Correction: Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire.

[This corrects the article DOI: 10.1371/journal.pone.0258040.].

Depressive Symptoms and Sleep Quality Mediate the Relationship Between Race and Quality of Life Among Patients With Heart Failure: A Serial Multiple Mediator Model.

BACKGROUND: Black patients with heart failure (HF) report worse quality of life (QoL) than White patients. Few investigators have examined mediators of the association between race and QoL, but depressive symptoms and sleep quality are associated with QoL. OBJECTIVE: The aim of this study was to determine whether depressive symptoms and sleep quality are mediators of the relationship between race and QoL among patients with HF. METHODS: This was a cross-sectional study. We included 271 outpatients with HF. Self-reported race (White/Black), depressive symptoms (Patient Health Questionnaire), sleep quality (Pittsburgh Sleep Quality Index), and QoL (Kansas City Cardiomyopathy Questionnaire) were collected at baseline. A serial multiple mediator analysis was conducted using the PROCESS macro for SPSS. RESULTS: Ninety-six patients (35.4%) were Black. Black participants reported higher levels of depressive symptoms and poorer sleep quality than White participants. Race was not directly associated with QoL but indirectly associated with QoL through depressive symptoms and poorer sleep quality. Because of higher levels of depressive symptoms and poorer sleep quality, Black participants reported poorer QoL than White participants. CONCLUSIONS: Depressive symptoms and sleep quality together mediated the relationship between race and QoL. These findings suggest that screening for depressive symptoms and sleep quality could identify patients at risk for poor QoL, especially in Black patients.

Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use?

As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.

Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine.

Since the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep clinicians. To help clinicians understand the current state of AI and sleep medicine, and to further enable these solutions to be adopted into clinical practice, a discussion panel was conducted on June 7, 2022, at the Associated Professional Sleep Societies Sleep Conference in Charlotte, North Carolina. The article is a summary of key discussion points from this session, including aspects of considerations for the clinician in evaluating AI-enabled solutions including but not limited to what steps might be taken both by the Food and Drug Administration and clinicians to protect patients, logistical issues, technical challenges, billing and compliance considerations, education and training considerations, and other unique challenges specific to AI-enabled solutions. Our summary of this session is meant to support clinicians in efforts to assist in the clinical care of patients with sleep disorders utilizing AI-enabled solutions. CITATION: Bandyopadhyay A, Bae C, Cheng H, et al. Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine. J Clin Sleep Med. 2023;19(10):1823-1833.

Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea.

GOAL AND AIMS: Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification. FOCUS TECHNOLOGY: Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30. DESIGN: Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule. CORE ANALYTICS: Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix. CORE OUTCOMES: The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep. CORE CONCLUSION: Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.

Photoplethysmography-new applications for an old technology: a sleep technology review.

Education is integral to the American Academy of Sleep Medicine (AASM) mission. The AASM Emerging Technology Committee identified an important and evolving piece of technology that is present in many of the consumer and clinical technologies that we review on the AASM #SleepTechnology (https://aasm.org/consumer-clinical-sleep-technology/) resource-photoplethysmography. As more patients with sleep tracking devices ask clinicians to view their data, it is important for sleep providers to have a general understanding of the technology, its sensors, how it works, targeted users, evidence for the claimed uses, and its strengths and weaknesses. The focus in this review is photoplethysmography-a sensor type used in the familiar pulse oximeter that is being developed for additional utilities and data outputs in both consumer and clinical sleep technologies. CITATION: Ryals S, Chang A, Schutte-Rodin S, et al. Photoplethysmography-new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023;19(1):189-195.

In search of a better CPAP interface: A network meta-analysis comparing nasal masks, nasal pillows and oronasal masks.

