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Article(id=1198619427659936313, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198619422425448948, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.0855.2023.1206, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1687104000000, receivedDateStr=2023-06-19, revisedDate=null, revisedDateStr=null, acceptedDate=1692028800000, acceptedDateStr=2023-08-15, onlineDate=1763702740825, onlineDateStr=2025-11-21, pubDate=1716825600000, pubDateStr=2024-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763702740825, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763702740825, creator=13701087609, updateTime=1763702740825, updator=13701087609, issue=Issue{id=1198619422425448948, tenantId=1146029695717560320, journalId=1189873630562394117, year='2024', volume='49', issue='5', pageStart='489', pageEnd='610', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1763702739578, creator=13701087609, updateTime=1763702927730, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1198620211667628088, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198619422425448948, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1198620211667628089, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198619422425448948, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=504, endPage=510, ext={EN=ArticleExt(id=1198619428041617995, articleId=1198619427659936313, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Influencing factors and the Nomogram model to predict early hematoma expansion of intracranial hemorrhage, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

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.

, correspAuthors=Jian-Hao Li, authorNote=null, correspAuthorsNote=
E-mail:
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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。

, correspAuthors=李建浩, authorNote=null, correspAuthorsNote=
李建浩，E-mail：
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伍发，医学硕士，主治医师，主要从事神经系统与骨骼肌肉相关疾病方面的研究

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伍发，医学硕士，主治医师，主要从事神经系统与骨骼肌肉相关疾病方面的研究

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伍发，医学硕士，主治医师，主要从事神经系统与骨骼肌肉相关疾病方面的研究

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Medicine (Baltimore), 2020, 99(50): e23557., articleTitle=A prognostic nomogram incorporating red cell distribution width for patients with intracerebral hemorrhage, refAbstract=null), Reference(id=1198619441035571222, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, doi=null, pmid=null, pmcid=null, year=2022, volume=30, issue=7, pageStart=117, pageEnd=121, url=null, language=null, rfNumber=[20], rfOrder=19, authorNames=伍发, 王鹏, 蒋锐, journalName=实用心脑肺血管病杂志, refType=null, unstructuredReference=伍发, 王鹏, 蒋锐, 等. 影像组学特征对自发性脑出血患者发生血肿扩大的预测价值[J]. 实用心脑肺血管病杂志, 2022, 30(7): 117-121., articleTitle=影像组学特征对自发性脑出血患者发生血肿扩大的预测价值, refAbstract=null), Reference(id=1198619441115263001, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, doi=null, pmid=null, pmcid=null, year=2021, volume=6, issue=4, pageStart=610, pageEnd=614, url=null, language=null, rfNumber=[21], rfOrder=20, authorNames=Zhong JW, Jin YJ, Song ZJ, journalName=Stroke Vasc Neurol, refType=null, unstructuredReference=Zhong JW, Jin YJ, Song ZJ, et al. Deep learning for automatically predicting early haematoma expansion in Chinese patients[J]. 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figureFileSmall=ZW9J35aZPdw5SKNWasOcbA==, figureFileBig=8D7LYOIpCieu4e2za7ausA==, tableContent=null), ArticleFig(id=1198619435905938326, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=CN, label=图1, caption=预测sICH患者HE发生风险的列线图模型

sICH. 原发性脑出血；HE. 血肿扩大

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sICH. 原发性脑出血；HE. 血肿扩大

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sICH. 原发性脑出血；HE. 血肿扩大

, figureFileSmall=JBHNt67z3VMl/K8igz2fPA==, figureFileBig=sqAGJ7MdOviGTBekI9UvmQ==, tableContent=null), ArticleFig(id=1198619436522501039, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=EN, label=Fig.4, caption=The ROC curves of prediction of HE in sICH patients by the models, figureFileSmall=DXnj7yY4X4q74FAUOXQunQ==, figureFileBig=37RKmhqcOeYERQjDDchwtA==, tableContent=null), ArticleFig(id=1198619436627358641, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=CN, label=图4, caption=各模型对sICH患者发生HE预测价值的ROC曲线

sICH. 原发性脑出血；HE. 血肿扩大；NCCT. CT平扫描影像学表现；GCS. 格拉斯哥昏迷量表

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Comparison of clinical data between two groups of sICH patients

