Analysis of mandibular jaw movements to assess ventilatory support management of children with obstructive sleep apnea syndrome treated with positive airway pressure therapies.

BACKGROUND: The polysomnography (PSG) is the gold-standard for obstructive sleep apnea (OSA) syndrome diagnosis and assessment under positive airway pressure (PAP) therapies in children. Recently, an innovative digital medicine solution, including a mandibular jaw movement (MJM) sensor coupled with automated analysis, has been validated as an alternative to PSG for pediatric application. OBJECTIVE: This study aimed to assess the reliability of MJM automated analysis for the assessment of residual apnea/hypopnea events during sleep in children with OSA treated with noninvasive ventilation (NIV) or continuous PAP (CPAP). METHODS: In this open-label prospective non-randomized multicentric trial, we included children aged from 5 to 18 years with a diagnosis of severe OSA. The children underwent in-laboratory PSG with simultaneous MJM monitoring and at-home recording with MJM monitoring 3 months later. Agreement between PSG and MJM analysis in measuring the residual apnea-hypopnea index (AHI) was evaluated by the Bland-Altman method. The treatment effect on residual AHI was estimated for both PSG and MJM analysis. RESULTS: Fifteen (60% males) children were included with a median age of 12 years [interquartile range 8-15]. Two (17%) were ventilated with NIV and 13 (83%) with CPAP. There was a good agreement between MJM-AHI and PSG-AHI with a median bias of -0.25 (95% CI: -3.40 to +2.04) events/h. The reduction in AHI under treatment was consistently significant across the three measurement methods: in-laboratory PSG and MJM recordings in the laboratory and at home. CONCLUSION: Automated analysis of MJM is a highly reliable alternative method to assess residual events in a small population treated with PAP therapies.

Efficacy of oral 20-hydroxyecdysone (BIO101), a MAS receptor activator, in adults with severe COVID-19 (COVA): a randomized, placebo-controlled, phase 2/3 trial.

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.

Mandibular Jaw Movement Automated Analysis for Oral Appliance Monitoring in Obstructive Sleep Apnea: A Prospective Cohort Study.

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.

Insights on mandibular jaw movements during polysomnography in obstructive sleep apnea.

A strong and specific comprehensive physiological association has been documented between mandibular jaw movements and related periods of normal or disturbed breathing across different sleep stages. The mandibular jaw movement biosignal can be incorporated in the polysomnography, displayed on the screen as a function of time like any standard polysomnography signal (eg, airflow, oxygen saturation, respiratory inductance plethysmography bands) and interpreted in the context of the target period of breathing and its associated respiratory effort level. Overall, the mandibular jaw movement biosignal that depicts the muscular trigeminal respiratory drive is a highly effective tool for differentiating between central and obstructive sleep episodes including hypopneas and for providing clinicians with valuable insights into wake/sleep states, arousals, and sleep stages. These fundamental characteristics of the mandibular jaw movement biosignal contrast with photoplethysmography, airflow, or oxygen saturation signals that provide information more about the consequence of the disturbed breathing episode than about the event itself. CITATION: Malhotra A, Martinot J-B, Pépin J-L. Insights on mandibular jaw movements during polysomnography in obstructive sleep apnea. J Clin Sleep Med. 2024;20(1):151-163.

Determinants of apnea-hypopnea index variability during home sleep testing.

