Sleep-disordered breathing diagnosis: a comprehensive audit of home sleep testing in real clinical settings

PURPOSE

This study aimed to investigate the indications and outcomes of Home Sleep Testing (HST) for patients with suspected obstructive sleep apnea (OSA), aligning with guidelines set forth by the American Academy of Sleep Medicine and the European Sleep Research Society. Specifically, we aimed to audit whether validated type 3 polygraphy could effectively ensure patient care while optimizing resource utilization.

METHODS

A retrospective analysis was conducted on data from patients undergoing type 3 polygraphy for suspected OSA in a tertiary referral hospital between January 2022 and December 2022. Demographic, clinical, and management data were collected. The efficacy of HST in guiding management plans was evaluated, with outcomes categorized as effective or ineffective based on subsequent need for in-laboratory polysomnography.

RESULTS

While 85% of patients received a reliable diagnosis, 44.4% of them still required subsequent polysomnography, primarily due to adherence to funding regulations, rather than clinical need for further testing. Factors impacting the efficacy of HST included patient age, severity of apnea, and referral by a certified sleep specialist physician.

CONCLUSION

Our study highlighted the potential of type 3 polygraphy, as a valuable tool for diagnosing OSA in an outpatient setting. However, having the result interpreted by a certified sleep specialist doctor was not enough. To streamline the care pathway, the referral for polygraphy had also to be made by a trained specialist. Challenges related to funding regulations, patient demographics and physician training stress the need for optimized diagnostic pathways to improve patient care and resource utilization.

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

Feasibility of Type 3 Polygraphy for Evaluating Leak Determinants in CPAP-Treated OSA Patients: A Step Toward Personalized Leak Management

BACKGROUND

Unintentional leaks (ULs) are frequent adverse effects in CPAP-treated patients. We previously published a novel methodology for analyzing the determinants of UL using polysomnography. We now propose a simplified recording system using a type 3 polygraphic device (Somnolter; Nomics S.A.).

RESEARCH QUESTION

(1) To describe individual UL determinants provided by the Somnolter software in automatic-CPAP-treated OSA patients; (2) To subsequently describe the clinical consensus of four physicians on how to manage each individual UL situation.

STUDY DESIGN AND METHODS

Somnoler recordings performed under nasal automatic-CPAP were automatically analyzed with APIOS software. For each polygraphic recording, APIOS provided the OR and the CIs for potential determinants of UL: mouth opening, CPAP pressure, body position, and mandibular oscillation. Based on these results, each of four physicians was asked to choose one of four strategies: (1) increase or decrease therapeutic pressure; (2) change nasal mask for oro-nasal mask/chinstrap; (3) favor a nonsupine or supine position; (4) no action for individual leak management. Subsequently, a meeting was held to determine a consensus choice for each individual case.

RESULTS

Seventy-eight consecutive patients underwent home-polygraphy with Somnolter. Fifty recordings were analyzed (16 females; 65 [57-75] years of age; BMI = 31.1 [27.4-35.3]). Individual diagnosis of UL was routinely feasible. The determinants of UL were heterogeneous in the population, and diagnosis of UL was not feasible in 10 patients. Based on the results from this analysis, we established consensus leak management strategies at the individual level. The average Cohen κ coefficient for the four raters was 0.58. Pressure modification was proposed in 36% of patients, no action in 24%, installation of a facial mask/chinstrap in 22%, and positional treatment in 18%.

INTERPRETATION

The use of type 3 polygraphy for characterizing leak determinants in patients treated with nasal automatic-CPAP is feasible in routine practice. Leak determinants are patient specific. Interrater concordance for determining individual leak management strategies demonstrated a "fair" level of agreement.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT03381508; URL: www.clinicaltrials.gov).

Mandibular Movements As Accurate Reporters of Respiratory Effort during Sleep: Validation against Diaphragmatic Electromyography

Context

Mandibular movements (MM) are considered as reliable reporters of respiratory effort (RE) during sleep and sleep disordered breathing (SDB), but MM accuracy has never been validated against the gold standard diaphragmatic electromyography (EMG-d).

Objectives

To assess the degree of agreement between MM and EMG-d signals during different sleep stages and abnormal respiratory events.

