Cargando…

Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest

Patients undergoing hyperbaric oxygen therapy and divers engaged in underwater activity are at risk of central nervous system oxygen toxicity. An algorithm for predicting CNS oxygen toxicity in active underwater diving has been published previously, but not for humans at rest. Using a procedure simi...

Descripción completa

Detalles Bibliográficos
Autores principales: Aviner, Ben, Arieli, Ran, Yalov, Alexandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461992/
https://www.ncbi.nlm.nih.gov/pubmed/33013440
http://dx.doi.org/10.3389/fphys.2020.01007
_version_ 1783576837084938240
author Aviner, Ben
Arieli, Ran
Yalov, Alexandra
author_facet Aviner, Ben
Arieli, Ran
Yalov, Alexandra
author_sort Aviner, Ben
collection PubMed
description Patients undergoing hyperbaric oxygen therapy and divers engaged in underwater activity are at risk of central nervous system oxygen toxicity. An algorithm for predicting CNS oxygen toxicity in active underwater diving has been published previously, but not for humans at rest. Using a procedure similar to that employed for the derivation of our active diving algorithm, we collected data for exposures at rest, in which subjects breathed hyperbaric oxygen while immersed in thermoneutral water at 33°C (n = 219) or in dry conditions (n = 507). The maximal likelihood method was employed to solve for the parameters of the power equation. For immersion, the CNS oxygen toxicity index is K(I) = t(2) × PO(2)(10.93), where the calculated risk from the Standard Normal distribution is Z(I) = [ln(K(I)(0.5)) – 8.99)]/0.81. For dry exposures this is K(D) = t(2) × PO(2)(12.99), with risk Z(D) = [ln(K(D)(0.5)) – 11.34)]/0.65. We propose a method for interpolating the parameters at metabolic rates between 1 and 4.4 MET. The risk of CNS oxygen toxicity at rest was found to be greater during immersion than in dry conditions. We discuss the prediction properties of the new algorithm in the clinical hyperbaric environment, and suggest it may be adopted for use in planning procedures for hyperbaric oxygen therapy and for rest periods during saturation diving.
format Online
Article
Text
id pubmed-7461992
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-74619922020-10-01 Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest Aviner, Ben Arieli, Ran Yalov, Alexandra Front Physiol Physiology Patients undergoing hyperbaric oxygen therapy and divers engaged in underwater activity are at risk of central nervous system oxygen toxicity. An algorithm for predicting CNS oxygen toxicity in active underwater diving has been published previously, but not for humans at rest. Using a procedure similar to that employed for the derivation of our active diving algorithm, we collected data for exposures at rest, in which subjects breathed hyperbaric oxygen while immersed in thermoneutral water at 33°C (n = 219) or in dry conditions (n = 507). The maximal likelihood method was employed to solve for the parameters of the power equation. For immersion, the CNS oxygen toxicity index is K(I) = t(2) × PO(2)(10.93), where the calculated risk from the Standard Normal distribution is Z(I) = [ln(K(I)(0.5)) – 8.99)]/0.81. For dry exposures this is K(D) = t(2) × PO(2)(12.99), with risk Z(D) = [ln(K(D)(0.5)) – 11.34)]/0.65. We propose a method for interpolating the parameters at metabolic rates between 1 and 4.4 MET. The risk of CNS oxygen toxicity at rest was found to be greater during immersion than in dry conditions. We discuss the prediction properties of the new algorithm in the clinical hyperbaric environment, and suggest it may be adopted for use in planning procedures for hyperbaric oxygen therapy and for rest periods during saturation diving. Frontiers Media S.A. 2020-08-17 /pmc/articles/PMC7461992/ /pubmed/33013440 http://dx.doi.org/10.3389/fphys.2020.01007 Text en Copyright © 2020 Aviner, Arieli and Yalov. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Aviner, Ben
Arieli, Ran
Yalov, Alexandra
Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest
title Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest
title_full Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest
title_fullStr Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest
title_full_unstemmed Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest
title_short Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest
title_sort power equation for predicting the risk of central nervous system oxygen toxicity at rest
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461992/
https://www.ncbi.nlm.nih.gov/pubmed/33013440
http://dx.doi.org/10.3389/fphys.2020.01007
work_keys_str_mv AT avinerben powerequationforpredictingtheriskofcentralnervoussystemoxygentoxicityatrest
AT arieliran powerequationforpredictingtheriskofcentralnervoussystemoxygentoxicityatrest
AT yalovalexandra powerequationforpredictingtheriskofcentralnervoussystemoxygentoxicityatrest