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    老年轻度颅脑创伤患者发生慢性硬膜下血肿的危险因素和预测模型

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

    • 摘要: 目的 探讨老年轻度颅脑创伤患者发生慢性硬膜下血肿(CSDH)的危险因素,并构建列线图。方法 选取2018年1月—2024年12月于徐州医科大学附属医院神经外科治疗的302例轻度颅脑创伤患者,收集临床资料进行回顾性分析。根据患者在创伤后3周至3个月复查的头颅CT/MRI结果,将患者分成CSDH组和非CSDH组。通过单因素和多因素logistic回归分析,筛选老年轻度颅脑创伤患者发生CSDH的危险性因素。使用R 4.4.3软件构建列线图,并通过受试者工作特征(ROC)曲线评估模型的区分度,校准曲线评估模型的拟合度,临床决策曲线评价模型的临床效益。结果 单因素分析显示,年龄、相对皮质萎缩指数(RCA)、抗凝/抗血小板治疗史和BMI与创伤后CSDH的发生有关(P<0.05)。多因素logistic回归分析进一步发现,抗凝/抗血小板治疗、RCA升高、BMI升高及高龄是CSDH发生的独立危险因素。基于这些变量构建的列线图在训练集中表现出良好的区分度(AUC=0.850)和校准一致性(Hosmer-Lemeshow检验P=0.469)。临床决策曲线分析证实,临床决策阈值为20%~40%,该模型具有明确的净收益优势。结论 本研究构建的列线图预测模型为老年轻度颅脑外伤患者的CSDH风险分层提供了量化工具。对于风险≥20%的高风险人群,应加强头颅CT复查和随访,实现风险分层和精准管理。

       

      Abstract: 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.

       

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