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Updated: May 6, 2026
Controlled Cortical Impact Model for Traumatic Brain Injury
Published on: August 5, 2014
A Nomogram Predicts the Risk Factors for Post-Traumatic Cerebral Infarction in Polytrauma Patients with Traumatic
Jianye Miao1, Xin Qian2, Zhenjun Miao1
1Department of Emergency, Affiliated Jiangbin Hospital, Jiangsu University, Zhenjiang, China.
Insights
Identifying risk factors for post-traumatic cerebral infarction (PTCI) in patients with traumatic brain injury (TBI) is vital. This study found cerebral hernia, basilar skull fracture, platelet-lymphocyte ratio, D-dimer, and albumin independently predict PTCI risk.
Area of Science:
- Neurology
- Trauma Surgery
- Critical Care Medicine
Background:
- Post-traumatic cerebral infarction (PTCI) is a severe complication in patients with traumatic brain injury (TBI) and polytrauma.
- Early identification of patients at high risk for PTCI is critical for timely intervention and improved outcomes.
Purpose of the Study:
- To identify independent risk factors for PTCI in polytrauma patients with TBI.
- To develop and validate a predictive model for PTCI risk.
Main Methods:
- Retrospective analysis of 511 polytrauma patients with TBI.
- Univariable, Lasso, and multivariable logistic regression to identify independent risk factors.
- Development and validation of a nomogram prediction model using ROC, calibration curves, and DCA.
Main Results:
- Independent risk factors for PTCI included cerebral hernia, basilar skull fracture, platelet-lymphocyte ratio (PLR), D-dimer, and albumin (all p < 0.05).
- The nomogram demonstrated strong predictive accuracy with an AUC of 0.90 in the prediction cohort and 0.87 in the validation cohort.
- The model showed excellent performance, discrimination, and clinical utility.
Conclusions:
- Cerebral hernia, basilar skull fracture, PLR, D-dimer, and albumin are significant independent risk factors for PTCI in TBI patients.
- The developed nomogram is a valuable tool for early identification of high-risk PTCI patients.
- This model can aid clinicians in risk stratification and timely management decisions.
Abstract:
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 p < 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 p < 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.
