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Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study
BACKGROUND: To develop a mathematical model to estimate daily evolution of disease severity using routinely available parameters in patients admitted to the intensive care unit (ICU). METHODS: Over a 3-year period, we prospectively enrolled consecutive adults with sepsis and categorized patients as...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909511/ https://www.ncbi.nlm.nih.gov/pubmed/31831072 http://dx.doi.org/10.1186/s13054-019-2687-z |
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author | Klein Klouwenberg, Peter M. C. Spitoni, Cristian van der Poll, Tom Bonten, Marc J. Cremer, Olaf L. |
author_facet | Klein Klouwenberg, Peter M. C. Spitoni, Cristian van der Poll, Tom Bonten, Marc J. Cremer, Olaf L. |
author_sort | Klein Klouwenberg, Peter M. C. |
collection | PubMed |
description | BACKGROUND: To develop a mathematical model to estimate daily evolution of disease severity using routinely available parameters in patients admitted to the intensive care unit (ICU). METHODS: Over a 3-year period, we prospectively enrolled consecutive adults with sepsis and categorized patients as (1) being at risk for developing (more severe) organ dysfunction, (2) having (potentially still reversible) limited organ failure, or (3) having multiple-organ failure. Daily probabilities for transitions between these disease states, and to death or discharge, during the first 2 weeks in ICU were calculated using a multi-state model that was updated every 2 days using both baseline and time-varying information. The model was validated in independent patients. RESULTS: We studied 1371 sepsis admissions in 1251 patients. Upon presentation, 53 (4%) were classed at risk, 1151 (84%) had limited organ failure, and 167 (12%) had multiple-organ failure. Among patients with limited organ failure, 197 (17%) evolved to multiple-organ failure or died and 809 (70%) improved or were discharged alive within 14 days. Among patients with multiple-organ failure, 67 (40%) died and 91 (54%) improved or were discharged. Treatment response could be predicted with reasonable accuracy (c-statistic ranging from 0.55 to 0.81 for individual disease states, and 0.67 overall). Model performance in the validation cohort was similar. CONCLUSIONS: This prediction model that estimates daily evolution of disease severity during sepsis may eventually support clinicians in making better informed treatment decisions and could be used to evaluate prognostic biomarkers or perform in silico modeling of novel sepsis therapies during trial design. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01905033 |
format | Online Article Text |
id | pubmed-6909511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69095112019-12-19 Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study Klein Klouwenberg, Peter M. C. Spitoni, Cristian van der Poll, Tom Bonten, Marc J. Cremer, Olaf L. Crit Care Research BACKGROUND: To develop a mathematical model to estimate daily evolution of disease severity using routinely available parameters in patients admitted to the intensive care unit (ICU). METHODS: Over a 3-year period, we prospectively enrolled consecutive adults with sepsis and categorized patients as (1) being at risk for developing (more severe) organ dysfunction, (2) having (potentially still reversible) limited organ failure, or (3) having multiple-organ failure. Daily probabilities for transitions between these disease states, and to death or discharge, during the first 2 weeks in ICU were calculated using a multi-state model that was updated every 2 days using both baseline and time-varying information. The model was validated in independent patients. RESULTS: We studied 1371 sepsis admissions in 1251 patients. Upon presentation, 53 (4%) were classed at risk, 1151 (84%) had limited organ failure, and 167 (12%) had multiple-organ failure. Among patients with limited organ failure, 197 (17%) evolved to multiple-organ failure or died and 809 (70%) improved or were discharged alive within 14 days. Among patients with multiple-organ failure, 67 (40%) died and 91 (54%) improved or were discharged. Treatment response could be predicted with reasonable accuracy (c-statistic ranging from 0.55 to 0.81 for individual disease states, and 0.67 overall). Model performance in the validation cohort was similar. CONCLUSIONS: This prediction model that estimates daily evolution of disease severity during sepsis may eventually support clinicians in making better informed treatment decisions and could be used to evaluate prognostic biomarkers or perform in silico modeling of novel sepsis therapies during trial design. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01905033 BioMed Central 2019-12-12 /pmc/articles/PMC6909511/ /pubmed/31831072 http://dx.doi.org/10.1186/s13054-019-2687-z Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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. |
spellingShingle | Research Klein Klouwenberg, Peter M. C. Spitoni, Cristian van der Poll, Tom Bonten, Marc J. Cremer, Olaf L. Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study |
title | Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study |
title_full | Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study |
title_fullStr | Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study |
title_full_unstemmed | Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study |
title_short | Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study |
title_sort | predicting the clinical trajectory in critically ill patients with sepsis: a cohort study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909511/ https://www.ncbi.nlm.nih.gov/pubmed/31831072 http://dx.doi.org/10.1186/s13054-019-2687-z |
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