The utility of home sleep apnea tests in patients with low versus high pre-test probability for moderate to severe OSA

FDA-cleared home sleep apnea testing devices

The demand for home sleep apnea testing (HSAT) devices is escalating, particularly in the context of the coronavirus 2019 (COVID-19) pandemic. The absence of standardized development and verification procedures poses a significant challenge. This study meticulously analyzed the approval process characteristics of HSAT devices by the U.S. Food and Drug Administration (FDA) from September 1, 2003, to September 1, 2023, with a primary focus on ensuring safety and clinical effectiveness. We examined 58 reports out of 1046 that underwent FDA clearance via the 510(k) and de novo pathways. A substantial surge in certifications after the 2022 pandemic was observed. Type-3 devices dominated, signifying a growing trend for both home and clinical use. Key measurement items included respiration and sleep analysis, with the apnea-hypopnea index (AHI) and sleep stage emerging as pivotal indicators. The majority of FDA-cleared HSAT devices adhered to electrical safety and biocompatibility standards. Critical considerations encompass performance and function testing, usability, and cybersecurity. This study emphasized the nearly indispensable role of clinical trials in ensuring the clinical effectiveness of HSAT devices. Future studies should propose guidances that specify stringent requirements, robust clinical trial designs, and comprehensive performance criteria to guarantee the minimum safety and clinical effectiveness of HSATs.

Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index

Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets (n = 1561). Moreover, TAT-based AHI (AHITAT) and TST-based REI (REITST) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHITAT, and REITST were significantly lower than AHI (p < 0.0001, p ≤ 0.002, and p ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHITAT, the accuracies were 68.4% and 85.9%, and based on REITST, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REITST (r = 0.98 and r = 0.99 for the datasets) and least with REI (r = 0.92 and r = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REITST the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity.

Out of the Laboratory and into the Home: Home Testing for Sleep Apnea and Other Sleep Disorders

SUMMARY

Home sleep testing has emerged over the past decade and become an intriguing option for patients and providers because it can be conducted in the home environment. However, appropriate application of this technology is key to ensure accurate and validated results to provide appropriate patient care. In this review, we will cover the current guidelines for the use of home sleep apnea tests, the types of testing available, and future directions of home testing.

Sex-specific differences in diagnostic approaches of inpatient sleep testing for obstructive sleep apnea

BACKGROUND

Few studies have investigated sex-specific disparities in inpatient sleep testing. We postulate that women are more likely to have a milder degree of obstructive sleep apnea (OSA) and lower extent of hypoxia on Type III sleep studies versus polysomnography (PSG).

PATIENTS AND METHODS

The Cleveland Clinic Sleep laboratory registry was leveraged to identify all adult inpatient sleep studies performed for OSA. Demographics, comorbidities, and sleep study measures were collected and compared by sex and sleep study type. Logistic regression was used to examine sleep study type predictive of OSA (apnea hypopnea index [AHI; ≥5, ≥15 and ≥ 30]) and hypoxia, (median percentage of sleep time spent at <90% SaO2 [TST<90%,≥ 11%,] adjusted for covariates.

RESULTS

The sample 778 patients had a mean age of 56.1 ± 16.1 years; 44.5% were female and 72.2% Caucasian. At an AHI≥5, women showed an increase odds of OSA (adjusted, OR = 2.04,95%; CI:1.24-3.35, p = 0.005) with Type III sleep study vs PSG compared to men. At an AHI≥15, men had less odds of OSA (adjusted OR = 0.60,95%CI:0.39-0.90,p = 0.015) with Type III sleep study vs PSG compared to women (OR = 1.15,95%CI:0.72-1.85,p = 0.56), with an interaction p-value of 0.040. These results were attenuated when the analysis was restricted using the 3% hypopnea scoring rule. Men and women had higher odds of TST <90 ≥ 11% (OR:2.60,95%CI:1.60-4.21,p=<0.001; OR:3.46,95%CI:1.97-6.05,p < 0.001) with Type III sleep study versus PSG, albeit no sex-interaction was observed.

CONCLUSIONS

These results suggest that sex-specific differences in diagnostic performance of sleep testing type in the inpatient setting should be considered according to level of OSA severity, which are influenced by hypopnea-related desaturation extent.

ERS technical standards for using type III devices (limited channel studies) in the diagnosis of sleep disordered breathing in adults and children

For more than three decades, type III devices have been used in the diagnosis of sleep disordered breathing in supervised as well as unsupervised settings. They have satisfactory positive and negative predictive values for detecting obstructive and central sleep apnoea in populations with moderately high pre-test probability of symptoms associated with these events. However, standardisation of commercially available type III devices has never been undertaken and the technical specifications can vary widely. None have been subjected to the same rigorous processes as most other diagnostic modalities in the medical field. Although type III devices do not include acquisition of electroencephalographic signals overnight, the minimum number of physical sensors required to allow for respiratory event scoring using standards outlined by the American Academy of Sleep Medicine remains debatable. This technical standard summarises data on type III studies published since 2007 from multiple perspectives in both adult and paediatric sleep practice. Most importantly, it aims to provide a framework for considering current type III device limitations in the diagnosis of sleep disordered breathing while raising research- and practice-related questions aimed at improving our use of these devices in the present and future.

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.

The Future of Sleep Measurements: A Review and Perspective

This article provides an overview of the current use, limitations, and future directions of the variety of subjective and objective sleep assessments available. This article argues for various ways and sources of collecting, combining, and using data to enlighten clinical practice and the sleep research of the future. It highlights the prospects of digital management platforms to store and present the data, and the importance of codesign when developing such platforms and other new instruments. It also discusses the abundance of opportunities that data science and machine learning open for the analysis of data.

Predictors of moderate to severe obstructive sleep apnea: identification of sex differences

PURPOSE

Home sleep apnea tests are recommended only for patients at high risk of moderate to severe obstructive sleep apnea (OSA, apnea-hypopnea index [AHI] ≥ 15/h). We evaluated 14 factors known to be associated with OSA and identified sex differences in predictors of moderate to severe OSA.

METHODS

Retrospective analysis was done on 545 subjects who completed sleep questionnaires and underwent diagnostic polysomnogram at a tertiary sleep center. Univariate and multivariate analysis was conducted separately in males and females to determine which variables were independent predictors of moderate to severe OSA.

RESULTS

Overall, physical traits were stronger predictors in both males and females. For each sex, only 3 variables were found to be independently predictive of moderate to severe OSA. In order of predictive strength, this included body mass index (BMI) ≥ 38 kg/m² (aOR 5.80, p < 0.001), neck circumference (NC) ≥ 17 in. (aOR 2.52, p = 0.002), and Epworth sleepiness scale (ESS) ≥ 13 (aOR 2.22, p = 0.015) for males and age ≥ 50 years (aOR 4.19, p < 0.001), NC ≥ 14.5 in. (aOR 3.13, p = 0.003), and report of morning headaches (aOR 2.00, p = 0.039) for females. Applying the Bonferroni correction, BMI and NC remained significant for males, and age and NC remained significant for females.

CONCLUSIONS

In a subject population referred for sleep evaluation at a tertiary care center only a few variables are independently predictive of moderate to severe OSA, and these variables differed between males and females. Only BMI, NC, and a high ESS were independently predictive of moderate to severe OSA in males, whereas age, NC, and morning headaches were independently predictive in females.