OBJECTIVE: Various prognostic models have been developed to predict outcome after traumatic brain injury (TBI). We aimed to determine the validity of six models that used baseline clinical and computed tomographic characteristics to predict mortality or unfavorable outcome at 6 months or later after severe or moderate TBI. STUDY DESIGN AND SETTING: The validity was studied in two selected series of TBI patients enrolled in clinical trials (Tirilazad trials; n = 2,269; International Selfotel Trial; n = 409) and in two unselected series of patients consecutively admitted to participating centers (European Brain Injury Consortium [EBIC] survey; n = 796; Traumatic Coma Data Bank; n = 746). Validity was indicated by discriminative ability (AUC) and calibration (Hosmer-Lemeshow goodness-of-fit test). RESULTS: The models varied in number of predictors (four to seven) and in development technique (two prediction trees and four logistic regression models). Discriminative ability varied widely (AUC: .61-.89), but calibration was poor for most models. Better discrimination was observed for logistic regression models compared with trees, and for models including more predictors. Further, discrimination was better when tested on unselected series that contained more heterogeneous populations. CONCLUSION: Our findings emphasize the need for external validation of prognostic models. The satisfactory discrimination indicates that logistic regression models, developed on large samples, can be used for classifying TBI patients according to prognostic risk.
OBJECTIVE: Various prognostic models have been developed to predict outcome after traumatic brain injury (TBI). We aimed to determine the validity of six models that used baseline clinical and computed tomographic characteristics to predict mortality or unfavorable outcome at 6 months or later after severe or moderate TBI. STUDY DESIGN AND SETTING: The validity was studied in two selected series of TBIpatients enrolled in clinical trials (Tirilazad trials; n = 2,269; International Selfotel Trial; n = 409) and in two unselected series of patients consecutively admitted to participating centers (European Brain Injury Consortium [EBIC] survey; n = 796; Traumatic Coma Data Bank; n = 746). Validity was indicated by discriminative ability (AUC) and calibration (Hosmer-Lemeshow goodness-of-fit test). RESULTS: The models varied in number of predictors (four to seven) and in development technique (two prediction trees and four logistic regression models). Discriminative ability varied widely (AUC: .61-.89), but calibration was poor for most models. Better discrimination was observed for logistic regression models compared with trees, and for models including more predictors. Further, discrimination was better when tested on unselected series that contained more heterogeneous populations. CONCLUSION: Our findings emphasize the need for external validation of prognostic models. The satisfactory discrimination indicates that logistic regression models, developed on large samples, can be used for classifying TBIpatients according to prognostic risk.
Authors: Bram Jacobs; Tjemme Beems; Ton M van der Vliet; Arie B van Vugt; Cornelia Hoedemaekers; Janneke Horn; Gaby Franschman; Ian Haitsma; Joukje van der Naalt; Teuntje M J C Andriessen; George F Borm; Pieter E Vos Journal: Neurocrit Care Date: 2013-08 Impact factor: 3.210
Authors: Alexis F Turgeon; François Lauzier; Jean-François Simard; Damon C Scales; Karen E A Burns; Lynne Moore; David A Zygun; Francis Bernard; Maureen O Meade; Tran Cong Dung; Mohana Ratnapalan; Stephanie Todd; John Harlock; Dean A Fergusson Journal: CMAJ Date: 2011-08-29 Impact factor: 8.262
Authors: Bob Roozenbeek; Andrew I R Maas; Anthony Marmarou; Isabella Butcher; Hester F Lingsma; Juan Lu; Gillian S McHugh; Gordon D Murray; Ewout W Steyerberg Journal: J Neurotrauma Date: 2009-07 Impact factor: 5.269