Background

Mechanisms underlying blood pressure changes in obstructive sleep apnoea (OSA) are incompletely understood. Increased respiratory effort is one of the main features of OSA and is associated with sympathetic overactivity, leading to increased vascular wall stiffness and remodelling. This study investigated associations between a new measure of respiratory effort (percentage of total sleep time spent with increased respiratory effort based on measurement of mandibular jaw movements (MJM): REMOV, %TST) and prevalent hypertension in adults referred for evaluation of suspected OSA.

Methods

A machine learning model was built to predict hypertension from clinical data, conventional polysomnography (PSG) indices and MJM-derived parameters (including REMOV). The model was evaluated in a training subset and a test subset.

Results

The analysis included 1127 patients: 901 (80%) in the training subset and 226 (20%) in the test subset. The prevalence of hypertension was 31% and 30%, respectively, in the training and test subsets. A risk stratification model based on 18 input features including REMOV had good accuracy for predicting prevalent hypertension (sensitivity 0.75 and specificity 0.83). Using the Shapley additive explanation method, REMOV was the best predictor of hypertension after clinical risk factors (age, sex, body mass index and neck circumference) and time with oxygen saturation <90%, ahead of standard PSG metrics (including the apnoea-hypopnoea index and oxygen desaturation index).

Conclusion

The proportion of sleep time spent with increased respiratory effort automatically derived from MJM was identified as a potential new reliable metric to predict prevalent hypertension in patients with OSA.

Monitoring unintentional air leaks in continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA) is essential for therapy success. While leaks are often attributed to improperly sealed masks, mouth openings may also cause them, requiring interventions. Recent studies demonstrated distinctive mandibular jaw movement (MJM) signal patterns during sleep related to respiratory events and sleep stages. Analysing MJM during CPAP treatment reveals air leak peaks coinciding with maximal MJM amplitude during obstructive events, and air leak decreases corresponding to arousals. Examining leaks with MJM offers valuable insights into their origins and might open new avenues for CPAP management.

Aim

To determine the association between total sleep time (TST) spent in increased respiratory effort (RE) and the prevalence of type 2 diabetes in a large cohort of individuals with suspected obstructive sleep apnoea (OSA) referred for in-laboratory polysomnography (PSG).

Materials and methods

We conducted a retrospective cross-sectional study using the clinical data of 1128 patients. Non-invasive measurements of RE were derived from the sleep mandibular jaw movements (MJM) bio-signal. An explainable machine-learning model was built to predict prevalent type 2 diabetes from clinical data, standard PSG indices, and MJM-derived parameters (including the proportion of TST spent with increased respiratory effort [REMOV [%TST]).

Results

Original data were randomly assigned to training (n = 853) and validation (n = 275) subsets. The classification model based on 18 input features including REMOV showed good performance for predicting prevalent type 2 diabetes (sensitivity = 0.81, specificity = 0.89). Post hoc interpretation using the Shapley additive explanation method found that a high value of REMOV was the most important risk factor associated with type 2 diabetes after traditional clinical variables (age, sex, body mass index), and ahead of standard PSG metrics including the apnoea-hypopnea and oxygen desaturation indices.

Conclusions

These findings show for the first time that the proportion of sleep time spent in increased RE (assessed through MJM measurements) is an important predictor of the association with type 2 diabetes in individuals with OSA.

Purpose

Differentiation between obstructive and central apneas and hypopneas requires quantitative measurement of respiratory effort (RE) using esophageal pressure (PES), which is rarely implemented. This study investigated whether the sleep mandibular movements (MM) signal recorded with a tri-axial gyroscopic chin sensor (Sunrise, Namur, Belgium) is a reliable surrogate of PES in patients with suspected obstructive sleep apnea (OSA).

Patients and methods

In-laboratory polysomnography (PSG) with PES and concurrent MM monitoring was performed. PSGs were scored manually using AASM 2012 rules. Data blocks (n=8042) were randomly sampled during normal breathing (NB), obstructive or central apnea/hypopnea (OA/OH/CA/CH), respiratory effort-related arousal (RERA), and mixed apnea (MxA). Analyses were evaluation of the similarity and linear correlation between PES and MM using the longest common subsequence (LCSS) algorithm and Pearson's coefficient; description of signal amplitudes; estimation of the marginal effect for crossing from NB to a respiratory disturbance for a given change in MM signal using a mixed linear-regression.

Results

Participants (n=38) had mild to severe OSA (median AH index 28.9/h; median arousal index 23.2/h). MM showed a high level of synchronization with concurrent PES signals. Distribution of MM amplitude differed significantly between event types: median (95% confidence interval) values of 0.60 (0.16-2.43) for CA, 0.83 (0.23-4.71) for CH, 1.93 (0.46-12.43) for MxA, 3.23 (0.72-18.09) for OH, and 6.42 (0.88-26.81) for OA. Mixed regression indicated that crossing from NB to central events would decrease MM signal amplitude by -1.23 (CH) and -2.04 (CA) units, while obstructive events would increase MM amplitude by +3.27 (OH) and +6.79 (OA) units (all p<10^-6).

