A Nomogram Predicts the Risk Factors for Post-Traumatic Cerebral Infarction in Polytrauma Patients with Traumatic Brain Injury

Post-traumatic cerebral infarction (PTCI) is a significant complication in polytrauma patients with traumatic brain injury (TBI). Identifying high-risk patients for early intervention is crucial. This study aims to investigate the independent risk factors for PTCI in polytrauma patients with TBI to establish and validate a prediction model. A retrospective analysis was conducted on 511 patients with TBI and multiple injuries admitted between January 2016 and July 2023. The patients were divided into groups based on whether they developed PTCI. Independent risk factors for PTCI were identified using univariable, Lasso, and multivariable logistic regression analysis. A nomogram was established to predict the risk factors for PTCI. The receiver operating characteristic (ROC) area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to determine the predictive accuracy, discrimination, and clinical effectiveness of the nomogram, respectively. In addition, the Hosmer-Lemeshow test was used to assess the goodness-of-fit. Clinically significant associations were observed between PTCI and factors such as cerebral hernia, traumatic subarachnoid hemorrhage, basilar skull fracture, shock index, platelets, platelet-lymphocyte ratio (PLR), prothrombin time, international normalized ratio, D-dimer, albumin, injury severity score, and Glasgow coma score (all <i>p</i> < 0.05). These variables screened by Lasso regression were incorporated in multivariate logistic regression. They identified cerebral hernia, basilar skull fracture, PLR, D-dimer, and albumin as independent risk factors for PTCI (all <i>p</i> < 0.05). The analysis results were visually represented using a nomogram. The AUC of the prediction cohort was 0.9 [95% confidence interval (95% confidence intercal (CI)): 0.84, 0.97], and of the validation cohort was 0.87 (95% CI: 0.79, 0.96). The nomogram prediction model demonstrates excellent performance according to the ROC, calibration curve, and DCA, providing valuable insights for the early identification of high-risk PTCI patients.