TY - JOUR T1 - Early prediction of favourable recovery 6 months after mild traumatic brain injury JF - Journal of Neurology, Neurosurgery & Psychiatry JO - J Neurol Neurosurg Psychiatry SP - 936 LP - 942 DO - 10.1136/jnnp.2007.131250 VL - 79 IS - 8 AU - M Stulemeijer AU - S van der Werf AU - G F Borm AU - P E Vos Y1 - 2008/08/01 UR - http://jnnp.bmj.com/content/79/8/936.abstract N2 - Background: Predicting outcome after mild traumatic brain injury (MTBI) is notoriously difficult. Although it is recognised that milder head injuries do not necessarily mean better outcomes, less is known about the factors that do enable early identification of patients who are likely to recover well. Objective: To develop and internally validate two prediction rules for identifying patients who have the highest chance for good 6 month recovery. Methods: A prospective cohort study was conducted among patients with MTBI admitted to the emergency department. Apart from MTBI severity indices, a range of pre-, peri- and early post-injury variables were considered as potential predictors, including emotional and physical functioning. Logistic regression modelling was used to predict the absence of postconcussional symptoms (PCS) and full return to work (RTW). Results: At follow-up, 64% of the 201 participating patients reported full recovery. Based on our prediction rules, patients without premorbid physical problems, low levels of PCS and post-traumatic stress early after injury had a 90% chance of remaining free of PCS. Patients with over 11 years of education, without nausea or vomiting on admission, with no additional extracranial injuries and only low levels of pain early after injury had a 90% chance of full RTW. The discriminative ability of the prediction models was satisfactory, with an area under the curve >0.70 after correction for optimism. Conclusions: Early identification of patients with MTBI who are likely to have good 6 month recovery was feasible on the basis of relatively simple prognostic models. A score chart was derived from the models to facilitate clinical application. ER -