Mandible behaviour interpretation during wakefulness, sleep and sleep-disordered breathing

Air leak phenotyping by mandibular jaw movement analysis in CPAP therapy: Key insights for practitioners

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.

Mandibular movement monitor provides faster, yet accurate diagnosis for obstructive sleep apnoea: A randomised controlled study

Many patients with obstructive sleep apnoea (OSA) remain undiagnosed and thus untreated, and in part this relates to delay in diagnosis. Novel diagnostic strategies may improve access to diagnosis. In a multicentre, randomised study, we evaluated time to treatment decision in patients referred for suspected OSA, comparing a mandibular movement (MM) monitor to respiratory polygraphy, the most commonly used OSA detection method in the UK. Adults with high pre-test probability OSA were recruited from both northern Scotland and London. 40 participants (70 % male, mean±SD age 46.8 ± 12.9 years, BMI 36.9 ± 7.5 kg/m2, ESS 14.9 ± 4.1) wore a MM monitor and respiratory polygraphy simultaneously overnight and were randomised (1:1) to receive their treatment decision based on results from either device. Compared to respiratory polygraphy, MM monitor reduced time to treatment decision by 6 days (median(IQR): 13.5 (7.0-21.5) vs. 19.5 (13.7-35.5) days, P = 0.017) and saved an estimated 29 min of staff time per patient.

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

Polysomnographic Plethysmography Excursions are Reduced in Obese Elderly Men

Sleep apnea is a widespread disorder and is defined by the complete or partial cessation of breathing. Obstructive sleep apnea (OSA) is caused by an obstruction in the upper airway while central sleep apnea (CSA) is characterized by a diminished or absent respiratory effort. It is crucial to differentiate between these respiratory subtypes as they require radically different treatments. Currently, diagnostic polysomnography (PSG) is used to determine respiratory thoracic and abdominal movement patterns using plethysmography belt signals, to distinguish between OSA and CSA. There is significant manual technician interrater variability between these classifications, especially in the evaluation of CSA. We hypothesize that an increased body mass index (BMI) will cause decreased belt signal excursions that increase false scorings of CSA. The hypothesis was investigated by calculating the envelope as a continuous signal of belt signals in 2833 subjects from the MrOS Sleep Study and extracting a mean value of each of the envelopes for each subject. Using linear regression, we found that an increased BMI was associated with lower excursions during REM sleep (-0.013 [mV] thoracic and -0.018 [mV] abdominal, per BMI) and non-REM (-0.014 [mV] thoracic and -0.012 [mV] abdominal, per BMI). We conclude that increased BMI leads to lower excursions in the belt signals during event-free sleep, and that OSA and CSA events are harder to distinguish in subjects with high BMI. This has a major implication for the correct identification of CSA/OSA and its treatment.

Craniofacial morphology/phenotypes influence on mandibular range of movement in the design of a mandibular advancement device

Background

The mandibular opening path movements have different directions according to the craniofacial morphology of the patient but always downward and backward, therefore increasing the collapse of the upper airway. The aim of this work is to determine if there is a relationship between the craniofacial morphology and the mandibular movement to help understand the impact on the mandibular position.

Methods

52 students with full permanent dentition aged 19 to 23 years (mean 21.3 SD 1.7; 29 females and 23 males), participated in the study. Each subject had a lateral cephalometric radiograph taken. The opening angle was determined for two levels of vertical openings at 5 and 10 mm.

Results

The opening angle showed a greater variability between subjects ranging from 63.15 to 77.08 for 5 mm angle and from for 61.65 to 75.72 for the 10 mm angle. Differences of facial phenotypes was evident when comparing the individual dissoccluding angle of the low angle horizontal pattern and high angle vertical pattern.

Conclusions

The opening angle is related to craniofacial morphology with higher vertical anterior and shorter anteroposterior faces having a more horizontal path of mandibular movement than shorter vertical anterior and longer anteroposterior subjects who have a more vertical path.

Mandibular movement analisys by means of a kinematic model applied to the design of oral appliances for the treatment of obstructive sleep apnea

BACKGROUND

Mandibular advancement devices (MADs) are one of the treatment options used for the obstructive sleep apnea syndrome (OSAS). At present, MADs are designed with standard titration systems, without considering each patient's anatomical characteristics of the temporomandibular joint and mandible shape. The main objective of this study is to evaluate if a variability in mandibular morphology will influence the displacement of the jaw with a MAD. Such knowledge will be of help to find optimal mandibular positions with MAD even when opening the mouth.

METHODS

By using a mandibular movement model, the movement patterns of different points on the chin have been analyzed. The influence of different skeletal mandibular shapes on these movements have also been studied. The results show differences in the movement patterns of the lower front teeth depending on its distance to the center of the condyle, with a more horizontal direction in those in which there is a greater distance.

RESULTS

Variations in mandibular morphology imply differences in movement patterns of the analyzed points of the mandible. Consequently, MADs should be designed according to each patient's anatomy to avoid mandibular retrusion in those areas that may narrow the upper airways.

