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.

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.

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.

Obstructive sleep apnea and chronic intermittent hypoxia: a review.

Hypoxia is an important topic both physiologically and clinically. Traditionally, physiology research has been focusing on the effect of acute and chronic sustained hypoxia and human adaptive response to high altitude. In the past 20 years, genetic studies by many have expanded our understanding of hypoxia to the molecular level. However, in contrast to our extensive knowledge about acute and chronic sustained hypoxia, we know relatively little about the effect of chronic intermittent hypoxia (CIH). In recent years, CIH has attracted more research attention because of the increasing prevalence of obesity and obstructive sleep apnea (OSA) in the western countries. Clinically, CIH is commonly seen in patients with sleep-disordered breathing including OSA, Cheyne-Stokes respiration and nocturnal hypoventilation. It was estimated that for OSA of at least mild severity prevalence estimates range from 3 to 28% in the general population. OSA is characterized by recurrent upper airway collapse during sleep leading to intermittent nocturnal hypoxia and sleep fragmentation. OSA is associated with significant mortality and morbidity including neurocognitive dysfunction, hypertension, many cardiovascular disorders and metabolic disorders such as diabetes and metabolic syndrome. The intermittent hypoxia in OSA closely mimics what is seen in the ischemia-reperfusion injury. Experimentally, there is no universally accepted definition for CIH. Laboratory protocols vary greatly in duration of hypoxia exposure, numbers of hypoxia episodes per day and the total number of days of exposure. Despite the lack of a uniform definition, recent data suggest that CIH may lead to multiple long-term pathophysiologic consequences similar to what we see in patients with OSA. Recent evidences also demonstrate that there are remarkable differences in the response of the physiologic systems to sustained hypoxia and intermittent hypoxia. This review is aimed to briefly discuss the clinical significance of sleep-disordered breathing and our current understanding of CIH.

Prediction of postoperative lung function in patients with lung cancer: comparison of quantitative CT with perfusion scintigraphy.

OBJECTIVE: Prediction of postoperative lung function is important in preoperative evaluation of patients with lung cancer. Perfusion scintigraphy is the current method to assess the fractional contribution of lung function of the remaining lung. We developed a quantitative CT method and compared it with perfusion scintigraphy for predictions of postoperative forced expiratory volume in 1 sec (FEV1) in patients with lung cancer. SUBJECTS AND METHODS: Forty-four patients with lung cancer undergoing lung resection with preoperative CT and perfusion scintigraphy were enrolled. Quantitative CT used a dual threshold (-500 and -910 H) on standard preoperative CT to semiautomatically extract lung volume without emphysema or tumor and atelectasis, which we defined as "functional lung volume." Prediction was calculated from preoperative FEV1 multiplied by the fractional contribution of functional lung volume of the remaining lung by quantitative CT. Perfusion scintigraphy was the standard method. Predictions were correlated with postoperatively measured FEV1. RESULTS: Both quantitative CT and perfusion scintigraphy predicted postoperative FEV1 well in patients who underwent pneumonectomy (n = 28, r = 0.88 vs r = 0.86) and in lobectomy (n = 16, r = 0.90 vs r = 0.80) (both, p < 0.001). There was good agreement between the two methods by the Bland-Altman method. In the four patients with low measured postoperative FEV1 (<40% predicted normal), quantitative CT had true-positive prediction in four and perfusion scintigraphy, in only two. CONCLUSION: Given its simplicity, we proposed that quantitative CT be widely used in predicting postoperative FEV1.