Introduction Obstructive sleep apnoea (OSA) is underdiagnosed, necessitating an expansion of clinical testing.1 The novel Sunrise™ monitor (Sunrise, Belgium) provides an automated approach for diagnosing OSA, utilising mandibular movement and machine learning to estimate the apnoea-hypopnea index (AHI). AHI varies across consecutive nights of sleep analysis.2 This study aimed to evaluate the night-to-night variability in the estimated AHI as detected by the Sunrise™ monitor in healthy adults.
Methods Nineteen healthy volunteers (mean ± SD 38.1 ± 18.2 years), who reported snoring but had no diagnosed sleep disorders were invited to participate. Each participant underwent a home sleep study, wearing both the Sunrise monitor™ and a respiratory polygraphy device (Apnoealink, Resmed, Australia) simultaneously, on two consecutive nights. The Sunrise™ monitor provided an automated estimated AHI; the polygraphy data was automatically analysed using AASM 2012 scoring criteria, and then manually reviewed.3 The change in AHI across the two nights was evaluated using the Wilcoxon signed-rank test, and the agreement in the AHI across the two nights was evaluated using the intraclass correlation coefficient (ICC).
The study was approved by the Imperial College London’s ethics committee, which granted the supervisors the ability to review after submitting the Research Governance and Integrity Team (RGIT) ethics checklist.
Results The change in AHI for both the Sunrise™ monitor and respiratory polygraphy differed from night-to-night by -0.6 events/hour and -1.1 events/hour respectively (table 1). These differences were not statistically significant. The Sunrise™ monitor exhibited a higher level of agreement in AHI measurements from night-to-night compared to respiratory polygraphy; ICC 0.77 (0.50 to 0.91) vs 0.59 (0.21 to 0.82).
Discussion Both devices displayed a variability in the AHI between the two nights, the variability was less for the Sunrise™ monitor compared to Respiratory Polygraphy. Further investigation is necessary to assess its impact on OSA diagnosis and severity.
Simpson L, Hillman DR, Cooper MN, Ward KL, Hunter M, Cullen S, et al. High prevalence of undiagnosed obstructive sleep apnoea in the general population and methods for screening for representative controls. Sleep & Breathing = Schlaf & Atmung. 2013;17(3):967–973. Available from: doi: 10.1007/s11325-012-0785-0
Roeder M, Bradicich M, Schwarz EI, Thiel S, Gaisl T, Held U, et al. Night-to-night variability of respiratory events in obstructive sleep apnoea: a systematic review and meta-analysis. Thorax. 2020;75(12):1095–1102. Available from: doi: 10.1136/thoraxjnl-2020-2145441
Kapur Vishesh K, Auckley Dennis H, Susmita C, Kuhlmann David C, Reena M, Kannan R, et al. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline. Journal of Clinical Sleep Medicine. 13(03):479–504. Available from: doi: 10.5664/jcsm.6506
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.