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Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index
Pao(2) is the gold standard to assess acute hypoxic respiratory failure, but it is only routinely available by intermittent spot checks, precluding any automatic continuous analysis for bedside tools. OBJECTIVE: To validate a continuous and noninvasive method to estimate hypoxemia severity for all S...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480940/ https://www.ncbi.nlm.nih.gov/pubmed/34604787 http://dx.doi.org/10.1097/CCE.0000000000000546 |
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author | Sauthier, Michaël Tuli, Gaurav Jouvet, Philippe A. Brownstein, John S. Randolph, Adrienne G. |
author_facet | Sauthier, Michaël Tuli, Gaurav Jouvet, Philippe A. Brownstein, John S. Randolph, Adrienne G. |
author_sort | Sauthier, Michaël |
collection | PubMed |
description | Pao(2) is the gold standard to assess acute hypoxic respiratory failure, but it is only routinely available by intermittent spot checks, precluding any automatic continuous analysis for bedside tools. OBJECTIVE: To validate a continuous and noninvasive method to estimate hypoxemia severity for all Spo(2) values. DERIVATION COHORT: All patients who had an arterial blood gas and simultaneous continuous noninvasive monitoring from 2011 to 2019 at Boston Children’s Hospital (Boston, MA) PICU. VALIDATION COHORT: External cohort at Sainte-Justine Hospital PICU (Montreal, QC, Canada) from 2017 to 2020. PREDICTION MODEL: We estimated the Pao(2) using three kinds of neural networks and an empirically optimized mathematical model derived from known physiologic equations. RESULTS: We included 52,879 Pao(2) (3,252 patients) in the derivation dataset and 12,047 Pao(2) (926 patients) in the validation dataset. The mean function on the last minute before the arterial blood gas had the lowest bias (bias –0.1% validation cohort). A difference greater than or equal to 3% between pulse rate and electrical heart rate decreased the intraclass correlation coefficients (0.75 vs 0.44; p < 0.001) implying measurement noise. Our estimated Pao(2) equation had the highest intraclass correlation coefficient (0.38; 95% CI, 0.36–0.39; validation cohort) and outperformed neural networks and existing equations. Using the estimated Pao(2) to estimate the oxygenation index showed a significantly better hypoxemia classification (kappa) than oxygenation saturation index for both Spo(2) less than or equal to 97% (0.79 vs 0.60; p < 0.001) and Spo(2) greater than 97% (0.58 vs 0.52; p < 0.001). CONCLUSION: The estimated Pao(2) using pulse rate and electrical heart rate Spo(2) validation allows a continuous and noninvasive estimation of the oxygenation index that is valid for Spo(2) less than or equal to 97% and for Spo(2) greater than 97%. Display of continuous analysis of estimated Pao(2) and estimated oxygenation index may provide decision support to assist with hypoxemia diagnosis and oxygen titration in critically ill patients. |
format | Online Article Text |
id | pubmed-8480940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-84809402021-09-30 Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index Sauthier, Michaël Tuli, Gaurav Jouvet, Philippe A. Brownstein, John S. Randolph, Adrienne G. Crit Care Explor Predictive Modeling Report Pao(2) is the gold standard to assess acute hypoxic respiratory failure, but it is only routinely available by intermittent spot checks, precluding any automatic continuous analysis for bedside tools. OBJECTIVE: To validate a continuous and noninvasive method to estimate hypoxemia severity for all Spo(2) values. DERIVATION COHORT: All patients who had an arterial blood gas and simultaneous continuous noninvasive monitoring from 2011 to 2019 at Boston Children’s Hospital (Boston, MA) PICU. VALIDATION COHORT: External cohort at Sainte-Justine Hospital PICU (Montreal, QC, Canada) from 2017 to 2020. PREDICTION MODEL: We estimated the Pao(2) using three kinds of neural networks and an empirically optimized mathematical model derived from known physiologic equations. RESULTS: We included 52,879 Pao(2) (3,252 patients) in the derivation dataset and 12,047 Pao(2) (926 patients) in the validation dataset. The mean function on the last minute before the arterial blood gas had the lowest bias (bias –0.1% validation cohort). A difference greater than or equal to 3% between pulse rate and electrical heart rate decreased the intraclass correlation coefficients (0.75 vs 0.44; p < 0.001) implying measurement noise. Our estimated Pao(2) equation had the highest intraclass correlation coefficient (0.38; 95% CI, 0.36–0.39; validation cohort) and outperformed neural networks and existing equations. Using the estimated Pao(2) to estimate the oxygenation index showed a significantly better hypoxemia classification (kappa) than oxygenation saturation index for both Spo(2) less than or equal to 97% (0.79 vs 0.60; p < 0.001) and Spo(2) greater than 97% (0.58 vs 0.52; p < 0.001). CONCLUSION: The estimated Pao(2) using pulse rate and electrical heart rate Spo(2) validation allows a continuous and noninvasive estimation of the oxygenation index that is valid for Spo(2) less than or equal to 97% and for Spo(2) greater than 97%. Display of continuous analysis of estimated Pao(2) and estimated oxygenation index may provide decision support to assist with hypoxemia diagnosis and oxygen titration in critically ill patients. Lippincott Williams & Wilkins 2021-09-28 /pmc/articles/PMC8480940/ /pubmed/34604787 http://dx.doi.org/10.1097/CCE.0000000000000546 Text en Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Predictive Modeling Report Sauthier, Michaël Tuli, Gaurav Jouvet, Philippe A. Brownstein, John S. Randolph, Adrienne G. Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index |
title | Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index |
title_full | Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index |
title_fullStr | Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index |
title_full_unstemmed | Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index |
title_short | Estimated Pao(2): A Continuous and Noninvasive Method to Estimate Pao(2) and Oxygenation Index |
title_sort | estimated pao(2): a continuous and noninvasive method to estimate pao(2) and oxygenation index |
topic | Predictive Modeling Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480940/ https://www.ncbi.nlm.nih.gov/pubmed/34604787 http://dx.doi.org/10.1097/CCE.0000000000000546 |
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