BACKGROUND: A single-night attended in-laboratory polysomnography or home sleep testing are common approaches for obstructive sleep apnea (OSA) diagnosis. However, internight variability in apnea-hypopnea index value is common, and may result in misclassification of OSA severity and inapropriate treatment decisions. OBJECTIVE: To investigate factors determining short-term apnea-hypopnea index variability using multi-night automated home sleep testing, and to determine how this variability impacts clinical decisions. PATIENTS/METHODS: Adults with suspected OSA who successfully performed three home sleep tests using measurements of mandibular jaw movements (Sunrise, Namur, Belgium) combined with automated machine learning analysis were enrolled. Data analysis included principal component analysis, generalized estimating equation regression and qualitative agreement analysis. RESULTS: 160 individuals who performed three sleep tests over a mean of 8.78 ± 8.48 days were included. The apnea-hypopnea index varied by -0.88 events/h (5th-95th percentile range: -14.33 to 9.72 events/h). Based on a single-night recording, rates of overtreatment and undertreatment would have been of 13.5% and 6.0%, respectively. Regression analysis adjusted for age, sex, body mass index, total sleep time, and time between home sleep tests showed that time spent in deep non-rapid eye movement sleep and with head in supine position were independent significant predictors of the apnea-hypopnea index variability. CONCLUSIONS: At the individual level, short-term internight variability in the apnea-hypopnea index was significantly associated with time spent in deep non-rapid eye movement sleep and head in supine position. Clinical decisions based on a single-night testing may lead to errors in OSA severity classification and incorrect therapeutic decisions.

Creating an Optimal Approach for Diagnosing Sleep Apnea.

Sleep apnea is nowadays recognized as a treatable chronic disease and awareness of it has increased, leading to an upsurge in demand for diagnostic testing. Conventionally, diagnosis depends on overnight polysomnography in a sleep clinic, which is highly human-resource intensive and ignores the night-to-night variability in classical sleep apnea markers, such as the apnea-hypopnea index. In this review, the authors summarize the main improvements that could be made in the sleep apnea diagnosis strategy; how technological innovations and multi-night home testing could be used to simplify, increase access, and reduce costs of diagnostic testing while avoiding misclassification of severity.

Respiratory effort during sleep and the rate of prevalent type 2 diabetes in obstructive sleep apnoea.

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.

Respiratory effort during sleep and prevalent hypertension in obstructive sleep apnoea.

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.

Remote Monitoring of Positive Airway Pressure Data: Challenges, Pitfalls, and Strategies to Consider for Optimal Data Science Applications.

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.

Mandibular Movements are a Reliable Noninvasive Alternative to Esophageal Pressure for Measuring Respiratory Effort in Patients with Sleep Apnea Syndrome.

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.

Diagnosis of Sleep Apnoea Using a Mandibular Monitor and Machine Learning Analysis: One-Night Agreement Compared to in-Home Polysomnography.

Background: The capacity to diagnose obstructive sleep apnoea (OSA) must be expanded to meet an estimated disease burden of nearly one billion people worldwide. Validated alternatives to the gold standard polysomnography (PSG) will improve access to testing and treatment. This study aimed to evaluate the diagnosis of OSA, using measurements of mandibular movement (MM) combined with automated machine learning analysis, compared to in-home PSG. Methods: 40 suspected OSA patients underwent single overnight in-home sleep testing with PSG (Nox A1, ResMed, Australia) and simultaneous MM monitoring (Sunrise, Sunrise SA, Belgium). PSG recordings were manually analysed by two expert sleep centres (Grenoble and London); MM analysis was automated. The Obstructive Respiratory Disturbance Index calculated from the MM monitoring (MM-ORDI) was compared to the PSG (PSG-ORDI) using intraclass correlation coefficient and Bland-Altman analysis. Receiver operating characteristic curves (ROC) were constructed to optimise the diagnostic performance of the MM monitor at different PSG-ORDI thresholds (5, 15, and 30 events/hour). Results: 31 patients were included in the analysis (58% men; mean (SD) age: 48 (15) years; BMI: 30.4 (7.6) kg/m2). Good agreement was observed between MM-ORDI and PSG-ORDI (median bias 0.00; 95% CI -23.25 to + 9.73 events/hour). However, for 15 patients with no or mild OSA, MM monitoring overestimated disease severity (PSG-ORDI < 5: MM-ORDI mean overestimation + 5.58 (95% CI + 2.03 to + 7.46) events/hour; PSG-ORDI > 5-15: MM-ORDI overestimation + 3.70 (95% CI -0.53 to + 18.32) events/hour). In 16 patients with moderate-severe OSA (n = 9 with PSG-ORDI 15-30 events/h and n = 7 with a PSG-ORD > 30 events/h), there was an underestimation (PSG-ORDI > 15: MM-ORDI underestimation -8.70 (95% CI -28.46 to + 4.01) events/hour). ROC optimal cut-off values for PSG-ORDI thresholds of 5, 15, 30 events/hour were: 9.53, 12.65 and 24.81 events/hour, respectively. These cut-off values yielded a sensitivity of 88, 100 and 79%, and a specificity of 100, 75, 96%. The positive predictive values were: 100, 80, 95% and the negative predictive values 89, 100, 82%, respectively. Conclusion: The diagnosis of OSA, using MM with machine learning analysis, is comparable to manually scored in-home PSG. Therefore, this novel monitor could be a convenient diagnostic tool that can easily be used in the patients' own home. Clinical Trial Registration: https://clinicaltrials.gov, identifier NCT04262557.

