Predicting the clinical trajectory in critically ill patients with sepsis: A cohort study
| Autor: | Klein Klouwenberg, Peter M. C., Spitoni, Cristian, van der Poll, Tom, Bonten, Marc J., Cremer, Olaf L., Frencken, Jos F., van de Groep, Kirsten, Koster-Brouwer, Marlies E., Ong, David S. Y., Verboom, Diana, de Beer, Friso M., Bos, Lieuwe D. J., Glas, Gerie J., van Hooijdonk, Roosmarijn T. M., Schouten, Laura R. A., Straat, Marleen, Witteveen, Esther, Wieske, Luuk, Hoogendijk, Arie J., Huson, Mischa A., van Vught, Lonneke A., Schultz, Marcus |
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| Přispěvatelé: | Center of Experimental and Molecular Medicine, Infectious diseases, AII - Infectious diseases, Anesthesiology, Graduate School, ACS - Heart failure & arrhythmias, ANS - Neuroinfection & -inflammation, Intensive Care Medicine, ACS - Diabetes & metabolism, APH - Quality of Care, Neurology, ANS - Amsterdam Neuroscience, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, ARD - Amsterdam Reproduction and Development, ACS - Pulmonary hypertension & thrombosis, ACS - Microcirculation |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
Epidemiology Discharged alive Disease Research Support Critical Care and Intensive Care Medicine law.invention Sepsis 03 medical and health sciences 0302 clinical medicine law medicine Journal Article Organ failure Intensive care unit 030212 general & internal medicine Non-U.S. Gov't Outcome Critically ill business.industry Research Support Non-U.S. Gov't Organ dysfunction lcsh:Medical emergencies. Critical care. Intensive care. First aid 030208 emergency & critical care medicine lcsh:RC86-88.9 medicine.disease Markov model Emergency medicine medicine.symptom business Cohort study |
| Zdroj: | Critical Care, Vol 23, Iss 1, Pp 1-9 (2019) Critical care (London, England), 23(1). Springer Science + Business Media Critical care (London, England), 23(1):408. Springer Science + Business Media |
| ISSN: | 1364-8535 1466-609X |
| Popis: | 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: | OpenAIRE |
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