Mortality prediction models in the general trauma population: A systematic review.

Autor: de Munter L; Department Trauma TopCare, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands. Electronic address: l.demunter@etz.nl., Polinder S; Department of Public Health, Erasmus Medical Centre, Rotterdam, The Netherlands. Electronic address: s.polinder@erasmusmc.nl., Lansink KW; Department Trauma TopCare, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands; Brabant Trauma Registry, Network Emergency Care Brabant, The Netherlands; Department of Surgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands. Electronic address: k.lansink@etz.nl., Cnossen MC; Department of Public Health, Erasmus Medical Centre, Rotterdam, The Netherlands. Electronic address: m.c.cnossen@erasmusmc.nl., Steyerberg EW; Department of Public Health, Erasmus Medical Centre, Rotterdam, The Netherlands. Electronic address: e.steyerberg@erasmusmc.nl., de Jongh MA; Department Trauma TopCare, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands; Brabant Trauma Registry, Network Emergency Care Brabant, The Netherlands. Electronic address: m.d.jongh@nazb.nl.
Jazyk: angličtina
Zdroj: Injury [Injury] 2017 Feb; Vol. 48 (2), pp. 221-229. Date of Electronic Publication: 2016 Dec 15.
DOI: 10.1016/j.injury.2016.12.009
Abstrakt: Background: Trauma is the leading cause of death in individuals younger than 40 years. There are many different models for predicting patient outcome following trauma. To our knowledge, no comprehensive review has been performed on prognostic models for the general trauma population. Therefore, this review aimed to describe (1) existing mortality prediction models for the general trauma population, (2) the methodological quality and (3) which variables are most relevant for the model prediction of mortality in the general trauma population.
Methods: An online search was conducted in June 2015 using Embase, Medline, Web of Science, Cinahl, Cochrane, Google Scholar and PubMed. Relevant English peer-reviewed articles that developed, validated or updated mortality prediction models in a general trauma population were included.
Results: A total of 90 articles were included. The cohort sizes ranged from 100 to 1,115,389 patients, with overall mortality rates that ranged from 0.6% to 35%. The Trauma and Injury Severity Score (TRISS) was the most commonly used model. A total of 258 models were described in the articles, of which only 103 models (40%) were externally validated. Cases with missing values were often excluded and discrimination of the different prediction models ranged widely (AUROC between 0.59 and 0.98). The predictors were often included as dichotomized or categorical variables, while continuous variables showed better performance.
Conclusion: Researchers are still searching for a better mortality prediction model in the general trauma population. Models should 1) be developed and/or validated using an adequate sample size with sufficient events per predictor variable, 2) use multiple imputation models to address missing values, 3) use the continuous variant of the predictor if available and 4) incorporate all different types of readily available predictors (i.e., physiological variables, anatomical variables, injury cause/mechanism, and demographic variables). Furthermore, while mortality rates are decreasing, it is important to develop models that predict physical, cognitive status, or quality of life to measure quality of care.
(Copyright © 2016 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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