Azithromycin for treatment of hospitalised COVID-19 patients: a randomised, multicentre, open-label clinical trial (DAWn-AZITHRO).

BACKGROUND AND OBJECTIVES: Azithromycin was rapidly adopted as a repurposed drug to treat coronavirus disease 2019 (COVID-19) early in the pandemic. We aimed to evaluate its efficacy in patients hospitalised for COVID-19. METHODS: In a series of randomised, open-label, phase 2 proof-of-concept, multicentre clinical trials (Direct Antivirals Working against the novel coronavirus (DAWn)), several treatments were compared with standard of care. In 15 Belgian hospitals, patients hospitalised with moderate to severe COVID-19 were allocated 2:1 to receive standard of care plus azithromycin or standard of care alone. The primary outcome was time to live discharge or sustained clinical improvement, defined as a two-point improvement on the World Health Organization (WHO) ordinal scale sustained for at least 3 days. RESULTS: Patients were included between April 22 and December 17, 2020. When 15-day follow-up data were available for 160 patients (56% of preset cohort), an interim analysis was performed at request of the independent Data Safety and Monitoring Board. Subsequently, DAWn-AZITHRO was stopped for futility. In total, 121 patients were allocated to the treatment arm and 64 patients to the standard-of-care arm. We found no effect of azithromycin on the primary outcome with a hazard ratio of 1.044 (95% CI 0.772-1.413; p=0.7798). None of the predefined subgroups showed significant interaction as covariates in the Fine-Gray regression analysis. No benefit of azithromycin was found on any of the short- and longer-term secondary outcomes. CONCLUSION: Time to clinical improvement is not influenced by azithromycin in patients hospitalised with moderate to severe COVID-19.

AKR1B10, One of the Triggers of Cytokine Storm in SARS-CoV2 Severe Acute Respiratory Syndrome.

Preventing the cytokine storm observed in COVID-19 is a crucial goal for reducing the occurrence of severe acute respiratory failure and improving outcomes. Here, we identify Aldo-Keto Reductase 1B10 (AKR1B10) as a key enzyme involved in the expression of pro-inflammatory cytokines. The analysis of transcriptomic data from lung samples of patients who died from COVID-19 demonstrates an increased expression of the gene encoding AKR1B10. Measurements of the AKR1B10 protein in sera from hospitalised COVID-19 patients suggests a significant link between AKR1B10 levels and the severity of the disease. In macrophages and lung cells, the over-expression of AKR1B10 induces the expression of the pro-inflammatory cytokines Interleukin-6 (IL-6), Interleukin-1ß (IL-1ß) and Tumor Necrosis Factor a (TNFα), supporting the biological plausibility of an AKR1B10 involvement in the COVID-19-related cytokine storm. When macrophages were stressed by lipopolysaccharides (LPS) exposure and treated by Zopolrestat, an AKR1B10 inhibitor, the LPS-induced production of IL-6, IL-1ß, and TNFα is significantly reduced, reinforcing the hypothesis that the pro-inflammatory expression of cytokines is AKR1B10-dependant. Finally, we also show that AKR1B10 can be secreted and transferred via extracellular vesicles between different cell types, suggesting that this protein may also contribute to the multi-organ systemic impact of COVID-19. These experiments highlight a relationship between AKR1B10 production and severe forms of COVID-19. Our data indicate that AKR1B10 participates in the activation of cytokines production and suggest that modulation of AKR1B10 activity might be an actionable pharmacological target in COVID-19 management.

