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Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator

We created an online calculator using machine learning (ML) algorithms to impute the partial pressure of oxygen (PaO(2))/fraction of delivered oxygen (FiO(2)) ratio using the non-invasive peripheral saturation of oxygen (SpO(2)) and compared the accuracy of the ML models we developed to published eq...

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Autores principales: Ren, Shuangxia, Zupetic, Jill A., Tabary, Mohammadreza, DeSensi, Rebecca, Nouraie, Mehdi, Lu, Xinghua, Boyce, Richard D., Lee, Janet S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114384/
https://www.ncbi.nlm.nih.gov/pubmed/35581469
http://dx.doi.org/10.1038/s41598-022-12419-7
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author Ren, Shuangxia
Zupetic, Jill A.
Tabary, Mohammadreza
DeSensi, Rebecca
Nouraie, Mehdi
Lu, Xinghua
Boyce, Richard D.
Lee, Janet S.
author_facet Ren, Shuangxia
Zupetic, Jill A.
Tabary, Mohammadreza
DeSensi, Rebecca
Nouraie, Mehdi
Lu, Xinghua
Boyce, Richard D.
Lee, Janet S.
author_sort Ren, Shuangxia
collection PubMed
description We created an online calculator using machine learning (ML) algorithms to impute the partial pressure of oxygen (PaO(2))/fraction of delivered oxygen (FiO(2)) ratio using the non-invasive peripheral saturation of oxygen (SpO(2)) and compared the accuracy of the ML models we developed to published equations. We generated three ML algorithms (neural network, regression, and kernel-based methods) using seven clinical variable features (N = 9900 ICU events) and subsequently three features (N = 20,198 ICU events) as input into the models. Data from mechanically ventilated ICU patients were obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC III) database and used for analysis. Compared to seven features, three features (SpO(2), FiO(2) and PEEP) were sufficient to impute PaO(2) from the SpO(2). Any of the ML models enabled imputation of PaO(2) from the SpO(2) with lower error and showed greater accuracy in predicting PaO(2)/FiO(2) ≤ 150 compared to the previously published log-linear and non-linear equations. To address potential hidden hypoxemia that occurs more frequently in Black patients, we conducted sensitivity analysis and show ML models outperformed published equations in both Black and White patients. Imputation using data from an independent validation cohort of ICU patients (N = 133) showed greater accuracy with ML models.
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spelling pubmed-91143842022-05-19 Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator Ren, Shuangxia Zupetic, Jill A. Tabary, Mohammadreza DeSensi, Rebecca Nouraie, Mehdi Lu, Xinghua Boyce, Richard D. Lee, Janet S. Sci Rep Article We created an online calculator using machine learning (ML) algorithms to impute the partial pressure of oxygen (PaO(2))/fraction of delivered oxygen (FiO(2)) ratio using the non-invasive peripheral saturation of oxygen (SpO(2)) and compared the accuracy of the ML models we developed to published equations. We generated three ML algorithms (neural network, regression, and kernel-based methods) using seven clinical variable features (N = 9900 ICU events) and subsequently three features (N = 20,198 ICU events) as input into the models. Data from mechanically ventilated ICU patients were obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC III) database and used for analysis. Compared to seven features, three features (SpO(2), FiO(2) and PEEP) were sufficient to impute PaO(2) from the SpO(2). Any of the ML models enabled imputation of PaO(2) from the SpO(2) with lower error and showed greater accuracy in predicting PaO(2)/FiO(2) ≤ 150 compared to the previously published log-linear and non-linear equations. To address potential hidden hypoxemia that occurs more frequently in Black patients, we conducted sensitivity analysis and show ML models outperformed published equations in both Black and White patients. Imputation using data from an independent validation cohort of ICU patients (N = 133) showed greater accuracy with ML models. Nature Publishing Group UK 2022-05-17 /pmc/articles/PMC9114384/ /pubmed/35581469 http://dx.doi.org/10.1038/s41598-022-12419-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ren, Shuangxia
Zupetic, Jill A.
Tabary, Mohammadreza
DeSensi, Rebecca
Nouraie, Mehdi
Lu, Xinghua
Boyce, Richard D.
Lee, Janet S.
Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator
title Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator
title_full Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator
title_fullStr Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator
title_full_unstemmed Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator
title_short Machine learning based algorithms to impute PaO(2) from SpO(2) values and development of an online calculator
title_sort machine learning based algorithms to impute pao(2) from spo(2) values and development of an online calculator
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114384/
https://www.ncbi.nlm.nih.gov/pubmed/35581469
http://dx.doi.org/10.1038/s41598-022-12419-7
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