Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Review and perspective on sleep-disordered breathing research and translation to clinics

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

Application study of apnea-hypopnea duration for assessing adult obstructive sleep apnea

BACKGROUND

Obstructive sleep apnea (OSA) is a common sleep disordered breathing disorder, which can cause serious damage to multiple human systems. Although polysomnography (PSG) is the current gold standard for diagnosis, it is complex and expensive. Therefore, it is of great significance to find a simple, economical and rapid primary screening and diagnosis method to replace PSG for the diagnosis of OSA.

OBJECTIVE

The purpose of this study is to propose a new method for the diagnosis and classification of OSA, which is used to automatically detect the duration of sleep apnea hypopnea events (AHE), so as to estimate the ratio(S) of the total duration of all-night AHE to the total sleep time only based on the sound signal of sleep respiration, and to identify OSA.

METHODS

We performed PSG tests on participants and extracted relevant sleep breathing sound signal data. This study is carried out in two stages. In the first stage, the relevant PSG report data of eligible subjects were recorded, the total duration of AHE in each subject's data was extracted, and the S value was calculated to evaluate the severity of OSA. In the second stage, only the sleep breath sound signal data of the same batch of subjects were used for automatic detection, and the S value in the sleep breath sound signal was extracted, and the S value was compared with the PSG diagnosis results to calculate the accuracy of the experimental method.

RESULTS

Among 225 subjects. Using PSG as the reference standard, the S value extracted from the PSG diagnostic data report can accurately diagnose OSA(accuracy rate 99.56%) and distinguish its severity (accuracy rate 95.11%). The accuracy of the S value detected in the sleep breathing sound signal in the diagnosis of severe OSA reached 100%.

CONCLUSION

The results show that the experimental parameter S value is feasible in OSA diagnosis and classification. OSA can be identified and evaluated only by sleep breathing sounds. This method helps to simplify the diagnostic grading of traditional OSA and lays a foundation for the subsequent development of simple diagnostic grading equipment.

Poor sleep is associated with deficits of attention in COPD patients

BACKGROUND

Poor sleep and attention deficits are common in COPD.

OBJECTIVES

To assess the relationship between self-reported poor sleep and attention deficits in COPD. We also studied the association between self-reported sleep and the attention tests with the objective characteristics of sleep.

METHODS

Fifty-nine COPD patients were prospectively studied. Self-reported sleep quality was assessed using the Pittsburgh sleep quality index (PSQI). Objective characteristics of sleep were assessed by actigraphy and polysomnography. Attention was evaluated with the Oxford sleep resistance test (OSLER) and the Psychomotor vigilance test (PVT).

RESULTS

28 (47 %) patients referred poor sleep (PSQI >5). In the OSLER test they showed earlier sleep onset than patients with good sleep, median (Interquartil range): 31.2 min (25.4-40) vs 40 min (28.5-40), p: 0.048. They also spent more time making errors: 4.5 % (0.6-7.6) of total test time vs 0.7 % (0.2-5.3), p: 0.048. In PVT, patients with poor sleep presented a greater dispersion of the reaction time values with a higher value in the slowest 10 % of the reactions, 828 (609-1667) msec. vs 708 (601-993) msec, p: 0.028. No association was found between self-reported poor sleep and objective sleep variables. We found no correlation between OSLER and PVT results and polysomnographic variables except between sleep efficiency and PVT response speed (β: 0.309, p: 0.018).

CONCLUSION

Self-reported poor sleep in COPD is associated with attention deficits. Sleep quality should be included in future studies of this facet of cognition in COPD, as well as to assess its potential usefulness as a therapeutic target.

The role of artificial intelligence in the treatment of obstructive sleep apnea

BACKGROUND

The first-line and most common treatment for obstructive sleep apnea is nasal continuous positive airway pressure, which serves as a pneumatic splint to stabilize the upper airway and is effective when used with appropriate adherence. Continuous positive airway pressure compliance rates remain significantly low despite machine improvements and compliance intervention. Other treatment options include oral appliances, myofunctional therapy, and surgery. The aim of this project is to elucidate the role of artificial intelligence within improving the treatment of obstructive sleep apnea.

METHODS

Related publications between 1999 and 2022 were reviewed from PubMed and Embase databases utilizing search terms "artificial intelligence," "machine learning," "obstructive sleep apnea," and "treatment." Both authors independently screened the results by title/abstract then by full text review. 126 non-duplicate articles were screened, 38 articles were included after title and abstract screen and 30 articles were included after full text review. The inclusion criteria are outline in the PICO framework and involved studies focused on artificial intelligence application in guiding and evaluating obstructive sleep apnea treatment. Non-English articles were excluded.