Until now, no study has directly network meta-analysed the impact of nasal masks, nasal pillows and oronasal masks on continuous positive airway pressure therapy in patients with obstructive sleep apnea. This study aimed to meta-analyse the impact of three kinds of nasal interfaces with both network meta-analysis and pairwise comparison. PubMed, EMBASE, CENTRAL and ClinicalTrials.gov were systematically searched from inception to December 2020 for studies that compared the three types of nasal interfaces for treating obstructive sleep apnea with continuous positive airway pressure. The outcomes were residual apnea-hypopnea index, continuous positive airway pressure, and nightly average usage. The network meta-analysis was conducted using multivariate random-effects in a frequentist framework where three interfaces were ranked with the surface under the cumulative ranking probabilities. The pairwise comparison was conducted using random-effects meta-analysis. Twenty-nine articles comprising 6378 participants were included. The pairwise comparison showed both nasal masks and nasal pillows were associated with lower residual apnea-hypopnea index, lower continuous positive airway pressure, and higher continuous positive airway pressure adherence compared with oronasal masks. The surface under the cumulative ranking confirmed that nasal masks were associated with the lowest residual apnea-hypopnea index and highest adherence, while pillows were associated with the lowest continuous positive airway pressure. The meta-regression identified that lower pretreatment apnea-hypopnea index and continuous positive airway pressure determined during continuous positive airway pressure titration (versus determined during continuous positive airway pressure therapy) was associated with lower continuous positive airway pressure with nasal masks and nasal pillows. In conclusion, compared with oronasal masks, nasal masks and nasal pillows are better interfaces, especially in patients with lower pretreatment apnea-hypopnea index and those with the therapeutic pressure determined during continuous positive airway pressure titration.

Comparison of expiratory pressures generated by four different EPAP devices in a laboratory bench setting.

OBJECTIVE/BACKGROUND: Expiratory positive airway pressure (EPAP) has been a treatment option for patients with obstructive sleep apnea (OSA). ULTepap is a new FDA-cleared EPAP device that seals the nares with a nasal pillow interface. Comparisons of expiratory pressures generated by ULTepap and other EPAP devices like Provent, Bongo Rx, and Theravent are not available. We aimed to compare the backpressures created by these devices in an in vitro laboratory bench setting. METHODS: A test rig was designed and fabricated to test the expiratory pressures generated by ULTepap, Provent, Bongo Rx, and Theravent. Airflow was generated by a linear actuator-driven piston in a syringe, and a range of flow rates was provided by varying the voltage input to the actuator. The resulting expiratory and inspiratory pressures were measured and resistances were calculated. RESULTS: The backpressures generated by ULTepap and Provent were comparable at all flow rates. For flow rates at 99/142/212 ml/s, the expiratory pressures were 3.5/7.5/13.8 cmH2O for ULTepap and 4.5/8.5/14.5 cmH2O for Provent. Bongo Rx and Theravent devices produced substantially lower backpressures compared to ULTepap devices (0.8/1.8/3.5 cmH2O for Bongo Rx and 0.9/2.2/5.3 cmH2O for Theravent at flow rates of 99/142/212 ml/s). All four devices presented very low inspiratory flow resistance, with all generating 0.5 cmH2O or less at all flow rates. CONCLUSION: Not all FDA-cleared EPAP devices produce similar expiratory pressure profiles. ULTepap generated backpressures closest to that of Provent. Clinical trials comparing the efficacy, tolerance, and adherence of these EPAP devices in patients with OSA are warranted.

Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire.

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5-15; 19% had AHI 15-30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703-0.888], 0.931 [95% CI, 0.772-0.992], and 0.735 [95% CI, 0.589-0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828-0.987], 0.944 [95% CI, 0.727-0.999], and 0.933 [95% CI, 0.779-0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1.

Belun Ring Platform: a novel home sleep apnea testing system for assessment of obstructive sleep apnea.

STUDY OBJECTIVES: The objective of the study is to validate the performance of Belun Ring Platform, a novel home sleep apnea testing system using a patented pulse oximeter sensor and a proprietary cloud-based neural networks algorithm. METHODS: The Belun Ring captures oxygen saturation, photoplethysmography, and accelerometer signals. The Belun Ring total sleep time is derived from features extracted from accelerometer, oxygen saturation, and photoplethysmography signals. The Belun Ring respiratory event index is derived from Belun Ring total sleep time and features extracted from heart rate variability and oxygen saturation changes. A total of 50 adults without significant cardiopulmonary or neuromuscular comorbidities and heart rate affecting medications were evaluated. In-lab sleep studies were performed simultaneously with the Ring and the studies were manually scored using the American Academy of Sleep Medicine Scoring Manual 4% desaturation criteria. RESULTS: The Belun Ring respiratory event index correlated well with the polysomnography-apnea-hypopnea index (AHI; r = .894, P < .001). The sensitivity and specificity in categorizing AHI ≥ 15 events/h were 0.85 and 0.87, respectively, and the positive predictive value and negative predictive value were 0.88 and 0.83, respectively. The Belun Ring total sleep time also correlated well with the polysomnography-total sleep time (r = .945, P < .001). Although the Belun Ring Platform has a good overall performance, it tends to overestimate AHI in individuals with AHI under 15 events/h and underestimate AHI in those with AHI over 15 events/h. Conclusions: In this proof-of-concept study, the Belun Ring Platform demonstrated a reasonable accuracy in predicting AHI and total sleep time in patients without significant comorbidities and heart rate-affecting medications. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Validation of a Novel Device for Screening Patients With Symptoms of Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/NCT04121923; Identifier: NCT04121923.

