基于非对比CT影像征象和临床因素构建的列线图模型可有效预测自发性脑出血患者血肿扩张  

The nomogram model based on non-contrast computed tomography signs and clinical factors can effectively predict hematoma expansion in patients with spontaneous intracerebral hemorrhage

作　　者：蒋明宽 马宜传 陈岩 徐加利 朱芸 董小辉 JIANG Mingkuan;MA Yichuan;CHEN Yan;XU Jiali;ZHU Yun;DONG Xiaohui(Department of Radiology,The First Affiliated Hospital of Bengbu Medical University,Bengbu 233004,China;Department of Neurosurgery,The First Affiliated Hospital of Bengbu Medical University,Bengbu 233004,China)

机构地区：[1]蚌埠医科大学第一附属医院放射科,安徽蚌埠233004 [2]蚌埠医科大学第一附属医院神经外科,安徽蚌埠233004

出　　处：《分子影像学杂志》2025年第3期323-329,共7页Journal of Molecular Imaging

基　　金：蚌埠医科大学自然科学重点项目(2023byzd052)。

摘　　要：目的 构建并评估一种利用非对比CT(NCCT)影像征象和临床因素预测自发性脑出血(sICH)患者血肿扩张(HE)的新预测模型。方法 回顾性分析2023年1月1日~2024年9月30日蚌埠医科大学第一附属医院收治的sICH患者,根据基线血肿容量增加是否>6 mL或33%分为HE组和血肿未扩张组。进行单因素和二元Logistic回归分析,以筛选出与HE发生显著相关的独立预测因子,在此基础上使用R语言构建列线图预测模型,并构建相关的临床模型和影像模型。通过ROC曲线、校准曲线和临床决策曲线评估列线图模型的预测性能。结果 发病-首检CT时间、糖尿病、血小板、低密度征、混合征、卫星征是HE发生的独立预测因素(P<0.05)。将以上因素建立列线图预测模型,其ROC曲线下面积为0.928,临床模型和影像模型分别为0.832和0.829,列线图模型的约登指数、敏感度和特异度整体上亦优于以上2种模型。校准曲线结果表明,列线图模型的预测概率与实际概率拟合度较好;决策曲线结果表明,该模型的阈值概率范围较宽。结论 基于NCCT影像征象和临床因素构建的列线图模型预测sICH患者HE发生的效能良好,优于单纯临床模型和影像模型,从而为患者HE的临床风险划分提供了一种有效且无创的工具。Objective To construct and evaluate a new model for predicting hematoma expansion(HE)in patients with spontaneous intracerebral hemorrhage(sICH)using non-contrast computed tomography(NCCT)signs and clinical factors.Methods A retrospective analysis was performed on the patients with sICH admitted to the First Affiliated Hospital of Bengbu Medical University from January 2023 to September 2024.HE and non-HE group were divided according to whether the baseline hematoma volume increase was>6 mL or 33%.Univariate and binary logistic regression analysis were performed to screen out the independent predictors significantly related to the occurrence of HE.On this basis,R language was used to construct a nomogram prediction model,and the relevant clinical model and imaging model were constructed.The predictive performance of the model was evaluated by ROC curve,calibration curve and clinical decision curve.Results The time from onset to first CT examination,diabetes,platelet,hypodensity sign,blend sign,and satellite sign were independent predictors of HE occurrence(P<0.05).The above factors were used to establish a nomogram prediction model,the area under the ROC curve of the nomogram model was 0.928,and the clinical model and imaging model were 0.832 and 0.829,respectively.The Youden index,sensitivity and specificity of the nomogram model were also better than those of the above two models.The calibration curve results showed that the predicted probability of the nomogram model fitted well with the actual probability,and the decision curve results showed that the threshold probability range of the model was wide.Conclusion The nomogram model based on NCCT signs and clinical factors has a good performance in predicting the occurrence of HE in sICH patients,which is superior to the clinical model and imaging model alone,thus providing an effective and non-invasive tool for clinical risk assessment of HE in patients.

关 键 词：自发性脑出血 血肿扩张 列线图模型 预测模型 CT 

分 类 号：R74[医药卫生—神经病学与精神病学]