Efficacy of the adjuvanted subunit protein COVID-19 vaccine, SCB-2019: a phase 2 and 3 multicentre, double-blind, randomised, placebo-controlled trial.

BACKGROUND: A range of safe and effective vaccines against SARS CoV 2 are needed to address the COVID 19 pandemic. We aimed to assess the safety and efficacy of the COVID-19 vaccine SCB-2019. METHODS: This ongoing phase 2 and 3 double-blind, placebo-controlled trial was done in adults aged 18 years and older who were in good health or with a stable chronic health condition, at 31 sites in five countries (Belgium, Brazil, Colombia, Philippines, and South Africa). The participants were randomly assigned 1:1 using a centralised internet randomisation system to receive two 0·5 mL intramuscular doses of SCB-2019 (30 µg, adjuvanted with 1·50 mg CpG-1018 and 0·75 mg alum) or placebo (0·9% sodium chloride for injection supplied in 10 mL ampoules) 21 days apart. All study staff and participants were masked, but vaccine administrators were not. Primary endpoints were vaccine efficacy, measured by RT-PCR-confirmed COVID-19 of any severity with onset from 14 days after the second dose in baseline SARS-CoV-2 seronegative participants (the per-protocol population), and the safety and solicited local and systemic adverse events in the phase 2 subset. This study is registered on EudraCT (2020-004272-17) and ClinicalTrials.gov (NCT04672395). FINDINGS: 30 174 participants were enrolled from March 24, 2021, until the cutoff date of Aug 10, 2021, of whom 30 128 received their first assigned vaccine (n=15 064) or a placebo injection (n=15 064). The per-protocol population consisted of 12 355 baseline SARS-CoV-2-naive participants (6251 vaccinees and 6104 placebo recipients). Most exclusions (13 389 [44·4%]) were because of seropositivity at baseline. There were 207 confirmed per-protocol cases of COVID-19 at 14 days after the second dose, 52 vaccinees versus 155 placebo recipients, and an overall vaccine efficacy against any severity COVID-19 of 67·2% (95·72% CI 54·3-76·8), 83·7% (97·86% CI 55·9-95·4) against moderate-to-severe COVID-19, and 100% (97·86% CI 25·3-100·0) against severe COVID-19. All COVID-19 cases were due to virus variants; vaccine efficacy against any severity COVID-19 due to the three predominant variants was 78·7% (95% CI 57·3-90·4) for delta, 91·8% (44·9-99·8) for gamma, and 58·6% (13·3-81·5) for mu. No safety issues emerged in the follow-up period for the efficacy analysis (median of 82 days [IQR 63-103]). The vaccine elicited higher rates of mainly mild-to-moderate injection site pain than the placebo after the first (35·7% [287 of 803] vs 10·3% [81 of 786]) and second (26·9% [189 of 702] vs 7·4% [52 of 699]) doses, but the rates of other solicited local and systemic adverse events were similar between the groups. INTERPRETATION: Two doses of SCB-2019 vaccine plus CpG and alum provides notable protection against the entire severity spectrum of COVID-19 caused by circulating SAR-CoV-2 viruses, including the predominating delta variant. FUNDING: Clover Biopharmaceuticals and the Coalition for Epidemic Preparedness Innovations.

Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea.