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample.

STUDY OBJECTIVES: Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice. METHODS: The SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, ≥5/h, ≥15/h, and ≥30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (<65 versus ≥65 years old [y/o]). RESULTS: Two features were selected to predict AHI cutoff ≥5/h with six features selected for ≥15/h, and six features selected for ≥30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male <65 y/o and modest for female ≥65 y/o. CONCLUSION: Our model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study.

Testing the reliability and validity of DSM-IV-TR and ICSD-2 insomnia diagnoses. Results of a multitrait-multimethod analysis.

CONTEXT: Distinctive diagnostic classification schemes for insomnia diagnoses are available, but the optimal insomnia nosology has yet to be determined. OBJECTIVES: To test the reliability and validity of insomnia diagnoses listed in the American Psychiatric Association's DSM-IV-TR and the International Classification of Sleep Disorders, second edition (ICSD-2). DESIGN: Multitrait-multimethod correlation design. SETTING: Two collaborating university medical centers, with recruitment from January 2004 to February 2009. PARTICIPANTS: A total of 352 adult volunteers (235 of whom were women) who met research diagnostic criteria for insomnia disorder. MAIN OUTCOME MEASURES: Goodness-of-fit ratings of 10 DSM-IV-TR and 37 ICSD-2 insomnia diagnoses for each patient. Ratings were provided by 3 clinician pairs who used distinctive assessment methods to derive diagnostic impressions. Correlations computed within and across clinician pairs were used to test reliability and validity of diagnoses. RESULTS: Findings suggested that the best-supported DSM-IV-TR insomnia categories were insomnia related to another mental disorder, insomnia due to a general medical condition, breathing-related sleep disorder, and circadian rhythm sleep disorder. The category of primary insomnia appeared to have marginal reliability and validity. The best-supported ICSD-2 categories were the insomnias due to a mental disorder and due to a medical condition, obstructive sleep apnea, restless legs syndrome, idiopathic insomnia, and circadian rhythm sleep disorder-delayed sleep phase type. Psychophysiological insomnia and inadequate sleep hygiene received much more variable support across sites, whereas the diagnosis of paradoxical insomnia was poorly supported. CONCLUSIONS: Both the DSM-IV-TR and ICSD-2 provide viable insomnia diagnoses, but findings support selected subtypes from each of the 2 nosologies. Nonetheless, findings regarding the frequently used DSM-IV-TR diagnosis of primary insomnia and its related ICSD-2 subtypes suggest that their poor reliability and validity are perhaps due to significant overlap with comorbid insomnia subtypes. Therefore, alternate diagnostic paradigms should be considered for insomnia classification.

Sleep-related hypoxemia aggravates systematic inflammation in emphysematous rats.

