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Open Access 01-12-2019 | Septicemia | Research

Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study

Authors: Peter M. C. Klein Klouwenberg, Cristian Spitoni, Tom van der Poll, Marc J. Bonten, Olaf L. Cremer, on behalf of the MARS consortium

Published in: Critical Care | Issue 1/2019

Abstract

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

Available only for authorised users

Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.CrossRefPubMedPubMedCentral

Klein Klouwenberg PM, Ong DS, Bos LD, de Beer FM, van Hooijdonk RT, Huson MA, Straat M, van Vught LA, Wieske L, Horn J, et al. Interobserver agreement of Centers for Disease Control and Prevention criteria for classifying infections in critically ill patients. Crit Care Med. 2013;41(10):2373–8.CrossRefPubMed

Rubulotta F, Marshall JC, Ramsay G, Nelson D, Levy M, Williams M. Predisposition, insult/infection, response, and organ dysfunction: a new model for staging severe sepsis. Crit Care Med. 2009;37(4):1329–35.CrossRefPubMed

Levy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D, Cohen J, Opal SM, Vincent JL, Ramsay G. 2001 SCCM/ESICM/ACCP/ATS/SIS international sepsis definitions conference. Intensive Care Med. 2003;29(4):530–8.CrossRefPubMed

Klein Klouwenberg PM, Zaal IJ, Spitoni C, Ong DS, van der Kooi AW, Bonten MJ, Slooter AJ, Cremer OL. The attributable mortality of delirium in critically ill patients: prospective cohort study. Bmj. 2014;349:g6652.CrossRefPubMedPubMedCentral

Jackson CH. Multi-state models for panel data: the msm package for R. J Stat Software. 2011;38(8):28.

Ohman EM, Granger CB, Harrington RA, Lee KL. Risk stratification and therapeutic decision making in acute coronary syndromes. JAMA. 2000;284(7):876–8.CrossRefPubMed

Hand DJT, R.J. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn. 2001;45(2):171–86.CrossRef

Spitoni C, Lammens V, Putter H. Prediction errors for state occupation and transition probabilities in multi-state models. Biom J. 2018;60(1):34–48.CrossRefPubMed

10.

Team R: R: a language and environment for statistical computing. R Foundation for Statistical Computing. Available from: http://www.r-project.org/. 2013.

11.

Leon AL, Hoyos NA, Barrera LI, De La Rosa G, Dennis R, Duenas C, Granados M, Londono D, Rodriguez FA, Molina FJ, et al. Clinical course of sepsis, severe sepsis, and septic shock in a cohort of infected patients from ten Colombian hospitals. BMC Infect Dis. 2013;13:345.CrossRefPubMedPubMedCentral

Title: Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study
Authors: Peter M. C. Klein Klouwenberg
Cristian Spitoni
Tom van der Poll
Marc J. Bonten
Olaf L. Cremer
on behalf of the MARS consortium
Publication date: 01-12-2019
Publisher: BioMed Central
Keywords: Septicemia
Septicemia
Published in: Critical Care / Issue 1/2019
Electronic ISSN: 1364-8535
DOI: https://doi.org/10.1186/s13054-019-2687-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study

Abstract

Background

Methods

Results

Conclusions

Clinical trial registration

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Clinical trial registration

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