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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2020.01007/full |
| _version_ | 1818843332229464064 |
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| author | Ben Aviner Ran Arieli Ran Arieli Alexandra Yalov |
| author_facet | Ben Aviner Ran Arieli Ran Arieli Alexandra Yalov |
| author_sort | Ben Aviner |
| collection | DOAJ |
| 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 KI = t2 × PO210.93, where the calculated risk from the Standard Normal distribution is ZI = [ln(KI0.5) – 8.99)]/0.81. For dry exposures this is KD = t2 × PO212.99, with risk ZD = [ln(KD0.5) – 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. |
| first_indexed | 2024-12-19T04:56:11Z |
| format | Article |
| id | doaj.art-87035d6ee6ab4dcebd33f2d0dd71adbf |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-19T04:56:11Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-87035d6ee6ab4dcebd33f2d0dd71adbf2022-12-21T20:35:13ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2020-08-011110.3389/fphys.2020.01007565258Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at RestBen Aviner0Ran Arieli1Ran Arieli2Alexandra Yalov3The Israel Naval Medical Institute, Israel Defense Forces Medical Corps, Haifa, IsraelThe Israel Naval Medical Institute, Israel Defense Forces Medical Corps, Haifa, IsraelEliachar Research Laboratory, Western Galilee Medical Center, Nahariya, IsraelHP – Indigo Division, Nes Ziona, IsraelPatients 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 KI = t2 × PO210.93, where the calculated risk from the Standard Normal distribution is ZI = [ln(KI0.5) – 8.99)]/0.81. For dry exposures this is KD = t2 × PO212.99, with risk ZD = [ln(KD0.5) – 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.https://www.frontiersin.org/article/10.3389/fphys.2020.01007/fullhyperbaric oxygen treatmentdivingalgorithmconvulsionssaturation |
| spellingShingle | Ben Aviner Ran Arieli Ran Arieli Alexandra Yalov Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest Frontiers in Physiology hyperbaric oxygen treatment diving algorithm convulsions saturation |
| 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 | hyperbaric oxygen treatment diving algorithm convulsions saturation |
| url | https://www.frontiersin.org/article/10.3389/fphys.2020.01007/full |
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