Methods

Twenty-five consecutive adult patients with SDB were studied by polysomnography (PSG) that also included multipair esophageal diaphragm electromyography and a magnetometer to record MM. EMG-d activity (microvolt) and MM (millimeter) amplitudes were extracted by envelope processing. Agreement between signals amplitudes was evaluated by mixed linear regression and cross-correlation function and in segments of PSG including event-free and SDB periods.

Results

The average total sleep time was 370 ± 18 min and the apnea hypopnea index was 24.8 ± 5.2 events/h. MM and EMG-d amplitudes were significantly cross-correlated: median r (95% CI): 0.67 (0.23–0.96). A mixed linear model showed that for each 10 µV of increase in EMG-d activity, MM amplitude increased by 0.28 mm. The variations in MM amplitudes (median range: 0.11–0.84 mm) between normal breathing, respiratory effort-related arousal, obstructive, mixed, and central apnea periods closely corresponded to those observed with EMG-d activity (median range: 2.11–8.23 µV).

Conclusion

MM amplitudes change proportionally to diaphragmatic EMG activity and accurately identify variations of RE during normal sleep and SDB.

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

BACKGROUND AND OBJECTIVE

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.

METHODS

This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model.

RESULTS

Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%).

CONCLUSIONS

A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended.

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.

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.

Diagnosis and treatment for obstructive sleep apnea: Fundamental and clinical knowledge in obstructive sleep apnea

PURPOSE

This review article covers the diagnosis and treatment of obstructive sleep apnea (OSA) from a dental perspective. It addresses the issue of when and how to screen for and then, if indicated, refer the patient for a more comprehensive.

STUDY SELECTION

Our focus in this article was on identifying current unanswered questions that relevant to OSA problems that dental scientists have to pursue and on providing valuable information on that problems, consequently the previous studies which investigated or reviewed the diagnosis and treatment of OSA were included. In addition, we included studies on jaw movements during sleep and on the use of a lateral cephalometric film related to the diagnosis and treatment of OSA.

RESULTS

The role of portable sleep monitoring devices versus full laboratory polysomnography is discussed. This review also describes what is known about the efficacy of mandibular advancement devices and when and how they fit in to a treatment program for a patient with obstructive sleep apnea. Finally some basic research is presented on jaw movements during sleep and how a lateral cephalometric film can be used to assess the changes of the airway with body posture and head posture.

CONCLUSION

This article provides the valuable suggestions for the clinical questions in the diagnosis and treatment of OSA.

Mandibular movements identify respiratory effort in pediatric obstructive sleep apnea

STUDY OBJECTIVES

Obstructive sleep apnea-hypopnea (OAH) diagnosis in children is based on the quantification of flow and respiratory effort (RE). Pulse transit time (PTT) is one validated tool to recognize RE. Pattern analysis of mandibular movements (MM) might be an alternative method to detect RE. We compared several patterns of MM to concomittant changes in PTT during OAH in children with adenotonsillar hypertrophy.

METHODS

PARTICIPANTS

33 consecutive children with snoring and symptoms/signs of OAH.

MEASUREMENTS

MMs were measured during polysomnography with a magnetometer device (Brizzy Nomics, Liege, Belgium) placed on the chin and forehead. Patterns of MM were evaluated representing peak to peak fluctuations > 0.3 mm in mandibular excursion (MML), mandibular opening (MMO), and sharp MM (MMS), which closed the mouth on cortical arousal (CAr).

RESULTS

The median (95% CI) hourly rate of at least 1 MM (MML, or MMO, or MMS) was 18.1 (13.2-36.3) and strongly correlated with OAHI (p = 0.003) but not with central apnea-hypopnea index (CAHI; p = 0.292). The durations when the MM amplitude was > 0.4 mm and PTT > 15 ms were strongly correlated (p < 0.001). The mean (SD) of MM peak to peak amplitude was larger during OAH than CAH (0.9 ± 0.7 mm and 0.2 ± 0.3 mm; p < 0.001, respectively). MMS at the termination of OAH had larger amplitude compared to MMS with CAH (1.5 ± 0.9 mm and 0.5 ± 0.7 mm, respectively, p < 0.001).

