The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study.
apnea-hypopnea index
autonomic arousals
polysomnography
portable monitor devices
respiratory disturbance index
sleep apnea
Journal
Nature and science of sleep
ISSN: 1179-1608
Titre abrégé: Nat Sci Sleep
Pays: New Zealand
ID NLM: 101537767
Informations de publication
Date de publication:
2020
2020
Historique:
received:
22
04
2020
accepted:
30
06
2020
entrez:
9
8
2020
pubmed:
9
8
2020
medline:
9
8
2020
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 32765141
doi: 10.2147/NSS.S258276
pii: 258276
pmc: PMC7371436
doi:
Types de publication
Journal Article
Langues
eng
Pagination
443-451Informations de copyright
© 2020 Mayer et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflict of interest.
Références
Sleep Med. 2020 Jul;71:151-160
pubmed: 32247571
J Sleep Res. 1998 Mar;7(1):53-9
pubmed: 9613428
J Clin Sleep Med. 2018 Jul 15;14(7):1245-1247
pubmed: 29991439
J Clin Sleep Med. 2017 Apr 15;13(4):551-555
pubmed: 28095966
Psychiatry Res. 2019 Apr;274:27-29
pubmed: 30776709
Am J Respir Crit Care Med. 2004 Mar 15;169(6):668-72
pubmed: 15003950
J Clin Sleep Med. 2017 Mar 15;13(3):479-504
pubmed: 28162150
Chest. 2016 Jan;149(1):16-9
pubmed: 26182150
Sleep. 2013 Jun 01;36(6):881-9
pubmed: 23729931
Neurol Clin. 1996 Aug;14(3):611-28
pubmed: 8871979
Nat Sci Sleep. 2019 Dec 19;11:423-431
pubmed: 31908554
Thorax. 2007 May;62(5):422-7
pubmed: 17251319
Lancet Respir Med. 2015 Apr;3(4):310-8
pubmed: 25682233
Respirology. 2012 Feb;17(2):223-36
pubmed: 21992649
Nat Sci Sleep. 2018 Jul 17;10:193-201
pubmed: 30046255
Int J Cardiol. 2016 Jan 1;202:67-72
pubmed: 26386925
Clin Exp Rheumatol. 2017 May-Jun;35 Suppl 105(3):74-80
pubmed: 28681708
Chest. 2016 Jan;149(1):14-6
pubmed: 26181884
PLoS One. 2010 Apr 16;5(4):e10211
pubmed: 20419135
N Engl J Med. 1993 Apr 29;328(17):1230-5
pubmed: 8464434
J Clin Sleep Med. 2012 Oct 15;8(5):597-619
pubmed: 23066376
J Oral Rehabil. 2017 Dec;44(12):925-933
pubmed: 28853162
Curr Cardiol Rep. 2019 Nov 9;21(11):137
pubmed: 31707504
Eur Respir J. 2003 Oct;22(4):661-7
pubmed: 14582921
Chest. 2018 Mar;153(3):744-755
pubmed: 28629917
J Sleep Res. 2019 Dec;28(6):e12855
pubmed: 30941829
J Oral Rehabil. 2019 Dec;46(12):1185-1191
pubmed: 31287190
Sleep. 2016 May 01;39(5):1165-6
pubmed: 27070135
Sleep. 2011 Jan 01;34(1):73-81
pubmed: 21203376
Sleep. 2014 Apr 01;37(4):645-53
pubmed: 24899756
Sleep Breath. 2019 Jun;23(2):483-492
pubmed: 30112633
Sleep Med Rev. 2017 Oct;35:113-123
pubmed: 27815038
Sleep. 2013 Feb 01;36(2):249-57
pubmed: 23372273
Sleep. 1999 Aug 1;22(5):667-89
pubmed: 10450601
Clin Sci (Lond). 1994 Aug;87(2):269-73
pubmed: 7924174
J Dent Res. 2020 Jan;99(1):26-35
pubmed: 31702942
J Clin Sleep Med. 2007 Dec 15;3(7):737-47
pubmed: 18198809
J Clin Sleep Med. 2018 Aug 15;14(8):1339-1345
pubmed: 30092888