We explored the prognostic value of diffusion tensor imaging (DTI) parameters of selected white matter (WM) tracts in predicting neuropsychological outcome, both at baseline and 6 months later, among well-characterized patients diagnosed with mild traumatic brain injury (mTBI). Sixty-one patients with mTBI (mean age=27.08; standard deviation [SD], 8.55) underwent scanning at an average of 10 h (SD, 4.26) post-trauma along with assessment of their neuropsychological performance at an average of 4.35 h (SD, 7.08) upon full Glasgow Coma Scale recovery. Results were then compared to 19 healthy control participants (mean age=29.05; SD, 5.84), both in the acute stage and 6 months post-trauma. DTI and neuropsychological measures between acute and chronic phases were compared, and significant differences emerged. Specifically, chronic-phase fractional anisotropy and radial diffusivity values showed significant group differences in the corona radiata, anterior limb of internal capsule, cingulum, superior longitudinal fasciculus, optic radiation, and genu of corpus callosum. Findings also demonstrated associations between DTI indices and neuropsychological outcome across two time points. Our results provide new evidence for the use of DTI as an imaging biomarker and indicator of WM damage occurring in the context of mTBI, and they underscore the dynamic nature of brain injury and possible biological basis of chronic neurocognitive alterations.
The identification of effective pharmacotherapies for traumatic brain injury (TBI) remains a major challenge. Treatment with heparin and its derivatives is associated with neuroprotective effects after experimental TBI; however, the optimal dosage and method of administration, modes of action, and effects on hemorrhage remain unclear. Therefore, this review aimed to systematically evaluate, analyze, and summarize the available literature on the use of heparin and low molecular weight heparins (LMWHs) as treatment options for experimental TBI. We searched two online databases (PubMed and ISI Web of Science) to identify relevant studies. Data pertaining to TBI paradigm, animal subjects, drug administration, and all pathological and behavior outcomes were extracted. Eleven studies met our pre-specified inclusion criteria, and for outcomes with sufficient numbers, data from seven publications were analyzed in a weighted mean difference meta-analysis using a random-effects model. Study quality and risk of bias were also determined. Meta-analysis revealed that heparin and its derivatives decreased brain edema, leukocyte rolling, and vascular permeability, and improved neurological function. Further, treatment did not aggravate hemorrhage. These findings must be interpreted with caution, however, because they were determined from a limited number of studies with substantial heterogeneity. Also, overall study quality was low based on absences of data reporting, and potential publication bias was identified. Importantly, we found that there are insufficient data to evaluate the variables we had hoped to investigate. The beneficial effects of heparin and LMWHs, however, suggest that further pre-clinical studies are warranted.
Traumatic brain injury (TBI) is one the major causes of death and morbidity in developing countries, where depression is a common psychiatric condition among individuals with TBI. The objectives were to investigate the occurrence and severity of depression one-year post-TBI; the association between radiological findings and depression; and the risk factors. We report a cross-sectional study among adult patients who were hospitalized because of TBI in the past one year. A structured data collection form was used to collect patients' demographic data during TBI, while the Patient Health Questionnaire (PHQ)-9 questionnaire was administered to assess the level of depression at one-year post-TBI. Of the 309 patients in this study; 46.6%, 26.2%, and 27.2% had mild, moderate, and severe TBI, respectively. The overall rate of depression was 33.7%, where 22.3%, 8.7%, and 2.6% had mild, moderate, and moderately severe depression, respectively. There was a significant, positive correlation between severity of TBI and level of depression; rs (0.427), p
Abstract Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. The Pan-Asian Trauma Outcome Study registry was used in this study, and the data were prospectively collected from January 1, 2015, to December 31, 2020. Among a total of 6540 patients (≥ 15 years) with isolated moderate and severe TBI, 3276 (50.1%) patients were randomly included with stratification by outcomes and subgrouping variables for model evaluation, and 3264 (49.9%) patients were included for model training and validation. Logistic regression was considered as a baseline, and ML models were constructed and evaluated using the area under the precision-recall curve (AUPRC) as the primary outcome metric, area under the receiver operating characteristic curve (AUROC), and precision at fixed levels of recall. The contribution of the variables to the model prediction was measured using the SHapley Additive exPlanations (SHAP) method. The ML models outperformed logistic regression in predicting the in-hospital mortality. Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O2 saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.