RESULTS

The role of artificial intelligence in the treatment of OSA was categorized into the following sections: Predicting treatment outcomes of various treatment options, Improving/Evaluating treatment, and Personalizing treatment with improving understanding of underlying mechanisms of OSA.

CONCLUSIONS

Artificial intelligence has the capacity to improve the treatment of OSA through predicting outcomes of treatment options, evaluating the treatment the patient is currently utilizing and increasing understanding of the mechanisms that contribute to OSA disease process and physiology. Implementing AI in guiding treatment decisions allows patients to connect with treatment methods that would be most effective on an individual basis.

Role of precision medicine in obstructive sleep apnoea

Obstructive sleep apnoea is a substantial clinical and public health problem because it contributes to harmful effects on quality of life, daytime symptoms, road traffic incidents, and cardiometabolic disease. Increasingly, obstructive sleep apnoea is recognised as a heterogeneous disease, and patients have varied susceptibility to long term complications and different responses to treatment. This narrative review summarises the current knowledge of precision medicine in obstructive sleep apnoea, particularly the role of symptom clusters, polysomnogram phenotypes, physiological endotypes, and circulating biomarkers in defining subtypes. In the near future, the prognostic accuracy of these measures in predicting long term complications in obstructive sleep apnoea will likely be improved, together with better matching of treatments to disease subtypes.

Comparative associations of oximetry patterns in Obstructive Sleep Apnea with incident cardiovascular disease

STUDY OBJECTIVES

Intermittent hypoxia is a key mechanism linking Obstructive Sleep Apnea (OSA) to cardiovascular disease (CVD). Oximetry analysis could enhance understanding of which OSA phenotypes are associated with CVD risk. The aim of this study was to compare associations of different oximetry patterns with incident CVD in men and women with OSA.

METHODS

Sleep Heart Health Study data were used for analysis. n = 2878 Participants (51.8% female; mean age 63.5 ± 10.5 years) with OSA (Apnea Hypopnea Index [AHI] ≥ 5 events/h) and no pre-existing CVD at baseline or within the first 2 years of follow-up were included. Four oximetry analysis approaches were applied: desaturation characteristics, time series analysis, power spectral density, and non-linear analysis. Thirty-one resulting oximetry patterns were compared to incident CVD using proportional hazards regression models adjusted for age, race, smoking, BMI, and sex.

RESULTS

There were no associations between OSA oximetry patterns and incident CVD in the total sample or in men. In women, there were some associations between incident CVD and time series analysis (e.g. SpO2 distribution standard deviation, HR 0.81, 95% CI 0.68-0.96, p = 0.014) and power spectral density oximetry patterns (e.g. Full frequency band mean HR 0.75; 95% CI 0.59-0.95; p = 0.015).

CONCLUSIONS

Comprehensive comparison of baseline oximetry patterns in OSA found none were related to development of CVD. There were no standout individual oximetry patterns that appear to be candidates for CVD risk phenotyping in OSA, but some showed marginal relationships with CVD risk in women. Further work is required to understand whether OSA phenotypes can be used to predict susceptibility to cardiovascular disease.

Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network

PURPOSE

This study evaluated a novel approach for diagnosis and classification of obstructive sleep apnea (OSA), called Obstructive Sleep Apnea Smart System (OSASS), using residual networks and single-channel nasal pressure airflow signals.

METHODS

Data were collected from the sleep center of the First Affiliated Hospital, Sun Yat-sen University, and the Integrative Department of Guangdong Province Traditional Chinese Medical Hospital. We developed a new model called the multi-resolution residual network (Mr-ResNet) based on a residual network to detect nasal pressure airflow signals recorded by polysomnography (PSG) automatically. The performance of the model was assessed by its sensitivity, specificity, accuracy, and F1-score. We built OSASS based on Mr-ResNet to estimate the apnea‒hypopnea index (AHI) and to classify the severity of OSA, and compared the agreement between OSASS output and the registered polysomnographic technologist (RPSGT) score, assessed by two technologists.

