Journal of Clinical Sleep Medicine最新文献

Study objectives: Excessive daytime sleepiness is prevalent and overwhelmingly stems from disturbed sleep. We hypothesized that age modulates the association between excessive daytime sleepiness and increased all-cause mortality.

Methods: We utilized the Veterans' Health Administration data from 1999-2022. We enrolled participants with sleep related International Classification of Diseases 9/10 codes or sleep services. A natural language processing pipeline was developed and validated to extract the Epworth Sleepiness Scale (ESS) as a self-reported tool to measure excessive daytime sleepiness from physician progress notes. The natural language processing's accuracy was assessed through manual annotation of 470 notes. Participants were categorized into normal-ESS (ESS 0-10) and high-ESS (ESS 11-24). We created 3 age groups: < 50 years, 50 to < 65 years, and ≥ 65 years. The adjusted odds ratio of mortality was calculated for age, body mass index, sex, race, ethnicity, and the Charlson Comorbidity Index, using normal-ESS as the reference. Subsequently, we conducted age stratified analysis.

Results: The first ESS records were extracted from 423,087 veterans with a mean age of 54.8 (± 14.6), mean body mass index of 32.6 (± 6.2), and 90.5% male. The adjusted odds ratio across all ages was 17% higher (1.15, 1.19) in the high-ESS category. The adjusted odds ratio s only became statistically significant for individuals aged ≥ 50 years in the high-ESS compared to the normal-ESS category (< 50 years: 1.02 [0.96, 1.08], 50 to < 65 years 1.13[1.10, 1.16]; ≥ 65 years: 1.25 [1.21, 1.28]).

Conclusions: High-ESS predicted increased mortality only in participants aged 50 and older. Further research is required to identify this differential behavior in relation to age.

Citation: Maghsoudi A, Azarian M, Sharafkhaneh A, et al. Age modulates the predictive value of self-reported sleepiness for all-cause mortality risk: insights from a comprehensive national database of veterans. J Clin Sleep Med. 2024;20(11):1785-1792.

Study objectives: Hypoglossal nerve stimulation (HGNS) therapy has historically had strict eligibility requirements including a body mass index (BMI) < 32 kg/m². However, recent Food and Drug Administration approval expanded indications to a BMI < 40 kg/m². There is a wide variability in body fat distribution. This study sought to determine whether neck circumference (NC) is a better surrogate predictive variable for HGNS outcomes than BMI.

Methods: A retrospective chart review was conducted at a tertiary care center on adults who underwent HGNS implantation by a single surgeon from March 2017 to October 2021. Baseline demographic data including NC, diagnostic sleep studies, and postimplantation HGNS titration studies were collected. Linear regression and Spearman's correlation coefficient analysis were used to compare NC, percentage of predicted NC, and BMI with the apnea-hypopnea index at effective voltage.

Results: This study included 43 patients who were middle-aged (61.1 years), predominantly male (76.7%), with severe obstructive sleep apnea (median apnea-hypopnea index, 35 events/h) and mean NC of 15.3 inches. Using the NC and percentage of predicted NC, positive correlations with apnea-hypopnea index at effective voltage were observed (P = .0033, Spearman's correlation coefficient = .438 and P = .0029, Spearman's correlation coefficient = .444). While controlling for BMI, a 1-inch increase in NC was associated with a 35% increase in apnea-hypopnea index at effective voltage (P = .0411).

Conclusions: A larger NC was independently associated with worse HGNS outcomes. Further research is needed to support and confirm these findings, particularly across sexes.

Citation: Hutz MJ, Plata DM, LoSavio P, Herdegen J, Zhang Y, Mokhlesi B. The impact of neck circumference on hypoglossal nerve stimulator therapy outcomes. J Clin Sleep Med. 2024;20(11):1755-1761.

Study objectives: Continuous positive airway pressure (CPAP) is considered effective treatment for moderate-severe obstructive sleep apnea (OSA). Clinicians measure adherence to treatment by the number of hours CPAP is used per 24 hours. There have been very few studies examining the patients' lived experience of CPAP and the barriers to use. This study examined the patients' experience of OSA, CPAP use and treatment during COVID-19.

