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Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach
Background: The detrimental impact of fluid overload (FO) on intensive care unit (ICU) morbidity and mortality is well known. However, research to identify subgroups of patients particularly prone to fluid overload is scarce. The aim of this cohort study was to derive “FO phenotypes” in the critical...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780174/ https://www.ncbi.nlm.nih.gov/pubmed/35054030 http://dx.doi.org/10.3390/jcm11020336 |
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author | Messmer, Anna S. Moser, Michel Zuercher, Patrick Schefold, Joerg C. Müller, Martin Pfortmueller, Carmen A. |
author_facet | Messmer, Anna S. Moser, Michel Zuercher, Patrick Schefold, Joerg C. Müller, Martin Pfortmueller, Carmen A. |
author_sort | Messmer, Anna S. |
collection | PubMed |
description | Background: The detrimental impact of fluid overload (FO) on intensive care unit (ICU) morbidity and mortality is well known. However, research to identify subgroups of patients particularly prone to fluid overload is scarce. The aim of this cohort study was to derive “FO phenotypes” in the critically ill by using machine learning techniques. Methods: Retrospective single center study including adult intensive care patients with a length of stay of ≥3 days and sufficient data to compute FO. Data was analyzed by multivariable logistic regression, fast and frugal trees (FFT), classification decision trees (DT), and a random forest (RF) model. Results: Out of 1772 included patients, 387 (21.8%) met the FO definition. The random forest model had the highest area under the curve (AUC) (0.84, 95% CI 0.79–0.86), followed by multivariable logistic regression (0.81, 95% CI 0.77–0.86), FFT (0.75, 95% CI 0.69–0.79) and DT (0.73, 95% CI 0.68–0.78) to predict FO. The most important predictors identified in all models were lactate and bicarbonate at admission and postsurgical ICU admission. Sepsis/septic shock was identified as a risk factor in the MV and RF analysis. Conclusion: The FO phenotypes consist of patients admitted after surgery or with sepsis/septic shock with high lactate and low bicarbonate. |
format | Online Article Text |
id | pubmed-8780174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87801742022-01-22 Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach Messmer, Anna S. Moser, Michel Zuercher, Patrick Schefold, Joerg C. Müller, Martin Pfortmueller, Carmen A. J Clin Med Article Background: The detrimental impact of fluid overload (FO) on intensive care unit (ICU) morbidity and mortality is well known. However, research to identify subgroups of patients particularly prone to fluid overload is scarce. The aim of this cohort study was to derive “FO phenotypes” in the critically ill by using machine learning techniques. Methods: Retrospective single center study including adult intensive care patients with a length of stay of ≥3 days and sufficient data to compute FO. Data was analyzed by multivariable logistic regression, fast and frugal trees (FFT), classification decision trees (DT), and a random forest (RF) model. Results: Out of 1772 included patients, 387 (21.8%) met the FO definition. The random forest model had the highest area under the curve (AUC) (0.84, 95% CI 0.79–0.86), followed by multivariable logistic regression (0.81, 95% CI 0.77–0.86), FFT (0.75, 95% CI 0.69–0.79) and DT (0.73, 95% CI 0.68–0.78) to predict FO. The most important predictors identified in all models were lactate and bicarbonate at admission and postsurgical ICU admission. Sepsis/septic shock was identified as a risk factor in the MV and RF analysis. Conclusion: The FO phenotypes consist of patients admitted after surgery or with sepsis/septic shock with high lactate and low bicarbonate. MDPI 2022-01-11 /pmc/articles/PMC8780174/ /pubmed/35054030 http://dx.doi.org/10.3390/jcm11020336 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Messmer, Anna S. Moser, Michel Zuercher, Patrick Schefold, Joerg C. Müller, Martin Pfortmueller, Carmen A. Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach |
title | Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach |
title_full | Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach |
title_fullStr | Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach |
title_full_unstemmed | Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach |
title_short | Fluid Overload Phenotypes in Critical Illness—A Machine Learning Approach |
title_sort | fluid overload phenotypes in critical illness—a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780174/ https://www.ncbi.nlm.nih.gov/pubmed/35054030 http://dx.doi.org/10.3390/jcm11020336 |
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