Conclusion

In OSA patients, MM signals facilitated the measurement of specific levels of RE associated with obstructive, central or mixed apneas and/or hypopneas. A high degree of similarity was observed with the PES gold-standard signal.

Rationale: Oral appliances are second-line treatments after continuous positive airway pressure for obstructive sleep apnea (OSA) management. However, the need for oral appliance titration limits their use as a result of monitoring challenges to assess the treatment effect on OSA. Objectives: To assess the validity of mandibular jaw movement (MJM) automated analysis compared with polysomnography (PSG) and polygraphy (PG) in evaluating the effect of oral appliance treatment and the effectiveness of MJM monitoring for oral appliance titration at home in patients with OSA. Methods: This observational, prospective study included 135 patients with OSA eligible for oral appliance therapy. The primary outcome was the apnea-hypopnea index (AHI), measured through in-laboratory PSG/PG and MJM-based technology. Additionally, MJM monitoring at home was conducted at regular intervals during the titration process. The agreement between PSG/PG and MJM automated analysis was revaluated using Bland-Altman analysis. Changes in AHI during the home-based oral appliance titration process were evaluated using a generalized linear mixed model and a generalized estimating equation model. Results: The automated MJM analysis demonstrated strong agreement with PG in assessing AHI at the end of titration, with a median bias of 0.24/h (limits of agreement, -11.2 to 12.8/h). The improvement of AHI from baseline in response to oral appliance treatment was consistent across three evaluation conditions: in-laboratory PG (-59.6%; 95% confidence interval, -59.8% to -59.5%), in-laboratory automated MJM analysis (-59.2%; -65.2% to -52.2%), and at-home automated MJM analysis (-59.7%; -67.4% to -50.2%). Conclusions: Incorporating MJM automated analysis into the oral appliance titration process has the potential to optimize oral appliance therapy outcomes for OSA.

Over recent years, positive airway pressure (PAP) remote monitoring has transformed the management of OSA and produced a large amount of data. Accumulated PAP data provide valuable and objective information regarding patient treatment adherence and efficiency. However, the majority of studies that have analyzed longitudinal PAP remote monitoring have summarized data trajectories in static and simplistic metrics for PAP adherence and the residual apnea-hypopnea index by the use of mean or median values. The aims of this article are to suggest directions for improving data cleaning and processing and to address major concerns for the following data science applications: (1) conditions for residual apnea-hypopnea index reliability, (2) lack of standardization of indicators provided by different PAP models, (3) missing values, and (4) consideration of treatment interruptions. To allow fair comparison among studies and to avoid biases in computation, PAP data processing and management should be conducted rigorously with these points in mind. PAP remote monitoring data contain a wealth of information that currently is underused in the field of sleep research. Improving the quality and standardizing data handling could facilitate data sharing among specialists worldwide and enable artificial intelligence strategies to be applied in the field of sleep apnea.

BACKGROUND: SARS-CoV-2 binding to ACE2 is potentially associated with severe pneumonia due to COVID-19. The aim of the study was to test whether Mas-receptor activation by 20-hydroxyecdysone (BIO101) could restore the Renin-Angiotensin System equilibrium and limit the frequency of respiratory failure and mortality in adults hospitalized with severe COVID-19. METHODS: Double-blind, randomized, placebo-controlled phase 2/3 trial. Randomization: 1:1 oral BIO101 (350 mg BID) or placebo, up to 28 days or until an endpoint was reached. Primary endpoint: mortality or respiratory failure requiring high-flow oxygen, mechanical ventilation, or extra-corporeal membrane oxygenation. Key secondary endpoint: hospital discharge following recovery (ClinicalTrials.gov Number, NCT04472728). FINDINGS: Due to low recruitment the planned sample size of 310 was not reached and 238 patients were randomized between August 26, 2020 and March 8, 2022. In the modified ITT population (233 patients; 126 BIO101 and 107 placebo), respiratory failure or early death by day 28 was 11.4% lower in the BIO101 (13.5%) than in the placebo (24.3%) group, (p = 0.0426). At day 28, proportions of patients discharged following recovery were 80.1%, and 70.9% in the BIO101 and placebo group respectively, (adjusted difference 11.0%, 95% CI [-0.4%, 22.4%], p = 0.0586). Hazard Ratio for time to death over 90 days: 0.554 (95% CI [0.285, 1.077]), a 44.6% mortality reduction in the BIO101 group (not statistically significant). Treatment emergent adverse events of respiratory failure were more frequent in the placebo group. INTERPRETATION: BIO101 significantly reduced the risk of death or respiratory failure supporting its use in adults hospitalized with severe respiratory symptoms due to COVID-19. FUNDING: Biophytis.