创伤性脑损伤颅骨骨折与颅内出血CT特征及风险预测模型的多中心研究OA

Objective To investigate the association between different skull fracture sites and the occurrence and complexity of intracranial hemorrhage in patients with traumatic brain injury(TBI),and to develop CT-based risk prediction models for intracranial hemorrhage using initial head computed tomography(CT)findings.Methods In this multicenter retrospective study,4 700 TBI patients from five tertiary hospitals were enrolled.Based on the initial CT findings,patients were categorized into three groups:no intracranial hemorrhage(n=1 602),single intracranial hemorrhage(n=1 011),and multiple intracranial hemorrhages(n=2 087).Data on age,sex,scalp hematoma,midline shift,cerebral herniation,skull fracture sites,and intracranial hemorrhage types were recorded.Continuous variables were compared among groups using one-way analysis of variance(ANOVA),while categorical variables were compared using the chi-square test.Univariable analysis was used to assess the associations between skull fracture sites and various types of intracranial hemorrhage.Multivariable logistic regression was then performed to identify independent risk factors related to skull fracture sites for predicting the occurrence and complexity of intracranial hemorrhage.Prediction models for the presence/absence of intracranial hemorrhage and hemorrhage complexity were constructed accordingly,and model performance was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC),calibration curves,and decision curve analysis(DCA).Results Significant differences were observed among the three groups in age,sex,scalp hematoma,midline shift,cerebral herniation,and the distribution of skull fracture sites(all P<0.05).Cranial fractures were identified as risk factors for intracranial hemorrhage,with sphenoid fractures(OR=8.35)and temporal fractures(OR=6.93)showing the strongest associations.In the model predicting the presence or absence of intracranial hemorrhage,after incorporating clinical information and specific skull fracture sites,the model achieved an AUC of 0.895(95%CI:0.89-0.90),demonstrated good calibration,and DCA indicated a high net benefit within a threshold probability range of approximately 0.10-0.60.The prediction model for multiple intracranial hemorrhages had an AUC of 0.689(95%CI:0.67-0.71),showed acceptable calibration,and retained some clinical utility within certain threshold probability ranges.Furthermore,epidural hematoma(EDH)was significantly associated with temporal and sphenoid fractures,as well as high-risk combined cranial fracture patterns(all P<0.05).Conclusion Skull fracture sites are closely associated with the occurrence and complexity of intracranial hemorrhage in TBI patients.The risk prediction models for intracranial hemorrhage,constructed based on skull fracture sites and relevant clinical information,exhibit good discriminative ability and have certain clinical decision-making value,which may serve as a reference for early risk stratification of TBI patients in the emergency department.