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Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes

STUDY OBJECTIVES: Conventional metrics of sleep quantity/depth have serious shortcomings. Odds-Ratio-Product (ORP) is a continuous metric of sleep depth ranging from 0 (very deep sleep) to 2.5 (full-wakefulness). We describe an ORP-based approach that provides information on sleep disorders not appa...

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Autores principales: Younes, Magdy, Gerardy, Bethany, Pack, Allan I, Kuna, Samuel T, Castro-Diehl, Cecilia, Redline, Susan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195236/
https://www.ncbi.nlm.nih.gov/pubmed/35272350
http://dx.doi.org/10.1093/sleep/zsac059
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author Younes, Magdy
Gerardy, Bethany
Pack, Allan I
Kuna, Samuel T
Castro-Diehl, Cecilia
Redline, Susan
author_facet Younes, Magdy
Gerardy, Bethany
Pack, Allan I
Kuna, Samuel T
Castro-Diehl, Cecilia
Redline, Susan
author_sort Younes, Magdy
collection PubMed
description STUDY OBJECTIVES: Conventional metrics of sleep quantity/depth have serious shortcomings. Odds-Ratio-Product (ORP) is a continuous metric of sleep depth ranging from 0 (very deep sleep) to 2.5 (full-wakefulness). We describe an ORP-based approach that provides information on sleep disorders not apparent from traditional metrics. METHODS: We analyzed records from the Sleep-Heart-Health-Study and a study of performance deficit following sleep deprivation. ORP of all 30-second epochs in each PSG and percent of epochs in each decile of ORPs range were calculated. Percentage of epochs in deep sleep (ORP < 0.50) and in full-wakefulness (ORP > 2.25) were each assigned a rank, 1–3, representing first and second digits, respectively, of nine distinct types (“1,1”, “1,2” … ”3,3”). Prevalence of each type in clinical groups and their associations with demographics, sleepiness (Epworth-Sleepiness-Scale, ESS) and quality of life (QOL; Short-Form-Health-Survey-36) were determined. RESULTS: Three types (“1,1”, “1,2”, “1,3”) were prevalent in OSA and were associated with reduced QOL. Two (“1,3” and “2,3”) were prevalent in insomnia with short-sleep-duration (insomnia-SSD), but only “1,3” was associated with poor sleep depth and reduced QOL, suggesting two phenotypes in insomnia-SSD. ESS was high in types “1,1” and “1,2”, and low in “1,3” and “2,3”. Prevalence of some types increased with age while in others it decreased. Other types were either rare (“1,1” and “3,3”) or high (“2,2”) at all ages. CONCLUSIONS: The proposed ORP histogram offers specific and unique information on the underlying neurophysiological characteristics of sleep disorders not captured by routine metrics, with potential of advancing diagnosis and management of these disorders.
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spelling pubmed-91952362022-06-15 Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes Younes, Magdy Gerardy, Bethany Pack, Allan I Kuna, Samuel T Castro-Diehl, Cecilia Redline, Susan Sleep Sleep, Health, and Disease STUDY OBJECTIVES: Conventional metrics of sleep quantity/depth have serious shortcomings. Odds-Ratio-Product (ORP) is a continuous metric of sleep depth ranging from 0 (very deep sleep) to 2.5 (full-wakefulness). We describe an ORP-based approach that provides information on sleep disorders not apparent from traditional metrics. METHODS: We analyzed records from the Sleep-Heart-Health-Study and a study of performance deficit following sleep deprivation. ORP of all 30-second epochs in each PSG and percent of epochs in each decile of ORPs range were calculated. Percentage of epochs in deep sleep (ORP < 0.50) and in full-wakefulness (ORP > 2.25) were each assigned a rank, 1–3, representing first and second digits, respectively, of nine distinct types (“1,1”, “1,2” … ”3,3”). Prevalence of each type in clinical groups and their associations with demographics, sleepiness (Epworth-Sleepiness-Scale, ESS) and quality of life (QOL; Short-Form-Health-Survey-36) were determined. RESULTS: Three types (“1,1”, “1,2”, “1,3”) were prevalent in OSA and were associated with reduced QOL. Two (“1,3” and “2,3”) were prevalent in insomnia with short-sleep-duration (insomnia-SSD), but only “1,3” was associated with poor sleep depth and reduced QOL, suggesting two phenotypes in insomnia-SSD. ESS was high in types “1,1” and “1,2”, and low in “1,3” and “2,3”. Prevalence of some types increased with age while in others it decreased. Other types were either rare (“1,1” and “3,3”) or high (“2,2”) at all ages. CONCLUSIONS: The proposed ORP histogram offers specific and unique information on the underlying neurophysiological characteristics of sleep disorders not captured by routine metrics, with potential of advancing diagnosis and management of these disorders. Oxford University Press 2022-03-10 /pmc/articles/PMC9195236/ /pubmed/35272350 http://dx.doi.org/10.1093/sleep/zsac059 Text en © Sleep Research Society 2022. Published by Oxford University Press on behalf of the Sleep Research Society. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Sleep, Health, and Disease
Younes, Magdy
Gerardy, Bethany
Pack, Allan I
Kuna, Samuel T
Castro-Diehl, Cecilia
Redline, Susan
Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
title Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
title_full Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
title_fullStr Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
title_full_unstemmed Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
title_short Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
title_sort sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes
topic Sleep, Health, and Disease
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195236/
https://www.ncbi.nlm.nih.gov/pubmed/35272350
http://dx.doi.org/10.1093/sleep/zsac059
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