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

Autor: Klein Klouwenberg PMC; Department of Medical Microbiology and Immunology, Rijnstate Hospital, Wagnerlaan 55, 6815 AD, Arnhem, the Netherlands. peterkk@live.com., Spitoni C; Department of Mathematics, University Utrecht, Utrecht, the Netherlands., van der Poll T; Center for Experimental and Molecular Medicine, Division of Infectious Diseases, Amsterdam University Medical Centers, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands., Bonten MJ; Department of Medical Microbiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands., Cremer OL; Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Jazyk: angličtina
Zdroj: Critical care (London, England) [Crit Care] 2019 Dec 12; Vol. 23 (1), pp. 408. Date of Electronic Publication: 2019 Dec 12.
DOI: 10.1186/s13054-019-2687-z
Abstrakt: 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.
Databáze: MEDLINE
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