RESUMO
Extravascular leakage on computed tomography (CT) angiography in patients with traumatic brain injury (TBI) is associated with hematoma expansion, functional prognosis, subsequent surgery, and death. Fresh frozen plasma (FFP) administration is often necessary to treat coagulation disorders associated with TBI. This study aimed to determine the relationship between the presence of extravascular leakage on contrast-enhanced head CT, fibrinogen level at admission, and FFP administration in patients with TBI. The medical records of patients with TBI ≥18 years of age referred to our hospital between January 2010 and December 2020 were examined retrospectively. Patients who underwent contrast-enhanced CT immediately after admission were selected, and the presence or absence of extravascular leakage, fibrinogen level at admission, and percentage of patients who required FFP administration within 24 h of admission were examined; 172 patients were included. Multi-variable linear regression analysis was performed to determine the effects of contrast extravasation on fibrinogen levels at admission and was adjusted for age, sex, systolic blood pressure, time from injury to admission, Marshall CT score, Glasgow Coma Scale score at admission, Injury Severity Score, and need for emergency surgery; the regression coefficient was -19.8. The effect of extravasation on FFP administration within 24 h of admission was analyzed using logistic regression while adjusting for age, systolic blood pressure, Marshall CT score, need for emergency surgery, and fibrinogen level at admission. The odds ratio of contrast extravasation was 7.08 after adjustment. Extravascular leakage is associated with fibrinogen levels at admission and FFP administration within 24 h of admission.
RESUMO
AIM: Treatment of severe traumatic brain injury is aided by better prediction of outcomes. The purpose of the present study was to develop and validate a prediction model using retrospective analysis of prospectively collected clinical data from two tertiary critical care medical centers in Japan. METHODS: Data were collected from 253 patients with a Glasgow Coma Scale score of <9. Within 24 h of their admission, 15 factors possibly related to outcome were evaluated. The dataset was randomly split into training and validation datasets using the repeated random subsampling method. A logistic regression model was fitted to the training dataset and predictive accuracy was assessed using the validation data. RESULTS: The best model included the variables age, pupillary light reflex, extensive subarachnoid hemorrhage, intracranial pressure, and midline shift. The estimated area under the curve for the model development data was 0.957, with a 95% confidence interval of 0.926-0.987, and that for validation data was 0.947, with a 95% confidence interval of 0.909-0.980. CONCLUSION: Our predictive model was shown to have high predictive value. It will be useful for review of treatment, family counseling, and efficient allocation of resources for patients with severe traumatic brain injury.