探究基于CT增强的点征及列线图模型对脑出血早期血肿扩大的预测价值  

To explore the predictive value of CT-enhancement-based spot sign and column line graph models in early hematoma enlargement of cerebral hemorrhage

作　　者：伍发 杨钰林 伍婷婷 蒋锐 叶礼婷 王鹏 黄菊 李建浩 杜飞舟 Wu Fa;Yang Yulin;Wu Tingting;Jiang Rui;Ye Liting;Wang Peng;Huang Ju;Li Jianhao;Du Feizhou(Radiology Diagnosis Department of Western Theater Command General Hospital of the People's Liberation Army,Chengdu 610083,China)

机构地区：[1]西部战区总医院放射诊断科,四川成都610083 [2]成都市第5人民医院超声科,四川成都611100 [3]西部战区总医院神经外科,四川成都610083 [4]自贡市第4人民医院手术室,四川自贡643000

出　　处：《中国急救医学》2024年第9期808-814,共7页Chinese Journal of Critical Care Medicine

基　　金：西部战区总医院助推基金(2019ZT09);四川省医学会(恒瑞)科研基金专项科研课题(2021HR75);西部战区总医院院管课题(2021-XZYG-C04,2021-XZYG-C05)。

摘　　要：目的探究点征及列线图模型预测自发性脑出血(sICH)患者早期血肿扩大的效能。方法收集中国人民解放军西部战区总医院2018年1月至2023年12月期间所有符合标准的210例sICH患者。依据血肿是否扩大超过33%或体积超过6 mL,将患者分为血肿扩大组和血肿非扩大组。通过对比分析两组患者的临床特征及CT影像学表现,运用多因素Logistic回归分析筛选影响血肿扩大的危险因素。借助R语言rms包,构建预测患者血肿扩大的列线图模型。采用曲线下面积(AUC)评估模型区分度,校准曲线评价模型校准度,决策曲线分析(DCA)评估模型临床有效性,并通过准确度、敏感度、特异度及约登指数等指标全面评估模型的预测效能。结果血肿扩大组患者66例,血肿非扩大组患者144例。点征、首次CT检查时间、低密度征、高血压史、均匀度、血肿异质性评分及液平等因素是预测早期血肿扩大的独立影响因素。临床模型AUC值0.680,准确度71.43%,敏感度40.91%,特异度88.89%,约登指数0.2980;点征预测模型AUC值0.838,准确度86.19%,敏感度77.27%,特异度90.28%,约登指数0.6755;列线图预测模型AUC值0.910,准确度87.14%,敏感度86.96%,特异度81.55%,约登指数0.6851。DeLong检验临床模型、点征模型与列线图模型差异有统计学意义。此外,列线图模型预测早期血肿扩大概率与实际发生概率具有较高拟合度。结论本研究构建的基于点征联合首次CT检查时间、低密度征、高血压史、均匀度、血肿异质性评分及液平等因素的列线图模型,在预测sICH患者早期血肿扩大方面具有显著价值,为临床决策提供了有力支持。Objective To deeply investigate the efficacy of spot sign and column line graph(nomogram)model in predicting early hematoma enlargement in the patients with spontaneous cerebral hemorrhage(sICH).Methods This study covered all 210 sICH patients who met the criteria from January 2018 to December 2023 in the Western Theater Command General Hospital of the People's Liberation Army.Based on whether the hematoma was enlarged more than 33%or had a volume of more than 6 mL,the patients were divided into hematoma enlargement group and hematoma non-enlargement group.By comparing and analyzing the clinical characteristics and CT imaging manifestations of patients in the two groups,the risk factors affecting hematoma enlargement were screened by using multivariate Logistic regression analysis.With the help of R language rms package,nomogram model for predicting hematoma enlargement in sICH patients was constructed.The area under curve(AUC)was used to evaluate the differentiation of the model,the calibration curve was used to evaluate the calibration of the model,the decision curve analysis(DCA)was used to evaluate the clinical validity of the model,and the predictive efficacy of the model was comprehensively evaluated by the indexes of accuracy,sensitivity,specificity and Jordon's index.Results There were 66 patients with hematoma enlargement and 144 patients with hematoma non-enlargement in this study.It was found that spot sign,the time of the first computed tomography(CT)examination,hypointensity sign,history of hypertension,homogeneity,hematoma heterogeneity score and fluid level were independent influencing factors in predicting early hematoma enlargement.The AUC value of clinical model was 0.680,with 71.43%accuracy,40.91%sensitivity,88.89%specificity and 0.2980 Jordon's index;the AUC value of spot sign prediction model was 0.838,with 86.19%accuracy,77.27%sensitivity,90.28%specificity and 0.6755 Jordon's index;and the AUC value of nomogram prediction model was 0.910,with 87.14%accuracy,86.96%sensitivity,81.55%specificit

关 键 词：自发性脑出血 血肿扩大 CT 点征 列线图模型 

分 类 号：R743.34[医药卫生—神经病学与精神病学] R816.1[医药卫生—临床医学]