原发性脑出血早期血肿扩大的影响因素及列线图预测模型

Influencing factors and the Nomogram model to predict early hematoma expansion of intracranial hemorrhage

目的探究原发性脑出血(sICH)患者发生早期血肿扩大(HE)的影响因素,构建预测模型并评估其预测效能。方法回顾性选取西部战区总医院2017年1月-2022年12月收治的sICH患者238例。按照HE超过33%或体积超过6 ml为标准,分为HE组(n=62)与血肿未扩大(NHE)组(n=176);比较两组患者的临床特征(临床症状、实验室检查结果)、CT平扫影像学表现(NCCT)、格拉斯哥昏迷量表(GCS)评分;采用多因素logistic回归分析筛选sICH患者发生HE的危险因素并建模预测HE的概率。采用R语言rms包构建预测sICH患者HE的列线图模型,并构建相关的临床模型、NCCT模型、GCS模型,采用受试者工作特征(ROC)曲线下面积(AUC)分析各模型对sICH患者发生HE的预测效能,采用准确度、敏感度、特异度、约登指数等评估各模型的临床应用价值,采用Delong检验分析各模型预测值的差异。结果HE组与NHE组sICH患者的卫星征、旋涡征、抗凝药物治疗史比较差异有统计学意义(P<0.05)。多因素logistic回归分析结果显示,sICH患者发生HE的危险因素包括首次CT检查时间、均匀度、抗凝药物治疗史、体积、最大径、低密度征、岛征、卫星征及旋涡征(P<0.05)。构建的临床模型、NCCT模型、GCS模型和列线图模型预测sICH患者发生HE的AUC分别为0.672、0.706、0.518和0.754,列线图模型预测的准确度、敏感度、约登指数、AUC值均高于临床模型及NCCT模型。结论首次CT检查时间、均匀度、抗凝药物治疗史、体积、最大径、低密度征、岛征、卫星征及旋涡征是预测sICH患者发生早期HE的独立影响因素;基于以上参数构建的列线图模型预测HE发生的效能较高,具有一定的临床应用前景。

Objective To investigate factors influencing the occurrence of early haematoma expansion(HE)in patients with spontaneous intracerebral hemorrhage(sICH),to develop a predictive model and evaluate its predictive efficacy.Methods A retrospective cohort of 238 patients with sICH,admitted to General Hospital of Western Theater Command between January 2017 and December 2022,was analyzed.Patients were categorized into two groups based on the criteria of HE exceeding 33%in relative volume or 6 ml in absolute volume:HE group(n=62)and non-haematoma expansion(NHE)group(n=176).Clinical characteristics,laboratory findings,Non-contrast Computed Tomography(NCCT)imaging,and Glasgow Coma Scale(GCS)scores were compared between the two groups.Multifactorial logistic regression analysis was employed to identify risk factors for HE and to model the probability of its occurrence.The R language rms package was utilized to construct a nomogram model for predicting HE in sICH patients,Additionally,the related clinical,NCCT,and GCS models were constructed.The predictive efficacy of each model for HE in sICH patients was evaluated using area under Receive Operative Characteristic(ROC)curve(AUC),and the clinical application value of each model was assessed using accuracy,sensitivity,specificity,and Jordon's index.The Delong test was applied to analyze differences in the predictive values of the models.Results Significant differences in satellite sign,vortex sign,and history of anticoagulant treatment were observed between two groups(P<0.05).Multifactorial logistic regression analysis revealed independent risk factors for HE in sICH patients,including the first CT examination time,homogeneity,history of anticoagulant medication,volume,maximal diameter,hypodensity sign,island sign,satellite sign,and vortex sign(P<0.05).The AUCs for the constructed clinical model,NCCT model,GCS model and nomogram model in predicting the occurrence of HE in sICH patients were 0.672,0.706,0.518 and 0.754,respectively.The nomogram model demonstrated higher accuracy,sensitivity,Jordon's index and AUC compared with those in the clinical and NCTT models.Conclusions The first CT examination time,homogeneity,history of anticoagulant treatment,volume,maximum diameter,hypodensity sign,island sign,satellite sign,and vortex sign are independent predictors of early HE in sICH patients.The nomogram model,constructed with the above parameters,demonstrated high predictive efficacy for HE and holds potential for clinical application.