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Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit
Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931234/ https://www.ncbi.nlm.nih.gov/pubmed/36812513 http://dx.doi.org/10.1371/journal.pdig.0000019 |
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author | Kausch, Sherry L. Sullivan, Brynne Spaeder, Michael C. Keim-Malpass, Jessica |
author_facet | Kausch, Sherry L. Sullivan, Brynne Spaeder, Michael C. Keim-Malpass, Jessica |
author_sort | Kausch, Sherry L. |
collection | PubMed |
description | Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness. |
format | Online Article Text |
id | pubmed-9931234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99312342023-02-16 Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit Kausch, Sherry L. Sullivan, Brynne Spaeder, Michael C. Keim-Malpass, Jessica PLOS Digit Health Research Article Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness. Public Library of Science 2022-03-17 /pmc/articles/PMC9931234/ /pubmed/36812513 http://dx.doi.org/10.1371/journal.pdig.0000019 Text en © 2022 Kausch et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kausch, Sherry L. Sullivan, Brynne Spaeder, Michael C. Keim-Malpass, Jessica Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit |
title | Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit |
title_full | Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit |
title_fullStr | Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit |
title_full_unstemmed | Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit |
title_short | Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit |
title_sort | individual illness dynamics: an analysis of children with sepsis admitted to the pediatric intensive care unit |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931234/ https://www.ncbi.nlm.nih.gov/pubmed/36812513 http://dx.doi.org/10.1371/journal.pdig.0000019 |
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