RESULTS

In the primary test set, the sensitivity, specificity, accuracy, and F1-score of Mr-ResNet were 90.8%, 90.5%, 91.2%, and 90.5%, respectively. In the independent test set, the Spearman correlation for AHI between OSASS and the RPSGT score determined by two technologists was 0.94 (p < 0.001) and 0.96 (p < 0.001), respectively. Cohen's Kappa scores for classification between OSASS and the two technologists' scores were 0.81 and 0.84, respectively.

CONCLUSION

Our results indicated that OSASS can automatically diagnose and classify OSA using signals from a single-channel nasal pressure airflow, which is consistent with polysomnographic technologists' findings. Thus, OSASS holds promise for clinical application.

The effects of mandibular advancement appliance therapy on jaw-closing muscle activity during sleep in patients with obstructive sleep apnea: a 3-6 months follow-up

STUDY OBJECTIVES

The main aim of this study was to investigate the effects of mandibular advancement appliance (MAA) therapy on jaw-closing muscle activity (JCMA) time-related to respiratory arousals, and on JCMA time-related to nonrespiratory arousals in patients with obstructive sleep apnea.

METHODS

Eighteen patients with OSA (mean ± standard deviation = 49.4 ± 9.8 years) with a mean ± standard deviation apnea-hypopnea index of 22.0 ± 16.0 events/h of sleep participated in a randomized controlled crossover trial in which two ambulatory polysomnographic recordings, 1 with an MAA in situ and another without the MAA in situ, were performed. JCMA was quantified as the sum of rhythmic masticatory muscle activities and other orofacial activities.

RESULTS

Significant reductions in the apnea-hypopnea index (Z = -2.984; P = .003), in the respiratory arousal index (Z = -2.896; P = .004), and in the JCMA time-related to respiratory arousal index (Z = -3.434; P = .001) were found with MAA in situ. On the nonrespiratory arousal index, and on the JCMA time-related to nonrespiratory arousal index, MAA had no significant effect (T = 2.23; P = .82; and Z = -0.66; P = .51, respectively).

CONCLUSIONS

This study shows that effective mandibular advancement appliance therapy significantly reduces jaw-closing muscle activities time-related to respiratory arousals in OSA patients. Future studies are needed to confirm these findings in obstructive sleep apnea patients with comorbid sleep bruxism.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: The Effects of Oral Appliance Therapy on Masseter Muscle Activity in Obstructive Sleep Apnea; Identifier: NCT02011425.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

The future of sleep-disordered breathing: A public health crisis

Find the whole series here https://onlinelibrary.wiley.com/doi/toc/10.1111/(ISSN)1440-1843.new-frontiers-in-sleep-disordered-breathing See cover image.

A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a common and heterogeneous disease characterized by episodic collapse within the upper airways, which leads to reduced ventilation and adverse consequences, including hypoxia, hypercapnia, sleep fragmentation, and long-term effects such as cardiovascular comorbidities. The clinical diagnosis of OSA and its severity classification are often determined based on the apnea-hypopnea index (AHI), defining the number of apneic and hypopnea events per hour of sleep. However, the limitations of the AHI to assess disease severity have necessitated the exploration of other metrics for additional information to reflect the complexity of OSA. Novel parameters such as the hypoxic burden have the potential to better capture the main features of OSA by maximizing the information available from the polysomnogram. These emerging measures have described multidimensional qualities of sleep-disordered breathing events and breathing irregularity and will ultimately result in better management of OSA.

Update on oral appliance therapy

Oral appliances are increasingly recommended for selected patients with obstructive sleep apnoea (OSA) and those who do not tolerate nor prefer continuous positive airway pressure. The most commonly used oral appliance advances the lower jaw during sleep, the so-called mandibular advancement device (MAD). Patients seek treatment because of disturbing snoring, daytime symptoms, apnoeas that disturb sleep and the longer term consequences with regard to cardiovascular risks. MADs reduce the apnoea-hypopnoea index, although to various degrees among patients. Effects on daytime sleepiness have been observed mainly among the more severe OSA patients. Blood pressure may be reduced in MAD-treated OSA patients. There is, however, uncertainty about which patients will respond to this therapy in terms of apnoea reductions, decreased sleepiness and other symptoms, and reduced risk for future impaired health. The occurrence of side-effects also remains difficult to predict at present. The majority of sleep apnoea patients suffer from various comorbidities in terms of cardiovascular diseases, type 2 diabetes and depression. The most recent findings indicate that phenotyping of patients, considering various aspects of this multifaceted disease, will shed more light on the indications for MADs in patients with nightly sleep breathing disturbances. This review summarises the most recent knowledge about MAD treatment.