Background::Despite advances in decompressive craniectomy (DC) for the treatment of traumatic brain injury (TBI), these patients are at risk of having a poor long-term prognosis. The aim of this study was to predict 1-year mortality in TBI patients undergoing DC using logistic regression and random tree models.Methods::This was a retrospective analysis of TBI patients undergoing DC from January 1, 2015, to April 25, 2019. Patient demographic characteristics, biochemical tests, and intraoperative factors were collected. One-year mortality prognostic models were developed using multivariate logistic regression and random tree algorithms. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were used to evaluate model performance.Results::Of the 230 patients, 70 (30.4%) died within 1 year. Older age (OR, 1.066; 95% CI, 1.045-1.087; n P < 0.001), higher Glasgow Coma Score (GCS) (OR, 0.737; 95% CI, 0.660-0.824; n P < 0.001), higher n D-dimer (OR, 1.005; 95% CI, 1.001-1.009; n P = 0.015), coagulopathy (OR, 2.965; 95% CI, 1.808-4.864; n P < 0.001), hypotension (OR, 3.862; 95% CI, 2.176-6.855; n P < 0.001), and completely effaced basal cisterns (OR, 3.766; 95% CI, 2.255-6.290; n P < 0.001) were independent predictors of 1-year mortality. Random forest demonstrated better performance for 1-year mortality prediction, which achieved an overall accuracy of 0.810, sensitivity of 0.833, specificity of 0.800, and AUC of 0.830 on the testing data compared to the logistic regression model.n Conclusions::The random forest model showed relatively good predictive performance for 1-year mortality in TBI patients undergoing DC. Further external tests are required to verify our prognostic model.“,”Background::Despite advances in decompressive craniectomy (DC) for the treatment of traumatic brain injury (TBI), these patients are at risk of having a poor long-term prognosis. The aim of this study was to predict 1-year mortality in TBI patients undergoing DC using logistic regression and random tree models.Methods::This was a retrospective analysis of TBI patients undergoing DC from January 1, 2015, to April 25, 2019. Patient demographic characteristics, biochemical tests, and intraoperative factors were collected. One-year mortality prognostic models were developed using multivariate logistic regression and random tree algorithms. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were used to evaluate model performance.Results::Of the 230 patients, 70 (30.4%) died within 1 year. Older age (OR, 1.066; 95% CI, 1.045-1.087; n P < 0.001), higher Glasgow Coma Score (GCS) (OR, 0.737; 95% CI, 0.660-0.824; n P < 0.001), higher n D-dimer (OR, 1.005; 95% CI, 1.001-1.009; n P = 0.015), coagulopathy (OR, 2.965; 95% CI, 1.808-4.864; n P < 0.001), hypotension (OR, 3.862; 95% CI, 2.176-6.855; n P < 0.001), and completely effaced basal cisterns (OR, 3.766; 95% CI, 2.255-6.290; n P < 0.001) were independent predictors of 1-year mortality. Random forest demonstrated better performance for 1-year mortality prediction, which achieved an overall accuracy of 0.810, sensitivity of 0.833, specificity of 0.800, and AUC of 0.830 on the testing data compared to the logistic regression model.n Conclusions::The random forest model showed relatively good predictive performance for 1-year mortality in TBI patients undergoing DC. Further external tests are required to verify our prognostic model.