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Outcome Prediction in Moderate and Severe Traumatic Brain Injury: A Focus on Computed Tomography Variables |
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| 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 |
| Institution: | 1. Department of Neurology (935), Radboud University Nijmegen Medical Centre (RUNMC), P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands 11. Department of Neurology (AB51), University Medical Centre Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands 2. Department of Neurosurgery (931), Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands 12. Department of Radiology, University Medical Centre Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands 3. Department of Radiology, RUNMC, Nijmegen, The Netherlands 13. Department of Surgery, Medisch Spectrum Twente, P.O. Box 50.000, 7500 KA, Enschede, The Netherlands 4. Department of Emergency Medicine, RUNMC, Nijmegen, The Netherlands 5. Department of Intensive Care Medicine (632), Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands 6. Department of Intensive Care Medicine, Academic Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands 7. Department of Anesthesiology, VU University Medical Centre, Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands 8. Department of Neurosurgery, Erasmus Medical Centre, Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands 9. Department of Neurology, University Medical Centre Groningen, University of Groningen, P.O. Box 30.001, 9700 RB, Groningen, The Netherlands 10. Department of Epidemiology, Biostatistics and HTA (133), Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands |
| Abstract: | BackgroundWith this study we aimed to design validated outcome prediction models in moderate and severe traumatic brain injury (TBI) using demographic, clinical, and radiological parameters.MethodsSeven hundred consecutive moderate or severe TBI patients were included in this observational prospective cohort study. After inclusion, clinical data were collected, initial head computed tomography (CT) scans were rated, and at 6 months outcome was determined using the extended Glasgow Outcome Scale. Multivariate binary logistic regression analysis was applied to evaluate the association between potential predictors and three different outcome endpoints. The prognostic models that resulted were externally validated in a national Dutch TBI cohort.ResultsIn line with previous literature we identified age, pupil responses, Glasgow Coma Scale score and the occurrence of a hypotensive episode post-injury as predictors. Furthermore, several CT characteristics were associated with outcome; the aspect of the ambient cisterns being the most powerful. After external validation using Receiver Operating Characteristic (ROC) analysis our prediction models demonstrated adequate discriminative values, quantified by the area under the ROC curve, of 0.86 for death versus survival and 0.83 for unfavorable versus favorable outcome. Discriminative power was less for unfavorable outcome in survivors: 0.69.ConclusionsOutcome prediction in moderate and severe TBI might be improved using the models that were designed in this study. However, conventional demographic, clinical and CT variables proved insufficient to predict disability in surviving patients. The information that can be derived from our prediction rules is important for the selection and stratification of patients recruited into clinical TBI trials. |
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