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Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data
BACKGROUND: Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739461/ https://www.ncbi.nlm.nih.gov/pubmed/33323104 http://dx.doi.org/10.1186/s12911-020-01326-4 |
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author | Yeh, Pete Pan, Yiheng Sanchez-Pinto, L. Nelson Luo, Yuan |
author_facet | Yeh, Pete Pan, Yiheng Sanchez-Pinto, L. Nelson Luo, Yuan |
author_sort | Yeh, Pete |
collection | PubMed |
description | BACKGROUND: Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study the general ICU population of the Medical Information Mart for Intensive Care III (MIMIC-III) database to corroborate these associations, and propose a supervised learning model for the prediction of hyperchloremia in ICU patients. METHODS: We assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortality, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regression analysis. Four predictive supervised learning classifiers were trained to predict hyperchloremia using features representative of clinical records from the first 24h of adult ICU stays. RESULTS: Hyperchloremia was shown to have an independent association with increased odds of ICU mortality, new AKI by day 7, and MODS on day 7. High chloride load was also associated with increased odds of ICU mortality. Our best performing supervised learning model predicted second-day hyperchloremia with an AUC of 0.76 and a number needed to alert (NNA) of 7—a clinically-actionable rate. CONCLUSIONS: Our results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes. |
format | Online Article Text |
id | pubmed-7739461 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77394612020-12-17 Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data Yeh, Pete Pan, Yiheng Sanchez-Pinto, L. Nelson Luo, Yuan BMC Med Inform Decis Mak Research BACKGROUND: Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study the general ICU population of the Medical Information Mart for Intensive Care III (MIMIC-III) database to corroborate these associations, and propose a supervised learning model for the prediction of hyperchloremia in ICU patients. METHODS: We assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortality, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regression analysis. Four predictive supervised learning classifiers were trained to predict hyperchloremia using features representative of clinical records from the first 24h of adult ICU stays. RESULTS: Hyperchloremia was shown to have an independent association with increased odds of ICU mortality, new AKI by day 7, and MODS on day 7. High chloride load was also associated with increased odds of ICU mortality. Our best performing supervised learning model predicted second-day hyperchloremia with an AUC of 0.76 and a number needed to alert (NNA) of 7—a clinically-actionable rate. CONCLUSIONS: Our results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes. BioMed Central 2020-12-15 /pmc/articles/PMC7739461/ /pubmed/33323104 http://dx.doi.org/10.1186/s12911-020-01326-4 Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Yeh, Pete Pan, Yiheng Sanchez-Pinto, L. Nelson Luo, Yuan Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data |
title | Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data |
title_full | Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data |
title_fullStr | Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data |
title_full_unstemmed | Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data |
title_short | Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data |
title_sort | hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739461/ https://www.ncbi.nlm.nih.gov/pubmed/33323104 http://dx.doi.org/10.1186/s12911-020-01326-4 |
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