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 HE组(n=62) NHE组(n=176) χ2/t/Z P
性别[例(%)] 0.528 0.467
男 45(72.6) 119(67.6)
女 17(27.4) 57(32.4)
年龄(岁, $\bar{x}±s$) 60.1±12.1 59.7±13.3 0.199 0.281
首次CT检查时间[h, M(Q1, Q3)] 5.0(4.1, 11.0) 6.0(4.0, 16.0) 1.122 0.251
复查CT检查时间(h, $\bar{x}±s$) 13.210±8.727 15.010±7.886 -1.504 0.079
出血速度[ml/h, M(Q1, Q3)] 2.636(0.757, 5.894) 1.861(0.586, 5.313) -0.397 0.991
出血体积[ml, M(Q1, Q3)] 15.756(6.640, 29.468) 10.442(6.156, 34.270) -0.378 0.948
收缩压(mmHg, $\bar{x}±s$) 159.770±26.726 161.880±27.481 -0.521 0.735
舒张压(mmHg, $\bar{x}±s$) 92.390±16.318 92.810±17.591 -0.167 0.725
平均CT值(HU, $\bar{x}±s$) 64.556±5.887 63.616±6.7613 0.972 0.420
最大径(cm, $\bar{x}±s$) 3.970±1.591 3.708±1.471 1.182 0.333
血糖(mmol/L, $\bar{x}±s$) 7.557±2.540 7.151±2.526 1.086 0.405
谷丙转氨酶[U/L, M(Q1, Q3)] 22.350(14.775, 31.275) 20.150(15.008, 30.400) 0.120 0.272
位置[例(%)] 3.763 0.152
基底节区 38(61.3) 128(72.7)
脑叶 18(29.0) 31(17.6)
其他 6(9.7) 17(9.7)
形态[例(%)] 0.971 0.615
不规则 29(46.8) 95(54.4)
类圆形 30(48.4) 73(41.5)
铸型 3(4.8) 8(4.5)
高血压[例(%)] 0.877 0.349
无 17(27.4) 38(21.6)
有 45(72.6) 138(78.4)
均匀度[例(%)] 0.892 0.345
不均匀 30(48.4) 73(41.5)
均匀 32(51.6) 103(58.5)
低密度征[例(%)] 2.620 0.106
无 25(40.3) 92(52.3)
有 37(59.7) 84(47.7)
卫星征[例(%)] 7.468 0.006
无 26(41.9) 109(61.9)
有 36(58.1) 67(38.1)
旋涡征[例(%)] 5.649 0.017
无 24(38.7) 99(56.3)
有 38(61.3) 77(43.8)
岛征[例(%)] 0.013 0.909
无 48(77.4) 135(76.7)
有 14(22.6) 41(23.3)
黑洞征[例(%)] 1.144 0.285
无 50(80.6) 130(73.9)
有 12(24.4) 46(26.1)
混合征[例(%)] 0.764 0.382
无 35(56.5) 88(50.0)
有 27(43.5) 88(50.0)
吸烟史[例(%)] 0.048 0.826
无 36(58.1) 105(59.7)
有 26(41.9) 71(40.3)
饮酒史[例(%)] 0.037 0.847
无 34(54.8) 99(56.3)
有 28(45.2) 77(43.7)
抗凝史[例(%)] 3.056 0.080
无 59(95.2) 174(98.9)
有 3(4.8) 2(1.1)
凝血障碍史[例(%)] 0.447 0.504
无 51(82.3) 151(85.8)
有 11(17.7) 25(14.2)
GCS评分[分, 例(%)] 1.526 0.466
3~8 13(7.4) 5(8.1)
9~12 48(27.3) 12(19.4)
13~15 115(65.3) 45(72.6)
), ArticleFig(id=1198619436837073844, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=CN, label=表1, caption=

