• Mehdi Moazzez Lesko, Tom Jenks, Pablo Perel, Sarah O'Brien, Charmaine Childs, Omar Bouamra, and Fiona Lecky.
• 1 University of Manchester , Manchester Academic Health Science Centre, the Trauma Audit and Research Network (TARN), Salford Royal NHS Foundation Trust, Salford, United Kingdom .
• J. Neurotrauma. 2013 Dec 15; 30 (24): 2021-30.

AbstractCurrently available prognostic models in Traumatic Brain Injury (TBI) are derived from historical data sets or from heterogeneous data sets, depending upon the trauma care delivered. The objective of our study was to develop models to predict survival in a recent cohort of TBI patients within a relatively homogeneous trauma care system. Records of patients with brain injury were extracted from the Trauma Audit and Research Network (TARN) database. The relationship of the variables (i.e., age, Glasgow Coma Score [GCS], pupillary reactivity, Injury Severity Score [ISS], Computed Tomography [CT] classifications, classification of various intracranial pathologies, systolic and mean blood pressure, oxygen saturation, and the presence of extracranial injury) to survival at discharge were determined. Stepwise logistical regression analysis was performed to determine the best prognostic model. Two models were derived from data of 802 patients (models A and B). Age, GCS, pupillary reactivity, hypoxia, and brainstem injury are significant predictors in both. However, model A contains ISS in contrast to model B, which contains the presence of brain swelling and major extracranial injury instead. Both models have good predictive performance (model A: area under the Receiver Operating Characteristic [ROC] curve [AUC]=0.92 [95% CI, 0.90-0.95], Nagelkerke R(2), 0.62; model B: AUC=0.93 [95% CI: 0.91-0.95], Nagelkerke R(2): 0.63). Hence, two accurate and reliable prognostic models were developed from a recent cohort of the TBI population.

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