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End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study

BACKGROUND: A non-invasive surrogate measurement for central venous oxygen saturation (ScVO2) would be useful in the ED for assessing therapeutic interventions in critically ill patients. We hypothesized that either linear or nonlinear mathematical manipulation of the partial pressure of oxygen in b...

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Autores principales: Jones, Alan E, Kuehne, Karl, Steuerwald, Michael, Kline, Jeffrey A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1592120/
https://www.ncbi.nlm.nih.gov/pubmed/16987417
http://dx.doi.org/10.1186/1471-227X-6-9
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author Jones, Alan E
Kuehne, Karl
Steuerwald, Michael
Kline, Jeffrey A
author_facet Jones, Alan E
Kuehne, Karl
Steuerwald, Michael
Kline, Jeffrey A
author_sort Jones, Alan E
collection PubMed
description BACKGROUND: A non-invasive surrogate measurement for central venous oxygen saturation (ScVO2) would be useful in the ED for assessing therapeutic interventions in critically ill patients. We hypothesized that either linear or nonlinear mathematical manipulation of the partial pressure of oxygen in breath at end expiration (EtO2) would accurately predict ScVO2. METHODS: Prospective observational study of a convenience sample of hemodialysis patients age > 17 years with existing upper extremity central venous catheters were enrolled. Using a portable respiratory device, we collected both tidal breathing and end expiratory oxygen and carbon dioxide concentrations, volume and flow on each patient. Simultaneous ScVO2 measurements were obtained via blood samples collected from the hemodialysis catheter. Two models were used to predict ScVO2: 1) Best-fit multivariate linear regression equation incorporating all respiratory variables; 2) MathCAD to model the decay curve of EtO2 versus expiratory volume using the least squares method to estimate the pO2 that would occur at <20% of total lung capacity. RESULTS: From 21 patients, the correlation between EtO2 and measured ScVO2 yielded R(2 )= 0.11. The best fit multivariate equation included EtCO2 and EtO2 and when solved for ScVO2, the equation yielded a mean absolute difference from the measured ScVO2 of 8 ± 6% (range -18 to +17%). The predicted ScVO2 value was within 10% of the actual value for 57% of the patients. Modeling of the EtO2 curve did not accurately predict ScVO2 at any lung volume. CONCLUSION: We found no significant correlation between EtO2 and ScVO2. A linear equation incorporating EtCO2 and EtO2 had at best modest predictive accuracy for ScVO2.
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spelling pubmed-15921202006-10-05 End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study Jones, Alan E Kuehne, Karl Steuerwald, Michael Kline, Jeffrey A BMC Emerg Med Research Article BACKGROUND: A non-invasive surrogate measurement for central venous oxygen saturation (ScVO2) would be useful in the ED for assessing therapeutic interventions in critically ill patients. We hypothesized that either linear or nonlinear mathematical manipulation of the partial pressure of oxygen in breath at end expiration (EtO2) would accurately predict ScVO2. METHODS: Prospective observational study of a convenience sample of hemodialysis patients age > 17 years with existing upper extremity central venous catheters were enrolled. Using a portable respiratory device, we collected both tidal breathing and end expiratory oxygen and carbon dioxide concentrations, volume and flow on each patient. Simultaneous ScVO2 measurements were obtained via blood samples collected from the hemodialysis catheter. Two models were used to predict ScVO2: 1) Best-fit multivariate linear regression equation incorporating all respiratory variables; 2) MathCAD to model the decay curve of EtO2 versus expiratory volume using the least squares method to estimate the pO2 that would occur at <20% of total lung capacity. RESULTS: From 21 patients, the correlation between EtO2 and measured ScVO2 yielded R(2 )= 0.11. The best fit multivariate equation included EtCO2 and EtO2 and when solved for ScVO2, the equation yielded a mean absolute difference from the measured ScVO2 of 8 ± 6% (range -18 to +17%). The predicted ScVO2 value was within 10% of the actual value for 57% of the patients. Modeling of the EtO2 curve did not accurately predict ScVO2 at any lung volume. CONCLUSION: We found no significant correlation between EtO2 and ScVO2. A linear equation incorporating EtCO2 and EtO2 had at best modest predictive accuracy for ScVO2. BioMed Central 2006-09-20 /pmc/articles/PMC1592120/ /pubmed/16987417 http://dx.doi.org/10.1186/1471-227X-6-9 Text en Copyright © 2006 Jones et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jones, Alan E
Kuehne, Karl
Steuerwald, Michael
Kline, Jeffrey A
End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study
title End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study
title_full End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study
title_fullStr End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study
title_full_unstemmed End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study
title_short End expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study
title_sort end expiratory oxygen concentrations to predict central venous oxygen saturation: an observational pilot study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1592120/
https://www.ncbi.nlm.nih.gov/pubmed/16987417
http://dx.doi.org/10.1186/1471-227X-6-9
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