Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea

Peripheral arterial tonometry versus polysomnography in suspected obstructive sleep apnoea

BACKGROUND

Polysomnography (PSG) is the gold standard for the diagnosis of obstructive sleep apnoea (OSA). Home sleep apnoea testing with peripheral arterial tonometry (PAT) is a recommended diagnostic alternative for patients with an increased risk for OSA. In a large clinical cohort, we investigated concordance and predictors for discordance in diagnosing OSA using PAT and PSG, and three-year cardiovascular risk in patients with discordant OSA diagnosis.

METHODS

Retrospective monocentric cohort study. Patients with a PAT AHI ≥ 5/h followed by an in-hospital PSG within three months were included. All patients with a PAT AHI ≥ 5/h but a PSG AHI < 5/h were classified as discordant. Patients with PAT and PSG AHI ≥ 5/h were classified as concordant. To ascertain cardiovascular risk, major adverse cardiovascular events (MACE) were analyzed in discordant patients and sex, age, body mass index (BMI) and cardiovascular disease-matched concordant patients over a follow-up time of 3.1 ± 0.06 years.

RESULTS

A total of 940 patients, 66% male with an average age of 55 ± 0.4 years and BMI of 31 ± 0.2 kg/m² were included. Agreement in OSA diagnosis was observed in 80% of patients (55% in mild and 86% in moderate and severe OSA). Factors significantly associated with a discordant diagnosis were female sex, younger age and lower BMI, but not comorbidities. There was no significant difference in MACE (p = 0.920) between discordant patients (n = 155) and matched concordant patients (n = 274) with or without therapy.

CONCLUSIONS

Concordance between PAT and PSG diagnosis of sleep apnoea is good, particularly in moderate and severe OSA. Predictors for discordant results between PAT and PSG were age, sex and BMI. MACE risk is similar in those with OSA diagnosed by PAT or PSG.

Keep it simple: A novel technique for measuring airflow using a wireless patch

OBJECTIVES

Despite the growing use of home sleep testing for obstructive sleep apnea (OSA), there are significant barriers that make it difficult for patients. This study aimed to evaluate a new set of wireless patches for the detection of OSA as compared to polysomnography (PSG) respiratory signals. Safety was also evaluated.

METHODS

Patients (n = 25; mean age = 51.3; SD = 15.6) undergoing standard PSG evaluation for OSA wore 2 device patches applied on the chest and abdomen to provide surrogates of respiratory effort and derived airflow. Two sets of respiratory data (with common SpO2 and heart rate as a reference from PSG) were created, one from PSG and one from the device patches. Data were scored by a Registered Polysomnographic Technologist. Linear regression analyses and Bland-Altman plots were used to compare the two derived apnea-hypopnea indices (AHI). Bench testing was performed to determine whether the device patch respiratory signal accuracy remained consistent for different body characteristics.

RESULTS

There was a significant correlation for AHI between the device patch and PSG (r = 0.979; P < .001; 95% CI 0.952, 1.00). There were also significant correlations between PSG and the device patch for supine AHI (r = 0.866, P < .001, 95% CI 0.610, 0.965) and nonsupine AHI (r = 0.983; <0.001, 95% CI 0.954, 1.00). No differences in respiratory signal accuracy were found with bench testing comparison groups.

CONCLUSIONS

These preliminary results suggest that the device patch may be an effective way to measure respiratory dynamics, including derived airflow, to evaluate OSA. Results show promise as a new innovative method for home sleep testing.

Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea

Background

Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, requires specialists to utilize, expensive, and has accessibility challenges. There are also challenges with awareness and identification of obstructive sleep apnea in the primary care setting. Artificial intelligence systems offer the opportunity for a new diagnostic approach that addresses the limitations of polysomnography and ultimately benefits patients by streamlining the diagnostic expedition.

Main body

The purpose of this project is to elucidate the barriers that exist in the implementation of artificial intelligence systems into the diagnostic context of obstructive sleep apnea. It is essential to understand these challenges in order to proactively create solutions and establish an efficient adoption of this new technology. The literature regarding the evolution of the diagnosis of obstructive sleep apnea, the role of artificial intelligence in the diagnosis, and the barriers in artificial intelligence implementation was reviewed and analyzed.

Conclusion

The barriers identified were categorized into different themes including technology, data, regulation, human resources, education, and culture. Many of these challenges are ubiquitous across artificial intelligence implementation in any medical diagnostic setting. Future research directions include developing solutions to the barriers presented in this project.

Graphical abstract

Multiple-access versus telemedicine home-based sleep apnea testing for obstructive sleep apnea (OSA) diagnosis: a cost-minimization study

Purpose

The aim of the present study was to compare two clinical pathways: the multiple-access outpatient pathway versus the telemedicine pathway.

Methods

The multiple-access outpatient pathway and the telemedicine pathway were both performed with WatchPAT and implemented in a real-life healthcare scenario, adopting a cost-minimization approach. A cost-minimization analysis was undertaken to assess the economic impact of the two alternatives. The cost analyses were performed in euros for the year 2021 adopting the patient, the hospital, and the societal perspectives. Given the chosen perspectives, direct medical costs, direct nonmedical costs, and indirect costs were considered. In addition, a univariate sensitivity analysis was conducted.

Results

From a hospital perspective, the telemedicine approach was estimated to cost €49 more than the multiple-access alternative. Considering the patient perspective, the telemedicine approach was estimated to cost €167 less than the multiple-access pathway. Considering the societal perspective, the telemedicine approach is estimated to cost €119 less than the multiple-access pathway.

Conclusion

The adoption of telemedicine home sleep apnea testing could improve the efficiency of the healthcare processes if considering the direct and indirect costs incurred by patients and not only by healthcare providers.

Sleep Apnoea and AF: Where Do We Stand? Practical Advice for Clinicians

AF is the most common sustained arrhythmia encountered in clinical practice. Among the largest contributing factors to the rapid increase in the incidence of AF are aging and obesity within the global population. Obstructive sleep apnoea (OSA) is a risk factor for AF that is clearly linked to obesity. Guidelines have advocated interrogation for clinical signs of OSA in all AF patients. The aim of this article is to provide practical advice for clinicians seeking to manage patients with AF and OSA. The authors discuss questionnaires to screen for OSA, various types of tests available for the diagnosis of OSA and data to assess the impact of treatment of OSA after various treatment options in AF patients. Finally, they outline the many areas that warrant further investigation in this patient population.

Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5-15; 19% had AHI 15-30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703-0.888], 0.931 [95% CI, 0.772-0.992], and 0.735 [95% CI, 0.589-0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828-0.987], 0.944 [95% CI, 0.727-0.999], and 0.933 [95% CI, 0.779-0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1.