Systematic review of prognostic models in traumatic brain injury

Studies that combine at least two variables to predict any outcome in patients with TBI were searched in PUBMED and EMBASE. Two reviewers independently examined titles, abstracts and assessed whether each met the pre-defined inclusion criteria.A total of 53 reports including 102 models were identified. Almost half (47%) were derived from adult patients. Three quarters of the models included less than 500 patients. Most of the models (93%) were from high income countries populations. Logistic regression was the most common analytical strategy to derived models (47%). In relation to the quality of the derivation models (n:66), only 15% reported less than 10% pf loss to follow-up, 68% did not justify the rationale to include the predictors, 11% conducted an external validation and only 19% of the logistic models presented the results in a clinically user-friendly wayPrognostic models are frequently published but they are developed from small samples of patients, their methodological quality is poor and they are rarely validated on external populations. Furthermore, they are not clinically practical as they are not presented to physicians in a user-friendly way. Finally because only a few are developed using populations from low and middle income countries, where most of trauma occurs, the generalizability to these setting is limited.Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Every year, an estimated 1.5 million people die and hundreds of millions require emergency treatment after a TBI. Fatality rates and disability rates vary depending on the severity and mechanisms of the TBI but unfavourable outcomes (death, vegetative state and severe disability) following TBI can be higher than 20%[1,2].Physicians routinely make diagnostic and therapeutic decisions based on the patient's prognosis. Furthermore, prognostic information is also important in the counselling of patients and relatives in this critical scenario. Nevertheless in gen