Risk factors and prediction model for chronic subdural hematoma in elderly patients with mild traumatic brain injury

Objective To investigate the risk factors for chronic subdural hematoma (CSDH) in elderly patients with mild traumatic brain injury (TBI) and to establish a nomogram. Methods Retrospective analysis was conducted on clinical data from 302 elderly patients with mild TBI who were treated at Department of Neurosurgery, the Affiliated Hospital of Xuzhou Medical University from January 2018 to December 2024. According to CT/MRI results of the head from 3 weeks to 3 months after trauma, the patients were divided into CSDH and non-CSDH groups. Univariate and multivariate binary logistic regression analyses were performed to identify risk factors for the occurrence of CSDH in elderly patients with mild TBI. The R 4.4.3 software was used to construct the mogram, and its discriminative ability was evaluated by receiver operating characteristic (ROC) curves. The calibration of the model was assessed using calibration curves, and its clinical utility was evaluated using decision curve analysis. Results Univariate analysis revealed that age, relative cortical atrophy index (RCA), anticoagulant/antiplatelet therapy history, and BMI were associated with the occurrence of CSDH after trauma (P<0.05). Multivariate logistic regression analysis further identified anticoagulant/antiplatelet therapy, elevated RCA, increased BMI, and advanced age as independent risk factors for CSDH. The nomogram developed using these variables showed good discriminative ability (AUC=0.850) and calibration consistency (Hosmer-Lemeshow test P=0.469) in the training set. Decision curve analysis confirmed that the clinical decision threshold was between 20% and 40%, demonstrating a clear net benefit for the model. Conclusions The nomogram developed in this study provides a quantitative tool for risk stratification of CSDH in elderly patients with mild TBI. For high-risk populations with a risk of ≥20%, enhanced follow-up with head CT re-examinations should be prioritized to achieve risk stratification and precise management.