TY - JOUR
T1 - Predicting the clinical trajectory in critically ill patients with sepsis: A cohort study
AU - Klein Klouwenberg, Peter M. C.
AU - Spitoni, Cristian
AU - van der Poll, Tom
AU - Bonten, Marc J.
AU - Cremer, Olaf L.
AU - Frencken, Jos F.
AU - van de Groep, Kirsten
AU - Koster-Brouwer, Marlies E.
AU - Ong, David S. Y.
AU - Verboom, Diana
AU - de Beer, Friso M.
AU - Bos, Lieuwe D. J.
AU - Glas, Gerie J.
AU - van Hooijdonk, Roosmarijn T. M.
AU - Schouten, Laura R. A.
AU - Straat, Marleen
AU - Witteveen, Esther
AU - Wieske, Luuk
AU - Hoogendijk, Arie J.
AU - Huson, Mischa A.
AU - van Vught, Lonneke A.
AU - Schultz, Marcus
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076419925&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/31831072
U2 - https://doi.org/10.1186/s13054-019-2687-z
DO - https://doi.org/10.1186/s13054-019-2687-z
M3 - Article
C2 - 31831072
SN - 1364-8535
VL - 23
JO - Critical care (London, England)
JF - Critical care (London, England)
IS - 1
M1 - 408
ER -