两组sICH患者临床资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 HE组(n=62) NHE组(n=176) χ2/t/Z P
性别[例(%)] 0.528 0.467
男 45(72.6) 119(67.6)
女 17(27.4) 57(32.4)
年龄(岁, $\bar{x}±s$) 60.1±12.1 59.7±13.3 0.199 0.281
首次CT检查时间[h, M(Q1, Q3)] 5.0(4.1, 11.0) 6.0(4.0, 16.0) 1.122 0.251
复查CT检查时间(h, $\bar{x}±s$) 13.210±8.727 15.010±7.886 -1.504 0.079
出血速度[ml/h, M(Q1, Q3)] 2.636(0.757, 5.894) 1.861(0.586, 5.313) -0.397 0.991
出血体积[ml, M(Q1, Q3)] 15.756(6.640, 29.468) 10.442(6.156, 34.270) -0.378 0.948
收缩压(mmHg, $\bar{x}±s$) 159.770±26.726 161.880±27.481 -0.521 0.735
舒张压(mmHg, $\bar{x}±s$) 92.390±16.318 92.810±17.591 -0.167 0.725
平均CT值(HU, $\bar{x}±s$) 64.556±5.887 63.616±6.7613 0.972 0.420
最大径(cm, $\bar{x}±s$) 3.970±1.591 3.708±1.471 1.182 0.333
血糖(mmol/L, $\bar{x}±s$) 7.557±2.540 7.151±2.526 1.086 0.405
谷丙转氨酶[U/L, M(Q1, Q3)] 22.350(14.775, 31.275) 20.150(15.008, 30.400) 0.120 0.272
位置[例(%)] 3.763 0.152
基底节区 38(61.3) 128(72.7)
脑叶 18(29.0) 31(17.6)
其他 6(9.7) 17(9.7)
形态[例(%)] 0.971 0.615
不规则 29(46.8) 95(54.4)
类圆形 30(48.4) 73(41.5)
铸型 3(4.8) 8(4.5)
高血压[例(%)] 0.877 0.349
无 17(27.4) 38(21.6)
有 45(72.6) 138(78.4)
均匀度[例(%)] 0.892 0.345
不均匀 30(48.4) 73(41.5)
均匀 32(51.6) 103(58.5)
低密度征[例(%)] 2.620 0.106
无 25(40.3) 92(52.3)
有 37(59.7) 84(47.7)
卫星征[例(%)] 7.468 0.006
无 26(41.9) 109(61.9)
有 36(58.1) 67(38.1)
旋涡征[例(%)] 5.649 0.017
无 24(38.7) 99(56.3)
有 38(61.3) 77(43.8)
岛征[例(%)] 0.013 0.909
无 48(77.4) 135(76.7)
有 14(22.6) 41(23.3)
黑洞征[例(%)] 1.144 0.285
无 50(80.6) 130(73.9)
有 12(24.4) 46(26.1)
混合征[例(%)] 0.764 0.382
无 35(56.5) 88(50.0)
有 27(43.5) 88(50.0)
吸烟史[例(%)] 0.048 0.826
无 36(58.1) 105(59.7)
有 26(41.9) 71(40.3)
饮酒史[例(%)] 0.037 0.847
无 34(54.8) 99(56.3)
有 28(45.2) 77(43.7)
抗凝史[例(%)] 3.056 0.080
无 59(95.2) 174(98.9)
有 3(4.8) 2(1.1)
凝血障碍史[例(%)] 0.447 0.504
无 51(82.3) 151(85.8)
有 11(17.7) 25(14.2)
GCS评分[分, 例(%)] 1.526 0.466
3~8 13(7.4) 5(8.1)
9~12 48(27.3) 12(19.4)
13~15 115(65.3) 45(72.6)
), ArticleFig(id=1198619436912571317, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=EN, label=Tab.2, caption=

Multivariate logistic regression analysis on sICH patients between two groups

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变量 Wald χ2 P OR(95%CI)
年龄 0.064 0.801 0.996(0.968~1.026)
谷丙转氨酶 0.080 0.777 0.997(0.975~1.019)
首次CT检查时间 7.446 0.006 1.043(1.012~1.076)
平均CT值 0.022 0.883 1.005(0.942~1.072)
低密度征 7.883 0.005 4.403(1.564~12.392)
岛征 4.804 0.028 0.277(0.088~0.873)
GCS评分 0.008 0.931 0.993(0.850~1.161)
高血压史 0.900 0.343 0.653(0.270~1.577)
黑洞征 1.753 0.185 0.520(0.197~1.369)
混合征 3.548 0.060 0.338(0.109~1.045)
均匀度 3.960 0.047 0.322(0.105~0.983)
抗凝药物治疗史 4.757 0.029 12.134(1.288~114.343)
血肿异质性分数 0.203 0.652 0.921(0.643~1.319)
凝血功能障碍 0.028 0.868 0.912(0.305~2.721)
破入脑室 0.012 0.914 1.051(0.431~2.561)
出血速度 0.013 0.908 1.000(1.000~1.000)
性别 2.040 0.153 0.483(0.178~1.311)
水肿程度 0.145 0.703 1.173(0.516~2.670)
收缩压 0.226 0.635 0.995(0.975~1.015)
舒张压 0.930 0.335 1.014(0.985~1.044)
体积 5.824 0.016 1.000(1.000~1.000)
最大径 8.015 0.005 1.068(1.021~1.118)
位置 2.153 0.142 1.476(0.877~2.484)
卫星征 13.039 0.000 5.445(2.170~13.662)
形态 0.574 0.449 1.349(0.622~2.923)
血糖 0.899 0.343 1.071(0.930~1.234)
旋涡征 9.623 0.002 5.330(1.852~15.343)
吸烟史 0.664 0.415 1.482(0.576~3.816)
饮酒史 0.880 0.348 0.641(0.254~1.622)
液平 0.322 0.570 1.879(0.213~16.589)
中线移位 0.009 0.925 0.960(0.416~2.216)
常量 2.049 0.152 4.985(0.744~0.849)
), ArticleFig(id=1198619436992263096, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=CN, label=表2, caption=

