCI. In individuals without SCI, SDB is known to negatively affect physical function and many of the physiological systems that negatively affect physical rehabilitation in SCI. Thus, owing to the high prevalence, under testing, low treatment adherence, and known negative effect on the physical function, it is contended that underdiagnosed SDB in SCI may be confounding physical rehabilitation research studies in individuals with SCI. Studies investigating the effect of treating SDB and its effect on physical rehabilitation in SCI were unable to be located. Thus, studies investigating the likely integrated relationship among physical rehabilitation, SDB, and proper treatment of SDB in SCI are needed. Owing to rapid growth in both sleep medicine and physical rehabilitation intervention research in SCI, the authors contend it is the appropriate time to begin the conversations and collaborations between these fields. We discuss a general overview of SDB and physical training modalities, as well as how SDB could be affecting these studies.

Putative associations between inflammatory biomarkers, obesity, and obstructive sleep apnea.

BACKGROUND:Previous studies have reported increased levels of inflammatory mediators in patients with obstructive sleep apnea (OSA), but their relation with the severity of OSA is controversial.OBJECTIVE:To address potential relationships between OSA-related inflammatory markers, namely, C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and fibrinogen, with different oxygenation parameters and with BMI.METHODS:All eligible patients with suspected OSA newly referred to the Sleep Medicine Research Center at King Abdulaziz University Hospital, Jeddah, were evaluated demographically and anthropometrically, and underwent overnight polysomnography. Fasting morning blood samples were collected to measure serum levels of CRP, fibrinogen, TNF-α, and IL-6. Potential correlations between these inflammatory mediators and severity measures of OSA and body mass index (BMI) were explored.RESULTS:Sixty-four patients completed the study (40 with OSA and 24 without OSA). Significantly increased levels of CRP, fibrinogen, IL-6, and TNF-α emerged in patients with OSA compared to non-OSA. Significant associations between log CRP and log fibrinogen levels emerged with increasing BMI. However, there was no significant association between any of the inflammatory markers and the severity of OSA based on the apnea/hypopnea index or oxyhemoglobin saturation-derived parameters.CONCLUSIONS:OSA patients exhibit increased levels of inflammatory mediators that do not appear to be associated with polysomnographic measures, but exhibit positive correlation with the degree of adiposity.

561-P: Hispanic/Latino Adults with or at Risk of Type 2 Diabetes: Adequate Activity but Not Enough Sleep?

Sufficient sleep and physical activity (PA) are key for optimal health; low PA and sleep are established risk factors for type 2 diabetes (T2D). The American Academy of Sleep Medicine and Sleep Research Society recommend 7 hours/ night for adults; less than 7 hours on a regular basis is associated with adverse health outcomes. Similarly, 8,000 steps/day is associated with 51% lower risk than 4,000 steps/day for premature death. This study assessed PA and sleep in Hispanic/Latino adults with or at risk of T2D. Sleep and PA were measured by Fitbit InspireHR worn on the non-dominant wrist 24 hours/day for 7 consecutive days by 105 Hispanic/Latino adults (mean±SD 57.4±10.3 years, 67 women, 92 with T2D, HbA1c 7.9±2.0). BMI and waist circumference (WC) differed by gender (BMI women (W) 32.3±5.9 vs. men (M) 29.2±4.8, p≤0.01; WC W 102.4±13.6 vs. M 97.1±10.2cm, p≤0.05). Sleep duration was below recommended levels in W and M. Average weekday steps differed significantly from weekend steps. In W, average steps/weekday was negatively correlated with WC -0.26, BMI -0.26, and age -0.27, all p≤0.05. In M, average steps/weekday was negatively correlated with HbA1c -0.44 and total sleep time -0.37, p≤0.05. In these Hispanic/Latino adults with or at risk of T2D, PA was within recommended levels, but sleep was low in both W and M at ~6 hours/night. More weekday steps was associated with lower WC and BMI in W and with lower HbA1c and sleep duration in M. Disclosure W. C. Bevier: Research Support; Self; Abbott Diabetes, Eli Lilly and Company. A. J. Larez: Research Support; Self; Abbott, Eli Lilly and Company. C. Conneely: Research Support; Self; Abbott, Eli Lilly and Company. N. M. Glantz: Research Support; Self; Abbott, Eli Lilly and Company. K. N. Castorino: Consultant; Self; Dexcom, Inc., Research Support; Self; Abbott Diabetes, Abbott Laboratories, Dexcom, Inc., Drawbridge Health, Inc., Lilly Diabetes, Medtronic, Novo Nordisk Inc. D. Kerr: Advisory Panel; Self; Novo Nordisk, Sanofi-Aventis, Research Support; Self; Abbott Diabetes, Elsevier, Lilly Diabetes, Stock/Shareholder; Self; Glooko, Inc. Funding Eli Lilly and Company

Opportunities to improve sleep of children exposed to interpersonal violence: A social-ecological perspective

Over 25% of U.S. children are witness to traumatic intrafamilial or community violence each year, and sleep medicine and developmental research jointly suggest that trauma-exposed youth experience more sleep disturbance than their non-exposed counterparts. Sleep medicine literature emphasizes physical and social environmental factors affecting sleep, and trauma literature underscores children’s seeking out physically and emotionally safe and predictable environments during trauma recovery. This study employed a hermeneutic phenomenological framing to explore the lived experiences of 65 violence-exposed children and families, and to examine how youths’ social and physical sleep environments facilitated or impeded sleep in the aftermath of trauma. Children’s sleep experiences following violence exposure shared two primary essences of experience: a) navigating external threats that agitated sleep after trauma; and b) exercising agency over sleep and related environments to restabilize emotional security. Clinicians and social services coordinators working with children and families are uniquely positioned to indicate sleep assessments as part of treatment following trauma, and to also facilitate identification of tangible, sleep-supportive and changeable factors in sleep environments.

