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Coagulopathy development in traumatic brain injury (TBI) is among the significant complications that can negatively affect the clinical course and outcome of TBI patients. Timely identification of this complication is of utmost importance in the acute clinical setting. We reviewed TBI patients admitted to our trauma center from 2015 to 2021. Demographic data, mechanism of injury, findings on admission, imaging studies, procedures during hospitalization, and functional outcomes were gathered. INR with a cutoff of 1.3, platelet count less than 100 × 10⁹/L, or partial thromboplastin time greater than 40s were utilized as the markers of coagulopathy. A total of 4002 patients were included. Coagulopathy occurred in 38.1% of the patients. Age of the patients (Odds Ratio (OR) = 0.993, 95% Confidence Interval (CI) = 0.986–0.999, p = 0.028), systolic blood pressure (OR = 0.993, 95% CI = 0.989–0.998, p = 0.005), fibrinogen level (OR = 0.998, 95% CI = 0.996–0.999, p < 0.001), and hemoglobin level (OR = 0.886, 95% CI = 0.839–0.936, p < 0.001) were independently associated with coagulopathy. Furthermore, coagulopathy was independently associated with higher mortality rates and longer ICU stays. Coagulopathy had the most substantial effect on mortality of TBI patients (OR = 2.6, 95% CI = 2.1–3.3, p < 0.001), compared to other admission clinical characteristics independently associated with mortality such as fixed pupillary light reflex (OR = 1.8, 95% CI = 1.5–2.4, p < 0.001), GCS (OR = 0.91, 95% CI = 0.88–0.94, p < 0.001), and hemoglobin level (OR = 0.93, 95% CI = 0.88–0.98, p = 0.004). Early coagulopathy in TBI patients can lead to higher mortality rates. Future studies are needed to prove that early detection and correction of coagulopathy and modifiable risk factors may help improve outcomes of TBI patients. [ABSTRACT FROM AUTHOR]

In patients affected by traumatic brain injury (TBI), hypofibrinogenemia within the initial hours of trauma can be expected due to vascular and inflammatory changes. In this study, we aimed to evaluate the effect of hypofibrinogenemia on the in-hospital mortality and 6-month functional outcomes of TBI patients, admitted to Rajaee Hospital, a referral trauma center in Shiraz, Iran. This study included all TBI patients admitted to our center who had no prior history of coagulopathy or any systemic disease, were alive on arrival, and had not received any blood product before admission. On admission, hospitalization, imaging, and 6-month follow-up information of included patients were extracted from the TBI registry database. The baseline characteristics of patients with fibrinogen levels of less than 150 mg/dL were compared with the cases with higher levels. To assess the effect of low fibrinogen levels on in-hospital mortality, a uni- and multivariate was conducted between those who died in hospital and survivors. Based on the 6-month GOSE score of patients, those with GOSE < 4 (unfavorable outcome) were compared with those with a favorable outcome. A total of 3049 patients (84.3% male, 15.7% female), with a mean age of 39.25 ± 18.87, met the eligibility criteria of this study. 494 patients had fibrinogen levels < 150 mg/dl, who were mostly younger and had lower average GCS scores in comparison to cases with higher fibrinogen levels. By comparison of the patients who died during hospitalization and survivors, it was shown that fibrinogen < 150 mg/dl is among the prognostic factors for in-hospital mortality (OR:1.75, CI: 1.32:2.34, P-value < 0.001), while the comparison between patients with the favorable and unfavorable functional outcome at 6-month follow-up, was not in favor of prognostic effect of low fibrinogen level (OR: 0.80, CI: 0.58: 1.11, P-value: 0.19). Hypofibrinogenemia is associated with in-hospital mortality of TBI patients, along with known factors such as higher age and lower initial GCS score. However, it is not among the prognostic factors of midterm functional outcome. [ABSTRACT FROM AUTHOR]

Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term. [ABSTRACT FROM AUTHOR]