两组sICH患者多因素logistic回归分析结果

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变量 Wald χ2 P OR(95%CI)
年龄 0.064 0.801 0.996(0.968~1.026)
谷丙转氨酶 0.080 0.777 0.997(0.975~1.019)
首次CT检查时间 7.446 0.006 1.043(1.012~1.076)
平均CT值 0.022 0.883 1.005(0.942~1.072)
低密度征 7.883 0.005 4.403(1.564~12.392)
岛征 4.804 0.028 0.277(0.088~0.873)
GCS评分 0.008 0.931 0.993(0.850~1.161)
高血压史 0.900 0.343 0.653(0.270~1.577)
黑洞征 1.753 0.185 0.520(0.197~1.369)
混合征 3.548 0.060 0.338(0.109~1.045)
均匀度 3.960 0.047 0.322(0.105~0.983)
抗凝药物治疗史 4.757 0.029 12.134(1.288~114.343)
血肿异质性分数 0.203 0.652 0.921(0.643~1.319)
凝血功能障碍 0.028 0.868 0.912(0.305~2.721)
破入脑室 0.012 0.914 1.051(0.431~2.561)
出血速度 0.013 0.908 1.000(1.000~1.000)
性别 2.040 0.153 0.483(0.178~1.311)
水肿程度 0.145 0.703 1.173(0.516~2.670)
收缩压 0.226 0.635 0.995(0.975~1.015)
舒张压 0.930 0.335 1.014(0.985~1.044)
体积 5.824 0.016 1.000(1.000~1.000)
最大径 8.015 0.005 1.068(1.021~1.118)
位置 2.153 0.142 1.476(0.877~2.484)
卫星征 13.039 0.000 5.445(2.170~13.662)
形态 0.574 0.449 1.349(0.622~2.923)
血糖 0.899 0.343 1.071(0.930~1.234)
旋涡征 9.623 0.002 5.330(1.852~15.343)
吸烟史 0.664 0.415 1.482(0.576~3.816)
饮酒史 0.880 0.348 0.641(0.254~1.622)
液平 0.322 0.570 1.879(0.213~16.589)
中线移位 0.009 0.925 0.960(0.416~2.216)
常量 2.049 0.152 4.985(0.744~0.849)
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Predictive performance of the nomogram model, the clinical model, and the imaging model in prediction of HE in sICH patients

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变量 准确度(%) 敏感度(%) 特异度(%) 约登指数 标准误差 P AUC 95%CI
列线图模型 78.15 77.42 63.07 0.4049 0.0349 <0.0001 0.754 0.686~0.822
临床模型 74.37 56.45 72.73 0.2918 0.0395 <0.0001 0.672 0.608~0.731
NCCT模型 77.73 69.35 63.64 0.3299 0.0406 <0.0001 0.706 0.644~0.763
GCS模型 73.59 70.97 34.66 0.0563 0.0433 0.663 0.518 0.453~0.583
), ArticleFig(id=1198619438275720123, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=CN, label=表3, caption=

