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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...

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Autores principales: Kausch, Sherry L., Sullivan, Brynne, Spaeder, Michael C., Keim-Malpass, Jessica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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.
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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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