Phenotyping REM OSA by means of peripheral arterial tone-based home sleep apnea testing and polysomnography: A critical assessment of the sensitivity and specificity of both methods

OSA diagnosis goes wearable: are the latest devices ready to shine?

STUDY OBJECTIVES

From 2019-2023, the United States Food and Drug Administration has cleared 9 novel obstructive sleep apnea-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature.

METHODS

We collected information from PubMed, United States Food and Drug Administration clearance documents, ClinicalTrials.gov, and web sources, with direct industry input whenever feasible.

RESULTS

In this "device-centered" review, we broadly categorized these wearables into 2 main groups: those that primarily harness photoplethysmography data and those that do not. The former include the peripheral arterial tonometry-based devices. The latter was further broken down into 2 key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized.

CONCLUSIONS

In the foreseeable future, these novel obstructive sleep apnea-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe obstructive sleep apnea without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies.

CITATION

Chiang AA, Jerkins E, Holfinger S, et al. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med. 2024;20(11):1823-1838.

Autonomic arousal detection and cardio-respiratory sleep staging improve the accuracy of home sleep apnea tests

Introduction: The apnea-hypopnea index (AHI), defined as the number of apneas and hypopneas per hour of sleep, is still used as an important index to assess sleep disordered breathing (SDB) severity, where hypopneas are confirmed by the presence of an oxygen desaturation or an arousal. Ambulatory polygraphy without neurological signals, often referred to as home sleep apnea testing (HSAT), can potentially underestimate the severity of sleep disordered breathing (SDB) as sleep and arousals are not assessed. We aim to improve the diagnostic accuracy of HSATs by extracting surrogate sleep and arousal information derived from autonomic nervous system activity with artificial intelligence. Methods: We used polysomnographic (PSG) recordings from 245 subjects (148 with simultaneously recorded HSATs) to develop and validate a new algorithm to detect autonomic arousals using artificial intelligence. A clinically validated auto-scoring algorithm (Somnolyzer) scored respiratory events, cortical arousals, and sleep stages in PSGs, and provided respiratory events and sleep stages from cardio-respiratory signals in HSATs. In a four-fold cross validation of the newly developed algorithm, we evaluated the accuracy of the estimated arousal index and HSAT-derived surrogates for the AHI. Results: The agreement between the autonomic and cortical arousal index was moderate to good with an intraclass correlation coefficient of 0.73. When using thresholds of 5, 15, and 30 to categorize SDB into none, mild, moderate, and severe, the addition of sleep and arousal information significantly improved the classification accuracy from 70.2% (Cohen's κ = 0.58) to 80.4% (κ = 0.72), with a significant reduction of patients where the severity category was underestimated from 18.8% to 7.3%. Discussion: Extracting sleep and arousal information from autonomic nervous system activity can improve the diagnostic accuracy of HSATs by significantly reducing the probability of underestimating SDB severity without compromising specificity.

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.