BACKGROUND: Given the high prevalence and risk for outcomes associated with pediatric obstructive sleep apnea (OSA), there is a need for simplified diagnostic approaches. A prospective study in 140 children undergoing in-laboratory polysomnography (PSG) evaluates the accuracy of a recently developed system (Sunrise) to estimate respiratory efforts by monitoring sleep mandibular movements (MM) for the diagnosis of OSA (Sunrise™). METHODS: Diagnosis and severity were defined by an obstructive apnea/hypopnea index (OAHI) ≥ 1 (mild), ≥ 5 (moderate), and ≥ 10 events/h (severe). Agreement between PSG and Sunrise™ was assessed by Bland-Altman method comparing respiratory disturbances hourly index (RDI) (obstructive apneas, hypopneas, and respiratory effort-related arousals) during PSG (PSG_RDI), and Sunrise RDI (Sr_RDI). Performance of Sr_RDI was determined via ROC curves evaluating the device sensitivity and specificity at PSG_OAHI ≥ 1, 5, and 15 events/h. RESULTS: A median difference of 1.57 events/h, 95% confidence interval: -2.49 to 8.11 was found between Sr_RDI and PSG_RDI. Areas under the ROC curves of Sr_RDI were 0.75 (interquartile range [IQR]: 0.72-0.78), 0.90 (IQR: 0.86-0.92) and 0.95 (IQR: 0.90-0.99) for detecting children with PSG_OAHI ≥ 1, PSG_OAHI ≥ 5, or PSG_ OAHI ≥ 10, respectively. CONCLUSION: MM automated analysis shows significant promise to diagnose moderate-to-severe pediatric OSA.

Artificial Intelligence Analysis of Mandibular Movements Enables Accurate Detection of Phasic Sleep Bruxism in OSA Patients: A Pilot Study.

PURPOSE: Sleep bruxism (SBx) activity is classically identified by capturing masseter and/or temporalis masticatory muscles electromyographic activity (EMG-MMA) during in-laboratory polysomnography (PSG). We aimed to identify stereotypical mandibular jaw movements (MJM) in patients with SBx and to develop rhythmic masticatory muscles activities (RMMA) automatic detection using an artificial intelligence (AI) based approach. PATIENTS AND METHODS: This was a prospective, observational study of 67 suspected obstructive sleep apnea (OSA) patients in whom PSG with masseter EMG was performed with simultaneous MJM recordings. The system used to collect MJM consisted of a small hardware device attached on the chin that communicates to a cloud-based infrastructure. An extreme gradient boosting (XGB) multiclass classifier was trained on 79,650 10-second epochs of MJM data from the 39 subjects with a history of SBx targeting 3 labels: RMMA episodes (n=1072), micro-arousals (n=1311), and MJM occurring at the breathing frequency (n=77,267). RESULTS: Validated on unseen data from 28 patients, the model showed a very good epoch-by-epoch agreement (Kappa = 0.799) and balanced accuracy of 86.6% was found for the MJM events when using RMMA standards. The RMMA episodes were detected with a sensitivity of 84.3%. Class-wise receiver operating characteristic (ROC) curve analysis confirmed the well-balanced performance of the classifier for RMMA (ROC area under the curve: 0.98, 95% confidence interval [CI] 0.97-0.99). There was good agreement between the MJM analytic model and manual EMG signal scoring of RMMA (median bias -0.80 events/h, 95% CI -9.77 to 2.85). CONCLUSION: SBx can be reliably identified, quantified, and characterized with MJM when subjected to automated analysis supported by AI technology.

Detecting COVID-19 and other respiratory infections in obstructive sleep apnoea patients through CPAP device telemonitoring.

OBJECTIVE: The earliest possible detection of individuals with COVID-19 has been essential to curb the spread of infection. Existing digital tools have been scaled up to address this issue. Every night telemonitoring data on continuous positive airway pressure (CPAP) device use, the first-line therapy for obstructive sleep apnoea (OSA), is collected worldwide. We asked whether the changes in CPAP adherence patterns of might constitute an alert for COVID-19. METHODS: We analysed preliminary results of telemonitoring data, recorded between February 1 and April 30, 2020, on OSA patients followed by our sleep clinics and diagnosed with COVID-19. RESULTS: CPAP telemonitoring data from the first 19 patients diagnosed with COVID-19 showed a clear decrease or halt in adherence in the 20 days immediately preceding COVID-19 diagnosis compared to an earlier period (p < 0.01). CONCLUSION: Patterns of continuous positive airway pressure device use by obstructive sleep apnoea patients collected through telemonitoring can indicate the onset of COVID-19 symptoms. Existing telemonitoring platforms could be immediately used to screen for COVID-19, and for other respiratory infections, in this large at-risk population.