<p>The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study</p>

Abstract

Background: Autonomic arousals can be considered as surrogates of electroencephalography (EEG) arousals when calculating respiratory disturbance index (RDI). The main objective of this proof of concept study was to evaluate the use of heart rate acceleration (HRa) arousals associated with sleep respiratory events in a population undergoing full polysomnography (type 1) and in another undergoing portable monitor study (type 3). Our hypothesis is that when compared to other commonly used indexes, RDI based on HRa will capture more events in both types of recording. Materials and Methods: A retrospective analysis was performed in two different populations of patients with suspected OSA: a) 72 patients undergoing one night of type 1 recording and b) 79 patients undergoing one night of type 3 recording. Variables for type 1 were 4% oxygen desaturation index (ODI), apnea/hypopnea index (AHI), RDI based on EEG arousals (RDIe), and RDI based on HRa with threshold of 5bpm (RDIa5). For type 3, variables were 4% ODI, AHI, and RDIa5 (it is not possible to calculate RDIe due to the absence of EEG). Calculated data were 1) Mean values for each sleep disturbance index in type 1 and 3 recordings; 2) Frequency of migration from lower to higher OSA severity categories using RDIa5 in comparison to AHI (thresholds: ≥5/h mild, ≥15/h moderate, ≥30/h severe); and 3) Bland-Altman plots to assess agreement between AHI vs RDIe and RDIa5 in type 1 population, and AHI vs RDIa5 in type 3 populations. Results: More respiratory disturbance events were captured with RDIa5 index in both type 1 and type 3 recordings when compared to the other indexes. In type 1 recording, when using RDIa5 37% of patients classified as not having OSA with AHI were now identified as having OSA, and a total of 59% migrated to higher severity categories. In type 3 recording, similar results were obtained, as 37% of patients classified as not having OSA with AHI were now identified as having OSA using RDIa5, and a total of 55% patients migrated to higher severity categories. Mean differences for RDIa5 and AHI in type 1 and 3 populations were similar. Conclusion: The use of autonomic arousals such as HRa can help to detect more respiratory disturbance events when compared to other indexes, being a variable that may help to capture borderline mild cases. This becomes especially relevant in type 3 recordings. Future research is needed to determine its validity, optimization, and its clinical significance.

Diagnosing sleep-disordered breathing in neuromuscular diseases in 2021Response to Hunasikatti M. Home portable monitoring for the diagnosis of sleep-disordered breathing in adolescents and adults with neuromuscular disorders: not yet ready for prime time. J Clin Sleep Med . 2021;17(12):2565–2566. doi:10.5664/jcsm.9652

“…3 The use of autonomic arousals as a surrogate for electroencephalographic (EEG) arousals helps partially alleviate this problem. 4 Our data support the use of HSAT for ruling in but not ruling out sleep-disordered breathing in selected patients with NMD. Few participants in our study had evidence of a significant restrictive syndrome or chronic ventilatory failure.…”

Oxidative Stress Markers among Obstructive Sleep Apnea Patients

“…OSA severity is divided into mild, moderate, and severe, depending on AHI per hour; mild patients have 5 to 14 episodes per hour, moderate cases have 15 to 29 per hour, and severe cases have over 30 per hour [8]. Additional parameters, such as oxygen desaturation index (ODI), measured as the mean number of oxygen hemoglobin saturation drops of 3% or more per hour of subjective sleep duration, mean blood hemoglobin oxygen saturation (SpO 2 ), and duration of oxygen blood saturation below 90% (TSpO 2 < 90%) may also be measured during polysomnography [9,10]. The gold standard of treatment for OSA is a continuous positive airway pressure (CPAP), which can be delivered using a wide variety of devices.…”

Synchronization of the Processes of Autonomic Control of Blood Circulation in Humans Is Different in the Awake State and in Sleep Stages

“…The potential of using the analysis of autonomic processes instead of the analysis of electroencephalograms is noted in Mayer et al (2020) in the study of cardiorespiratory interaction. Currently, the classification of stages 1–3 of deep NREM sleep is successfully carried out by analyzing the instantaneous powers of various frequency components in electroencephalograms ( Hassan and Subasi, 2017 ; Nakamura et al, 2017 ; Gupta and Pachori, 2021 ).…”