title: Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics.
abstract: BACKGROUND
  Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors.
  
  
  RESULTS
  Prospectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial.
  
  
  CONCLUSIONS
  Prognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials.
authors.affiliation: Center for Medical Decision Sciences, Department of Public Health, Erasmus MC, Rotterdam, The Netherlands. E.Steyerberg@ErasmusMC.nl
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.forename: Nino
authors.initials: N
authors.lastname: Mushkudiani
authors.forename: Pablo
authors.initials: P
authors.lastname: Perel
authors.forename: Isabella
authors.initials: I
authors.lastname: Butcher
authors.forename: Juan
authors.initials: J
authors.lastname: Lu
authors.forename: Gillian S
authors.initials: GS
authors.lastname: McHugh
authors.forename: Gordon D
authors.initials: GD
authors.lastname: Murray
authors.forename: Anthony
authors.initials: A
authors.lastname: Marmarou
authors.forename: Ian
authors.initials: I
authors.lastname: Roberts
authors.forename: J Dik F
authors.initials: JD
authors.lastname: Habbema
authors.forename: Andrew I R
authors.initials: AI
authors.lastname: Maas
mesh_terms: D000328:Adult; D001930:Brain Injuries / Q000175:diagnosis* / Q000401:mortality / Q000628:therapy*; D002986:Clinical Trials as Topic; D006801:Humans; D008875:Middle Aged; D008954:Models, Biological; D010343:Patient Admission; D011379:Prognosis; D015203:Reproducibility of Results; D015599:Trauma Severity Indices; D016896:Treatment Outcome