CONCLUSIONS

This study may help to understand why not all patients move their lower jaws forwards equally with the same degree of mandibular protrusion measured in relation to the teeth. These results might also partially explain why airway obstruction is more severe in certain untreated sleep apnea subjects than in others when opening their mouth during sleep.

Synchronized mandibular movement and capnography: a novel approach to obstructive airway detection during procedural sedation-a post hoc analysis of a prospective study

Perioperative complications related to obstructive sleep apnea still occur despite the use of partial pressure end-tidal CO₂[Formula: see text] and pulse oximetry. Airway obstruction can complicate propofol sedation and a novel monitor combining mandibular movement analysis with capnography may facilitate its detection. Patients scheduled for sleep endoscopy were recruited and monitored with standard monitoring, [Formula: see text] and Jaw Activity (JAWAC) mandibular movement sensors. A post hoc analysis investigated airway obstruction prediction using a Respiratory Effort Sequential Detection Algorithm (RESDA) based on [Formula: see text] and mandibular movement signals. 21 patients were recruited and 54 episodes of airway obstruction occurred. RESDA detected obstructive apnea [mean ± SD (median)] 29 ± 29 (21) s, p < 0.0001, before [Formula: see text] alone. This prolonged the time between obstructive apnea detection and decrease to 90% oxygen saturation 64 ± 38 (54) versus 38 ± 20 (35) s, p < 0.0001. It predicted airway obstruction with a sensitivity and specificity of 81% and 93%, respectively. The RESDA algorithm, which is based on the combination of capnography with mandibular movement assessment of respiratory effort, can more rapidly alarm anesthetists of airway obstruction during propofol sedation than [Formula: see text] alone. However, [Formula: see text] pulse oximetry, and clinical monitoring are still required.Trial Registry numbers: ClinicalTrial.gov (NCT02909309) https://clinicaltrials.gov/ct2/show/NCT02909309 .

Monitoring mandibular movements to detect Cheyne-Stokes Breathing

Background

The patterns of mandibular movements (MM) during sleep can be used to identify increased respiratory effort periodic large-amplitude MM (LPM), and cortical arousals associated with “sharp” large-amplitude MM (SPM). We hypothesized that Cheyne Stokes breathing (CSB) may be identified by periodic abnormal MM patterns. The present study aims to evaluate prospectively the concordance between CSB detected by periodic MM and polysomnography (PSG) as gold-standard.

The present study aims to evaluate prospectively the concordance between CSB detected by periodic MM and polysomnography (PSG) as gold-standard.

Methods

In 573 consecutive patients attending an in-laboratory PSG for suspected sleep disordered breathing (SDB), MM signals were acquired using magnetometry and scored manually while blinded from the PSG signal. Data analysis aimed to verify the concordance between the CSB identified by PSG and the presence of LPM or SPM. The data were randomly divided into training and validation sets (985 5-min segments/set) and concordance was evaluated using 2 classification models.

Results

In PSG, 22 patients (mean age ± SD: 65.9 ± 15.0 with a sex ratio M/F of 17/5) had CSB (mean central apnea hourly indice ± SD: 17.5 ± 6.2) from a total of 573 patients with suspected SDB. When tested on independent subset, the classification of CSB based on LPM and SPM is highly accurate (Balanced-accuracy = 0.922, sensitivity = 0.922, specificity = 0.921 and error-rate = 0.078). Logistic models based odds-ratios for CSB in presence of SPM or LPM were 172.43 (95% CI: 88.23–365.04; p < 0.001) and 186.79 (95% CI: 100.48–379.93; p < 0.001), respectively.

Conclusion

CSB in patients with sleep disordered breathing could be accurately identified by a simple magnetometer device recording mandibular movements.

Validation of midsagittal jaw movements to measure sleep in healthy adults by comparison with actigraphy and polysomnography

OBJECTIVE

In a device based on midsagittal jaw movements analysis, we assessed a sleep-wake automatic detector as an objective method to measure sleep in healthy adults by comparison with wrist actigraphy against polysomnography (PSG).

METHODS

Simultaneous and synchronized in-lab PSG, wrist actigraphy and jaw movements were carried out in 38 healthy participants. Epoch by epoch analysis was realized to assess the ability to sleep-wake distinction. Sleep parameters as measured by the three devices were compared. This included three regularly reported parameters: total sleep time, sleep onset latency, and wake after sleep onset. Also, two supplementary parameters, wake during sleep period and latency time, were added to measure quiet wakefulness state.

RESULTS

The jaw movements showed sensitivity level equal to actigraphy 96% and higher specificity level (64% and 48% respectively). The level of agreement between the two devices was high (87%). The analysis of their disagreement by discrepant resolution analysis used PSG as resolver revealed that jaw movements was right (58.9%) more often than actigraphy (41%). In sleep parameters comparison, the coefficient correlation of jaw movements was higher than actigraphy in all parameters. Moreover, its ability to distinct sleep-wake state allowed for a more effective estimation of the parameters that measured the quiet wakefulness state.

CONCLUSIONS

Midsagittal jaw movements analysis is a reliable method to measure sleep. In healthy adults, this device proved to be superior to actigraphy in terms of estimation of all sleep parameters and distinction of sleep-wake status.