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Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit
An accurate method to predict the mortality in the intensive care unit (ICU) patients has been required, especially in children. The aim of this study is to evaluate the value of serum anion gap (AG) for predicting mortality in pediatric ICU (PICU). We reviewed a data of 461 pediatric patients were...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5431089/ https://www.ncbi.nlm.nih.gov/pubmed/28469150 http://dx.doi.org/10.1038/s41598-017-01681-9 |
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author | Kim, Min Jung Kim, Yoon Hee Sol, In Suk Kim, Soo Yeon Kim, Jong Deok Kim, Ha Yan Kim, Kyung Won Sohn, Myung Hyun Kim, Kyu-Earn |
author_facet | Kim, Min Jung Kim, Yoon Hee Sol, In Suk Kim, Soo Yeon Kim, Jong Deok Kim, Ha Yan Kim, Kyung Won Sohn, Myung Hyun Kim, Kyu-Earn |
author_sort | Kim, Min Jung |
collection | PubMed |
description | An accurate method to predict the mortality in the intensive care unit (ICU) patients has been required, especially in children. The aim of this study is to evaluate the value of serum anion gap (AG) for predicting mortality in pediatric ICU (PICU). We reviewed a data of 461 pediatric patients were collected on PICU admission. Corrected anion gap (cAG), the AG compensated for abnormal albumin levels, was significantly lower in survivors compared with nonsurvivors (p < 0.001). Multivariable logistic regression analysis identified the following variables as independent predictors of mortality; cAG (OR 1.110, 95% CI 1.06–1.17; p < 0.001), PIM3 [OR 7.583, 95% CI 1.81–31.78; p = 0.006], and PRISM III [OR 1.076, 95% CI 1.02–1.14; p = 0.008]. Comparing AUCs for mortality prediction, there were no statistically significant differences between cAG and other mortality prediction models; cAG 0.728, PIM2 0.779, PIM3 0.822, and PRISM III 0.808. The corporation of cAG to pre-existing mortality prediction models was significantly more accurate at predicting mortality than using any of these models alone. We concluded that cAG at ICU admission may be used to predict mortality in children, regardless of underlying etiology. And the incorporation of cAG to pre-existing mortality prediction models might improve predictability. |
format | Online Article Text |
id | pubmed-5431089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-54310892017-05-16 Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit Kim, Min Jung Kim, Yoon Hee Sol, In Suk Kim, Soo Yeon Kim, Jong Deok Kim, Ha Yan Kim, Kyung Won Sohn, Myung Hyun Kim, Kyu-Earn Sci Rep Article An accurate method to predict the mortality in the intensive care unit (ICU) patients has been required, especially in children. The aim of this study is to evaluate the value of serum anion gap (AG) for predicting mortality in pediatric ICU (PICU). We reviewed a data of 461 pediatric patients were collected on PICU admission. Corrected anion gap (cAG), the AG compensated for abnormal albumin levels, was significantly lower in survivors compared with nonsurvivors (p < 0.001). Multivariable logistic regression analysis identified the following variables as independent predictors of mortality; cAG (OR 1.110, 95% CI 1.06–1.17; p < 0.001), PIM3 [OR 7.583, 95% CI 1.81–31.78; p = 0.006], and PRISM III [OR 1.076, 95% CI 1.02–1.14; p = 0.008]. Comparing AUCs for mortality prediction, there were no statistically significant differences between cAG and other mortality prediction models; cAG 0.728, PIM2 0.779, PIM3 0.822, and PRISM III 0.808. The corporation of cAG to pre-existing mortality prediction models was significantly more accurate at predicting mortality than using any of these models alone. We concluded that cAG at ICU admission may be used to predict mortality in children, regardless of underlying etiology. And the incorporation of cAG to pre-existing mortality prediction models might improve predictability. Nature Publishing Group UK 2017-05-03 /pmc/articles/PMC5431089/ /pubmed/28469150 http://dx.doi.org/10.1038/s41598-017-01681-9 Text en © The Author(s) 2017 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kim, Min Jung Kim, Yoon Hee Sol, In Suk Kim, Soo Yeon Kim, Jong Deok Kim, Ha Yan Kim, Kyung Won Sohn, Myung Hyun Kim, Kyu-Earn Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit |
title | Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit |
title_full | Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit |
title_fullStr | Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit |
title_full_unstemmed | Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit |
title_short | Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit |
title_sort | serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5431089/ https://www.ncbi.nlm.nih.gov/pubmed/28469150 http://dx.doi.org/10.1038/s41598-017-01681-9 |
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