Methods: This CPAP patient experience qualitative study was part of a larger project. The overarching study was a 3-armed nonblinded randomized controlled trial of patients on CPAP for treatment of OSA using standard care or 1 of 2 telemedicine interventions. Patients who had completed the randomized controlled trial study were invited to be interviewed via telephone, about their OSA diagnosis, CPAP experiences, their experience of using telemedicine, and the impact of SARS-CoV-2 (COVID-19).

Results: Fifteen patients (5 from each arm of the trial) took part in a semistructured interview. Thematic analysis identified 3 themes: day to day living, standard hospital care vs telemedicine management, and living through COVID-19 with CPAP.

Conclusions: All participants described significant symptoms caused by their OSA before diagnosis. Although CPAP treatment was often described as challenging, patients discussed the improvement in their symptoms with treatment. There were differing opinions on virtual appointments; however, the consensus was the use of telemedicine to support CPAP treatment was well received. COVID-19 appeared to have little effect on the patient experience.

Citation: Jones TA, Roddis J, Stores R. Patient experience of the use of continuous positive airway pressure for the treatment of obstructive sleep apnea with or without telemedicine during COVID-19: a qualitative approach. J Clin Sleep Med. 2024;20(11):1739-1748.

Study objectives: From 2019-2023, the United States Food and Drug Administration has cleared 9 novel obstructive sleep apnea-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature.

Methods: We collected information from PubMed, United States Food and Drug Administration clearance documents, ClinicalTrials.gov, and web sources, with direct industry input whenever feasible.

Results: In this "device-centered" review, we broadly categorized these wearables into 2 main groups: those that primarily harness photoplethysmography data and those that do not. The former include the peripheral arterial tonometry-based devices. The latter was further broken down into 2 key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized.

Conclusions: In the foreseeable future, these novel obstructive sleep apnea-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe obstructive sleep apnea without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies.

Citation: Chiang AA, Jerkins E, Holfinger S, et al. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med. 2024;20(11):1823-1838.

Study objectives: To study the association between sleep-quality, total sleep duration and wound-healing among adult patients having good sleep-quality at the time of admission in hospital, undergoing laparotomy for various reasons.

Methods: In this observational study , consecutive adult subjects undergoing emergency laparotomy were followed up until the eighth postoperative day. The primary outcome (wound healing) was assessed using the Southampton Wound Grading System. Sleep quality (assessed by the single item sleep quality scale) was the primary predictor. Pain was assessed using visual analogue pain scale. We studied the effect of postoperative sleep quality on wound healing on postoperative day 8. Secondary analyses assessed the effect of total sleep time, severity of pain and markers of systemic inflammation on wound healing.

Results: In this study 110 participants were included. The average age of participants was 41.7±16.2 years. On postoperative day 8, 34.5% rated their sleep quality as "poor to fair" and rest as "good". Postoperative poor sleep quality was associated with impaired wound healing, starting from third postoperative day (P<0.001 for each subsequent day). Multiple logistic regression was overall significant (χ²= 118.40; df=9; P<0.001), classified 92.7% cases correctly and explained 88% variance to the outcome. This model showed that shorter total-sleep-time (P=0.009), higher total leukocyte count (P=0.005), presence of comorbidities (P=0.01) and poor sleep quality during the postoperative week (OR=78.14; P=0.005) increased odds for impaired healing of wound.

Conclusions: Poor sleep quality during the healing phase is associated with wound complications, a surrogate marker of impaired wound healing.

Study objectives: Sleep disturbances lead to negative health outcomes and caregiver burden, particularly in community settings. This study aimed to investigate a predictive model for sleep efficiency and its associated features in older adults living with dementia in their own homes.

Methods: This was an exploratory, observational study. A total of 69 older adults diagnosed with dementia were included in this study. Data were collected via actigraphy for sleep and physical activity for 14 days, a sweat patch for cytokines for 2-3 days, and a survey of diseases, medications, psychological and behavioral symptoms, functional status, and demographics at baseline. Using 730 days of actigraphy, sweat patches, and baseline data, the best prediction model for sleep efficiency was selected and further investigated to explore its associated top 10 features using machine learning analysis.

