• Crit Care · Dec 2019

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

    • Klein Klouwenberg Peter M C PMC http://orcid.org/0000-0002-8155-5336 Department of Medical Microbiology and Immunology, Rijnstate Hospital, Wagnerlaan, Cristian Spitoni, Tom van der Poll, Marc J Bonten, Olaf L Cremer, and MARS consortium.
    • Department of Medical Microbiology and Immunology, Rijnstate Hospital, Wagnerlaan 55, 6815 AD, Arnhem, the Netherlands. peterkk@live.com.
    • Crit Care. 2019 Dec 12; 23 (1): 408.

    BackgroundTo develop a mathematical model to estimate daily evolution of disease severity using routinely available parameters in patients admitted to the intensive care unit (ICU).MethodsOver 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.ResultsWe 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.ConclusionsThis 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 RegistrationClinicalTrials.gov NCT01905033.

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