CONCLUSIONS

MM > 0.4 mm occurred frequently during periods of OAH and were frequently terminated by MMS corresponding to mouth closure on CAr. The MM findings strongly correlated with changes in PTT. MM analysis could be a simple and accurate promising tool for RE characterization and optimization of OAH diagnosis in children.

Added value of a mandible movement automated analysis in the screening of obstructive sleep apnea

In-laboratory polysomnography is the 'gold standard' for diagnosing obstructive sleep apnea syndrome, but is time consuming and costly, with long waiting lists in many sleep laboratories. Therefore, the search for alternative methods to detect respiratory events is growing. In this prospective study, we compared attended polysomnography with two other methods, with or without mandible movement automated analysis provided by a distance-meter and added to airflow and oxygen saturation analysis for the detection of respiratory events. The mandible movement automated analysis allows for the detection of salient mandible movement, which is a surrogate for arousal. All parameters were recorded simultaneously in 570 consecutive patients (M/F: 381/189; age: 50±14 years; body mass index: 29±7 kg m(-2) ) visiting a sleep laboratory. The most frequent main diagnoses were: obstructive sleep apnea (344; 60%); insomnia/anxiety/depression (75; 13%); and upper airway resistance syndrome (25; 4%). The correlation between polysomnography and the method with mandible movement automated analysis was excellent (r: 0.95; P<0.001). Accuracy characteristics of the methods showed a statistical improvement in sensitivity and negative predictive value with the addition of mandible movement automated analysis. This was true for different diagnostic thresholds of obstructive sleep severity, with an excellent efficiency for moderate to severe index (apnea-hypopnea index ≥15h(-1) ). A Bland & Altman plot corroborated the analysis. The addition of mandible movement automated analysis significantly improves the respiratory index calculation accuracy compared with an airflow and oxygen saturation analysis. This is an attractive method for the screening of obstructive sleep apnea syndrome, increasing the ability to detect hypopnea thanks to the salient mandible movement as a marker of arousals.

Obstructive sleep apnoea syndrome: comparison between polysomnography and portable sleep monitoring based on jaw recordings

INTRODUCTION

Obstructive sleep apnoea syndrome (OSAS) constitutes a new major public health problem because of its several pathophysiologic consequences such as cognitive disorders, excessive daytime sleepiness with risks of traffic accidents, cardiovascular implications, and decrease of quality of life. The necessity of a gold-standard polysomnography to ensure an accurate diagnosis implies an expensive, technical and time-consuming examination. Thus, it seems logical to develop new systems so as to diagnose SAS and to make it possible to detect apnoeas/hypopnoeas easily during sleep even at home.

AIM OF THE STUDY

To assess a novel type-3 portable monitoring (PM) device, the Somnolter, and dedicated automatic analysis of several signals, one of which is the mandibular movement signal.

METHOD

We studied patients suffering from OSAS. For all the patients, a nocturnal diagnosis polysomnography (PSG) was recorded in hospital settings, based on six EEG channels, two EOG channels, chin EMG channel, EKG, and respiratory parameters. At the same time, the Somnolter PM device recorded the physiological parameters from its own nasal prongs, thoracic belt, pulse oxymeter, body position, and jaw movement sensors. A visual analysis of PSG recordings was made leading to the detection of apnoea/hypopnoea index (AHI-PSG) and an automatic analysis of the Somnolter traces was performed to get automatic apnoea/hypopnoea index (AHI-A). The added value of the mandible movement signals was the particular jaw movements related to arousals, to respiratory efforts and to sleep/wake state. A comparison was made between the automatic and gold AHIs standard and the correlation was calculated between them.

RESULTS

Ninety patients, aged between 47 and 70 years (mean age: 55.4±8.7) took part in the study. The linear regression and the correlation coefficient between AHI-PSG and AHI-A showed the good reliability of the automatic method. The Bland Altman analysis shows a correlation of 0.95 with a sensitivity of 83.6 and specificity of 81.8.

CONCLUSION

The dedicated automatic analysis based on mandibular movements presents a good potential for the diagnosis of OSAS. The AHI computed by the automatic method is correlated with the AHI-PSG and the Somnolter could easily be used both in hospital, and in ambulatory settings.