Background: Severe traumatic brain injury (sTBI) in adults remains a leading cause of mortality and disability worldwide. Early identification of reliable predictors of outcome is crucial for risk stratification and ICU management. Disturbances of hemostasis and metabolic factors such as body mass index (BMI) have been proposed as potential prognostic markers, but evidence remains limited. Methods: We conducted a retrospective, multicenter study including 307 adult patients with sTBI (Glasgow Coma Scale ≤ 8) admitted to three tertiary intensive care units in Ukraine between September 2023 and July 2024. All patients underwent surgical evacuation of hematomas and decompressive craniotomy. Laboratory parameters (APTT, INR, fibrinogen, platelets, D-dimer) were collected within 12 h of admission. BMI was calculated from measured height and weight. Predictive modeling was performed using L1-regularized logistic regression and Random Forest algorithms. Class imbalance was addressed with SMOTE. Model performance was assessed by AUC, accuracy, calibration, and feature importance. Results: The 28-day all-cause mortality was 32.9%. Compared with survivors, non-survivors had significantly lower GCS scores and higher INR, D-dimer, and APTT values. Very high VIF values indicated severe multicollinearity between predictors. Classical logistic regression was not estimable due to perfect separation; therefore, regularized logistic regression and Random Forest were applied. Random Forest demonstrated higher performance (AUC 0.95, accuracy ≈ 90%) than logistic regression (AUC 0.77, accuracy 70.1%), although results must be interpreted cautiously given the small sample size and potential overfitting. Feature importance analysis identified increased BMI, prolonged APTT, and elevated D-dimer as leading predictors of mortality. Sensitivity analysis excluding BMI still yielded strong performance (AUC 0.91), confirming the prognostic value of coagulation markers and GCS. Conclusions: Mortality in adult sTBI patients was strongly associated with impaired hemostasis, obesity, and low neurological status at admission. Machine learning-based modeling demonstrated promising predictive accuracy but is exploratory in nature. Findings should be interpreted with caution due to retrospective design, severe multicollinearity, potential overfitting, and absence of external validation. Larger, prospective, multicenter studies are needed to confirm these results and improve early risk stratification in severe TBI.

Background/Objectives: Treatment of severe traumatic brain injury (TBI) remains a major challenge in neurocritical care. The functional state of the brain largely depends on the applied ventilation strategy. Many patients develop acute respiratory distress syndrome (ARDS), for which lung-protective ventilation is recommended. However, its effect on outcomes in severe TBI remains unclear. This study aimed to assess whether a lung-protective ventilation strategy improves short-term outcomes in patients with severe TBI complicated by ARDS. Methods: This multicenter retrospective study included patients with severe TBI and ARDS treated in three Ukrainian tertiary hospitals. Lung-protective ventilation was defined as the use of a low tidal volume and moderate positive end-expiratory pressure (PEEP). The primary endpoint was 28-day mortality; secondary endpoints included the Glasgow Coma Scale (GCS) score and intracranial pressure (ICP) on day 28. Univariate and multivariate logistic regression analyses identified factors associated with mortality. Results: Mortality did not depend on arterial PaO₂ (p = 0.173) but correlated with lower GCS (p < 0.001), reduced PaO₂/FiO₂ ratio (p < 0.001), higher tidal volume (p < 0.001), and lower PEEP (p < 0.001). Lung-protective ventilation reduced mortality from 78.6% to 31.4%. Conclusions: Lung-protective ventilation is safe and effective in severe TBI with ARDS, significantly improving short-term survival without compromising cerebral outcomes.