BACKGROUND: Sleep disturbance is common in patients with emphysema. This study aimed to develop a novel model of sleep-related hypoxemia (SRH) in emphysema (SRHIE) with rats, and to explore the inflammatory status of SRHIE in lung, liver, pancreas, carotid artery and whole blood. METHODS: Seventy-five male Wistar rats were assigned to 5 groups with 15 per group according to the exposure conditions. The protocols varied with the degree of hypoxia exposure and severity of pre-existing emphysema caused by cigarette smoke exposure: (1) SRH control (SRHCtrl) group, sham smoke exposure (smoke exposure, exposed to smoke of 15 cigarettes twice everyday, 16 weeks) and SRH exposure (12.5% O2, 3 hours, SRH exposure, divide total hypoxia time (1.5 hours or 3 hours) into 4 periods evenly (22.5 minutes or 45 minutes) and distribute these hypoxia periods evenly into physiological sleep time of rats identified by electroencephalogram, week 9 to week 16); (2) Emphysema control (ECtrl) group, smoke exposure and sham SRH exposure (21% O2, 3 hours); (3) Short SRH in emphysema (SRHShort) group, smoke exposure and short SRH exposure (12.5% O2, 1.5 hours); (4) Mild SRH in emphysema (SRHMild) group, smoke exposure and mild SRH exposure (15% O2, 3 hours); (5) Standard SRH in emphysema (SRHStand) group, smoke exposure and SRH exposure (12.5% O2, 3 hours). ECtrl, SRHShort, SRHMild and SRHStand groups were groups with emphysematous rats. Two days before the end of exposure, 5 rats in each group were randomly selected for arterial blood gas analysis. In the rest 10 rats in each group, we obtained blood samples and bronchoalveolar lavage fluid (BALF) for routine tests. We also obtained tissue blocks of lung, liver, pancreas, and right carotid artery for pathologic scoring and measurements of liver oxidative stress (measuring hepatic oxidative stress enzymes, superoxide dismutase (SOD) activity, catalase (CAT) activity and malondialdehyde (MDA) concentration). RESULTS: Emphysematous groups had higher mean linear intercept (MLI) and mean alveolar number (MAN) values than SRHCtrl group. MLI values in SRHStand group were the highest (all P < 0.05). O2Sat in SRHStand rats when SRH exposure was (83.45 ± 1.76)%. Histological scores of lung, liver, pancreas and right carotid artery were higher in emphysematous groups than SRHCtrl group, and SRHStand group were the highest (all P < 0.05) (SOD and CAT values were lower and MDA values were higher in groups with emphysema than without and in SRHStand group than in ECtrl group (all P < 0.05)). MDA values were the highest in SRHStand group (all P < 0.05). Total cellular score in BALF and White blood cell (WBC) in whole blood were the highest in SRHStand group (all P < 0.05). Lymphocyte ratios were the highest in SRHStand group both in BALF and blood (all P < 0.05). Red blood cell (RBC) and hemoglobin in emphysematous groups were higher than that in SRHCtrl group, and SRHStand group were higher than ECtrl group (all P < 0.05). CONCLUSIONS: With a proper novo model of SRHIE with Wistar rats, we have demonstrated SRH may aggravate the degree of emphysematous changes, polycythemia, oxidative stress and systematic inflammation. SRH and emphysema may have a synergistic action in causing systematic damages, and lymphocyte may be playing a central role in this process. Longer duration and more severe extent of SRHIE exposure also seem to result in more serious systematic damages. The mechanisms of all these concerned processes remain to be studied.

The effects of sleep hypoxia on coagulant factors and hepatic inflammation in emphysematous rats.

OBJECTIVES: To develop a sleep hypoxia (SH) in emphysema (SHE) rat model and to explore whether SHE results in more severe hepatic inflammation than emphysema alone and whether the inflammation changes levels of coagulant/anticoagulant factors synthesized in the liver. METHODS: Seventy-five rats were put into 5 groups: SH control (SHCtrl), treated with sham smoke exposure (16 weeks) and SH exposure (12.5% O(2), 3 h/d, latter 8 weeks); emphysema control (ECtrl), smoke exposure and sham SH exposure (21% O(2)); short SHE (SHEShort), smoke exposure and short SH exposure (1.5 h/d); mild SHE (SHEMild), smoke exposure and mild SH exposure (15% O(2)); standard SHE (SHEStand), smoke exposure and SH exposure. Therefore, ECtrl, SHEShort, SHEMild and SHEStand group were among emphysematous groups. Arterial blood gas (ABG) data was obtained during preliminary tests. After exposure, hepatic inflammation (interleukin -6 [IL-6] mRNA and protein, tumor necrosis factor α [TNFα] mRNA and protein) and liver coagulant/anticoagulant factors (antithrombin [AT], fibrinogen [FIB] and Factor VIII [F VIII]) were evaluated. SPSS 11.5 software was used for statistical analysis. RESULTS: Characteristics of emphysema were obvious in emphysematous groups and ABGs reached SH criteria on hypoxia exposure. Hepatic inflammation parameters and coagulant factors are the lowest in SHCtrl and the highest in SHEStand while AT is the highest in SHCtrl and the lowest in SHEStand. Inflammatory cytokines of liver correlate well with coagulant factors positively and with AT negatively. CONCLUSIONS: When SH is combined with emphysema, hepatic inflammation and coagulability enhance each other synergistically and produce a more significant liver-derivative inflammatory and prothrombotic status.