ABSTRACT
OBJECTIVES: The Montreal Cognitive Assessement (MoCA) is a brief and standardized cognitive screening tool that has been used with several clinical populations. The aim of this study was to screen the early cognitive status of patients following mild traumatic brain injury (mTBI) with the MoCA. METHODS: The MoCA was administered within the first 2 weeks post-injury to 42 patients with uncomplicated mTBI, 92 patients with complicated mTBI and 50 healthy controls. RESULTS: Patients with complicated mTBI had a significantly lower performance (more impairments) on the total score of the MoCA than both the group with uncomplicated mTBI and the control group. Also, the group with uncomplicated mTBI had a significantly lower performance than controls. Moreover, age, education and TBI severity had a significant effect on the MoCA total score where younger, more educated and patients with less severe (higher GCS score) mTBI performed significantly better. CONCLUSIONS: The MoCA may be clinically useful to acutely screen cognition following mTBI.
ABSTRACT
OBJECTIVE: The goal of the current study is to explore the difference in acute post-concussive symptoms (PCS), headaches, sleep and mood complaints between groups of patients with complicated and uncomplicated mild traumatic brain injuries (mTBIs) and a comparable group of injured controls. Interactions among the following four factors were studied: presence of (1) PCS; (2) headaches; (3) sleep disorders; and (4) psychological status. METHODS: A total of 198 patients, followed at the outpatient mTBI clinic of the MUHC-MGH, completed questionnaires and a brief neurological assessment two weeks post-trauma. RESULTS: Whether they had a TBI or not, all patients presented PCS, headaches, sleep and mood complaints. No significant differences between groups in terms of reported symptoms were found. Variables such as depression and anxiety symptoms, as well as sleep difficulties and headaches were found to correlate with PCS. The high rate of PCS in trauma patients was observed independently of traumatic brain injury status. This study has also shown that patients with complicated mTBI were more likely to have vestibular impairment after their injury. CONCLUSION: The vestibular function should be assessed systematically after a complicated mTBI. Furthermore, the mTBI diagnosis should be based on operational criteria, and not on reported symptoms.
Subject(s)
Brain Injuries, Traumatic/complications , Headache/etiology , Mood Disorders/etiology , Post-Concussion Syndrome/etiology , Sleep Wake Disorders/etiology , Vestibular Diseases/etiology , Adolescent , Adult , Aged , Aged, 80 and over , Female , Headache/diagnosis , Humans , Male , Middle Aged , Mood Disorders/diagnosis , Neuropsychological Tests , Outcome Assessment, Health Care , Sleep Wake Disorders/diagnosis , Surveys and Questionnaires , Vestibular Diseases/diagnosis , Young AdultABSTRACT
BACKGROUND: Inconsistencies regarding the risk of developing Alzheimer disease after traumatic brain injury (TBI) remain in the literature. Indeed, why AD develops in certain TBI patients while others are unaffected is still unclear. OBJECTIVE: The aim of this study was to performed a systematic review to investigate whether certain variables related to TBI, such as TBI severity, loss of consciousness (LOC) and post-traumatic amnesia (PTA), are predictors of risk of AD in adults. METHODS: From 841 citations retrieved from MEDLINE via PubMed, EMBASE, PSYINFO and Cochrane Library databases, 18 studies were eligible for the review. RESULTS: The review revealed that about 55.5% of TBI patients may show deteriorated condition, from acute post-TBI cognitive deficits to then meeting diagnostic criteria for AD, but whether TBI is a risk factor for AD remains elusive. CONCLUSIONS: Failure to establish such a link may be related to methodological problems in the studies. To shed light on this dilemma, future studies should use a prospective design, define the types and severities of TBI and use standardized AD and TBI diagnostic criteria. Ultimately, an AD prediction model, based on several variables, would be useful for clinicians detecting TBI patients at risk of AD.