Results: The CatBoost model was selected as the best predictive model for sleep efficiency. In order of importance, the most important features were sleep regularity, number of medications, dementia medication, daytime activity count, instrumental activities of daily living, neuropsychiatric inventory, hypnotics, occupation, tumor necrosis factor-alpha, and waking hour lux.

Conclusions: This study established the best sleep efficiency predictive model among community-dwelling older adults with dementia and its associated features using machine learning and various sources, such as the Internet of Things. This study highlights the importance of individualized sleep interventions for community-dwelling older adults with dementia based on associated features.

Study objectives: We examined the effects of nurse-led brief behavioral treatment for insomnia (BBTI) on insomnia severity, sleep status, daytime function, quality of life, psychological distress levels, treatment response, and insomnia remission in young and middle-aged Asian adults with insomnia symptoms.

Methods: This 2-parallel, randomized controlled trial recruited 42 participants with insomnia symptoms randomly allocated to the nurse-led BBTI group or sleep hygiene group. The outcome measurements included the Insomnia Severity Index, sleep diary, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Brief Fatigue Inventory, RAND-36 Health Status Inventory, and the Depression, Anxiety, and Stress Scale-21. The measurement time points included baseline, the end of each week of the intervention period, and 1-month follow-up.

Results: Compared with the sleep hygiene group, participants in the BBTI group had significantly improved insomnia severity, sleep onset latency, sleep efficiency, sleep quality, daytime sleepiness, and the mental components of quality of life after completing nurse-led BBTI immediately and 1 month later (P < .05). In addition, 52.4% and 71.4% of the participants achieved remission after completing nurse-led BBTI immediately and 1 month later, which was significantly higher than in the sleep hygiene group (14.3%, P = .02; 14.3%, P < .001, respectively).

Conclusions: We suggested the relative effects of BBTI on declined insomnia severity and improved sleep status among young and middle-aged Asian adults with insomnia symptoms and confirmed the benefits of nurse-led BBTI in alleviating insomnia. Nurses should incorporate BBTI into insomnia care further to enhance the daytime function and quality of life of the population with insomnia symptoms.

Clinical trial registration: Registry: ClinicalTrials.gov; Name: Effects of Nurse-led Brief Behavioral Treatment for Insomnia: A Feasibility Randomized Controlled Trial; URL: https://clinicaltrials.gov/study/NCT05310136; Identifier: NCT05310136.

Citation: Chang K-M, Lin C-J, Jan Y-W, et al. Effects of nurse-led brief behavioral treatment for insomnia in adults: a randomized controlled trial. J Clin Sleep Med. 2024;20(11):1763-1772.

Study objectives: Behavioral sleep medicine (BSM) is a subspecialty that combines behavioral psychology and sleep medicine specialties. The objective of this study was to analyze referral patterns to a BSM clinic. The 3 specific aims were: (1) describe factors that predict referral acceptance, (2) identify barriers to attending initial appointment, and (3) describe variables associated with the number of visits attended.

Methods: Retrospective chart reviews were conducted as part of a quality improvement project by this study team's clinical setting. Adults over 21 years of age who were referred to a BSM clinic in an urban Midwestern academic health care system between 2014 and 2019 were included in this study.

Results: Sleep medicine was the main referral source for patients with BSM (74.2%), followed by internal medicine (9.3%) and neurology/psychiatry (7.3%). Thirty-eight percent of patients did not schedule an appointment after a referral for BSM was initiated. Younger age, longer distance from clinic, commercial insurance, and out-of-network insurance were all significantly greater for nonschedulers. Eighty-three percent of patients did attend the initial intake session with BSM providers. Older age was associated with lower likelihood of not attending scheduled BSM appointments.

Conclusions: Patient characteristics of older age, closer distance from clinic, and in-network insurance coverage were found to significantly increase the likelihood of BSM scheduling, while younger age, Black race and not getting a primary sleep disorder diagnosis (vs a diagnosis of insomnia disorder) and shorter days from referral to appointment were associated with an increased likelihood of not attending the scheduled BSM treatment engagement.

Citation: Chernyak Y, Ofner S, Williams MK, Bolarinwa C, Manchanda S, Otte JL. Patient accessibility and utilization of behavioral sleep medicine referrals in an academic center. J Clin Sleep Med. 2024;20(11):1793-1806.