Imaging of Head Trauma: Pearls and Pitfalls

Risk factors and predictive model of cerebral edema after road traffic accidents-related traumatic brain injury

PURPOSE

Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.

METHODS

This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q1, Q3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve.

RESULTS

According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval (CI): 2.08 - 25.42, p = 0.002), 2.85 (95% CI: 1.11 - 7.31, p = 0.030), 2.62 (95% CI: 1.12 - 6.13, p = 0.027), 2.44 (95% CI: 1.25 - 4.76, p = 0.009), and 1.5 (95% CI: 1.10 - 2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ2 = 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ2 = 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively.

CONCLUSION

Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

Prediction of clinical progression in nervous system diseases: plasma glial fibrillary acidic protein (GFAP)

Glial fibrillary acidic protein (GFAP), an intracellular type III intermediate filament protein, provides structural support and maintains the mechanical integrity of astrocytes. It is predominantly found in the astrocytes which are the most abundant subtypes of glial cells in the brain and spinal cord. As a marker protein of astrocytes, GFAP may exert a variety of physiological effects in neurological diseases. For example, previous published literatures showed that autoimmune GFAP astrocytopathy is an inflammatory disease of the central nervous system (CNS). Moreover, the studies of GFAP in brain tumors mainly focus on the predictive value of tumor volume. Furthermore, using biomarkers in the early setting will lead to a simplified and standardized way to estimate the poor outcome in traumatic brain injury (TBI) and ischemic stroke. Recently, observational studies revealed that cerebrospinal fluid (CSF) GFAP, as a valuable potential diagnostic biomarker for neurosyphilis, had a sensitivity of 76.60% and specificity of 85.56%. The reason plasma GFAP could serve as a promising biomarker for diagnosis and prediction of Alzheimer's disease (AD) is that it effectively distinguished AD dementia from multiple neurodegenerative diseases and predicted the individual risk of AD progression. In addition, GFAP can be helpful in differentiating relapsing-remitting multiple sclerosis (RRMS) versus progressive MS (PMS). This review article aims to provide an overview of GFAP in the prediction of clinical progression in neuroinflammation, brain tumors, TBI, ischemic stroke, genetic disorders, neurodegeneration and other diseases in the CNS and to explore the potential therapeutic methods.