Incremental health care utilization and expenditures for sleep disorders in the United States.

To determine the incremental increases in health care utilization and expenditures associated with sleep disorders. Adults with a diagnosis of a sleep disorder (International Classification of Diseases, 10th Revision, code G47.x) within the medical conditions file of the 2018 Medical Expenditure Panel Survey medical conditions file were identified. This dataset was then linked to the consolidated expenditures file and comparisons in health care utilization and expenditures were made between those with and without sleep disorders. Multivariate analyses, adjusted for demographics and comorbidities, were conducted for these comparisons. Overall, 5.6% ± 0.2% of the study population had been diagnosed with a sleep disorder, representing approximately 13.6 ± 0.6 million adults in the United States. Those with sleep disorders were more likely to be non-Hispanic, White, and female, with a higher proportion with public insurance and higher Charlson Comorbidity Scores. Adults with sleep disorders were found to have increased utilization of office visits (16.3 ± 0.8 vs 8.7 ± 0.3, P < .001), emergency room visits (0.52 ± 0.03 vs 0.37 ± 0.02, P < .001), and prescriptions (39.7 ± 1.2 vs 21.9 ± 0.4, P < .001) vs those without sleep disorders. The additional incremental health care expenses for those with sleep disorders were increased in all examined measures: total health care expense ($6,975 ± $800, P < .001), total office-based expenditures ($1,694 ± $277, P < .001), total prescription expenditures ($2,574 ± $364, P < .001), and total self-expenditures for prescriptions ($195 ± $32, P < .001). Sleep disorders are associated with significantly higher rates of health care utilization and expenditures. By using the conservative prevalence estimate found in this study, the overall incremental health care costs of sleep disorders in the United States represents approximately $94.9 billion. Huyett P, Bhattacharyya N. Incremental health care utilization and expenditures for sleep disorders in the United States. J Clin Sleep Med. 2021;17(10):1981-1986.

Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership's Common Data Model: a pilot feasibility study

Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format—the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract–transform–load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data. We believe that this study represents the first CDM conversion of PSG. In the future, CDM transformation will enable network research in sleep medicine and will contribute to presenting more relevant clinical evidence.

Past, present, and future of sleep medicine research in Latin America.

Sleep medicine is a relatively young field with exponential growth in development and research in the last decades. Parallel to the advances in the United States, Latin America also had its beginnings in sleep medicine housed in neuroscience laboratories. Since the very first Latin American meeting in 1985, and the first sleep society in 1993, sleep research has undergone significant development in subsequent years. From contributions in animal research that allowed understanding of the activity of the brain during sleep to the studies that improved our knowledge of sleep disorders in humans, Latin America has become a scientific hub for expansion of sleep research. In this article, we present a historical account of the development of sleep medicine in Latin America, the current state of education and the achievements in research throughout history, and the latest advances in the trending areas of sleep science and medicine. These findings were presented during World Sleep Society meeting in Vancouver in 2019 and complement the work on sleep societies and training published by Vizcarra-Escobar et al in their article "Sleep societies and sleep training programs in Latin America" (J Clin Sleep Med. 2020;16(6):983-988).

Current state and future perspectives of sleep medicine and sleep research in Armenia. A Joint Position Statement of Armenian Sleep Disorders Association, Armenian National Institute of Health, and European Assembly of National Sleep Societies

This article represents a brief overview and summary of the main ideas, suggestions, agreements, and conclusions reached during a special round-table discussion held on Oct 10, 2019, at the Armenian National Institute of Health, with the participation of representatives from the Armenian Sleep Disorders Association and the Executive Committee of the Assembly of National Sleep Societies (ANSS) of the European Sleep Research Society. As the pilot activity of the ANSS "Beyond Boundaries" project, it aimed to identify the current needs in the field of sleep medicine in Armenia and to summarize the recommendations to help improving the future multidisciplinary development of this important field in Armenia. The article aims to serve as a guiding point for further collaborations regarding sleep medicine in Armenia. Based on the evaluation of this pilot project, the ANSS will further shape and improve the "Beyond Boundaries" project for further implementation in other European countries that wish to develop knowledge and skills in the field of sleep medicine and research and broaden their international network.

Using the Remote Monitoring Framework to Promote Adherence to Continuous Positive Airway Pressure.

The ability to remotely monitor positive airway pressure therapy adherence and efficacy provides a unique opportunity for the field of sleep medicine to quickly and efficiently improve patient adherence. Smaller randomized studies and larger-scale retrospective evaluations show that telemedicine interventions leveraging these data can increase average usage and efficiency of care. However, more evidence on the impact of these programs on longer-term adherence and improving patient-reported outcomes is needed. Combining data from remote monitoring with clinical information in electronic health records may prove to be invaluable to the future of clinical sleep medicine practice and research.