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.
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.
Authors: Amanda R Rabinowitz; Xiaoqi Li; Stephen R McCauley; Elisabeth A Wilde; Amanda Barnes; Gerri Hanten; Donna Mendez; James J McCarthy; Harvey S Levin Journal: J Neurotrauma Date: 2015-07-17 Impact factor: 5.269
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Authors: Jessica Dennis; Aaron M Yengo-Kahn; Paul Kirby; Gary S Solomon; Nancy J Cox; Scott L Zuckerman Journal: J Neurotrauma Date: 2019-03-28 Impact factor: 5.269
Authors: L A Gaudet; L Eliyahu; J Beach; M Mrazik; G Cummings; D C Voaklander; B H Rowe Journal: Occup Med (Lond) Date: 2019-10-01 Impact factor: 1.611