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...
Autores principales: | , , |
---|---|
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 |