列线图模型、临床模型、NCCT模型及GCS模型预测sICH患者发生HE的效能比较

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变量 准确度(%) 敏感度(%) 特异度(%) 约登指数 标准误差 P AUC 95%CI
列线图模型 78.15 77.42 63.07 0.4049 0.0349 <0.0001 0.754 0.686~0.822
临床模型 74.37 56.45 72.73 0.2918 0.0395 <0.0001 0.672 0.608~0.731
NCCT模型 77.73 69.35 63.64 0.3299 0.0406 <0.0001 0.706 0.644~0.763
GCS模型 73.59 70.97 34.66 0.0563 0.0433 0.663 0.518 0.453~0.583
), ArticleFig(id=1198619438435103680, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198619427659936313, language=EN, label=Tab.4, caption=

There results of difference test of the models (Delong test)

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模型 NCCT模型 列线图模型 GCS模型
临床模型 0.5874 0.0800 0.0143*
NCCT模型 0.1200 0.0026*
列线图模型 <0.0001*
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各HE预测模型的差异性检验结果(Delong检验)

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模型 NCCT模型 列线图模型 GCS模型
临床模型 0.5874 0.0800 0.0143*
NCCT模型 0.1200 0.0026*
列线图模型 <0.0001*
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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
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伍发 1 , 杨钰林 2 , 伍婷婷 3 , 蒋锐 1 , 吴杰 1 , 王鹏 1 , 杜飞舟 1 , 于红梅 1 , 李建浩 1, *
解放军医学杂志 | 临床研究 2024,49(5): 504-510
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解放军医学杂志 | 临床研究 2024, 49(5): 504-510
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
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伍发1, 杨钰林2, 伍婷婷3, 蒋锐1, 吴杰1, 王鹏1, 杜飞舟1, 于红梅1, 李建浩1, *
作者信息
  • 1解放军西部战区总医院放射诊断科，四川成都 610083
  • 2成都市第五人民医院超声科，四川成都 611100
  • 3解放军西部战区总医院神经外科，四川成都 610083

    • 伍发，医学硕士，主治医师，主要从事神经系统与骨骼肌肉相关疾病方面的研究

通讯作者:

李建浩，E-mail：
Influencing factors and the Nomogram model to predict early hematoma expansion of intracranial hemorrhage
Fa Wu1, Yu-Lin Yang2, Ting-Ting Wu3, Rui Jiang1, Jie Wu1, Peng Wang1, Fei-Zhou Du1, Hong-Mei Yu1, Jian-Hao Li1, *
Affiliations
  • 1Department of Diagnostic Radiology, General Hospital of Western Theater Command of PLA, Chengdu, Sichuan 610083, China
  • 2Department of Ultrasound, Chengdu 5th People's Hospital, Chengdu, Sichuan 611100, China
  • 3Department of Neurosurgery, General Hospital of Western Theater Command of PLA, Chengdu, Sichuan 610083, China
出版时间: 2024-05-28 doi: 10.11855/j.issn.0577-7402.0855.2023.1206
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摘要
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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。

自发性脑出血  /  血肿扩大  /  列线图模型  /  预测  /  CT

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.

spontaneous cerebral hemorrhage  /  hematoma expansion  /  nomogram model  /  prediction  /  CT
伍发, 杨钰林, 伍婷婷, 蒋锐, 吴杰, 王鹏, 杜飞舟, 于红梅, 李建浩. 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. 解放军医学杂志, 2024 , 49 (5) : 504 -510 . DOI: 10.11855/j.issn.0577-7402.0855.2023.1206
Fa Wu, Yu-Lin Yang, Ting-Ting Wu, Rui Jiang, Jie Wu, Peng Wang, Fei-Zhou Du, Hong-Mei Yu, Jian-Hao Li. Influencing factors and the Nomogram model to predict early hematoma expansion of intracranial hemorrhage[J]. Medical Journal of Chinese People’s Liberation Army, 2024 , 49 (5) : 504 -510 . DOI: 10.11855/j.issn.0577-7402.0855.2023.1206
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原发性脑出血(primary intracerebral hemorrhage，sICH)，又称自发性脑出血或高血压脑出血，致残率及病死率均较高，且患者常在发病后6 h内出现早期血肿扩大(hematoma enlargement，HE)[1-2]。研究显示，HE是sICH患者预后不良及神经功能恶化的独立危险因素[3-4]。因此，早期预测HE，对于改善sICH的预后至关重要。目前HE的预测难度较大，以往研究提示，部分临床指标及基于CT平扫影像学特征可预测HE，如格拉斯哥昏迷量表(Glasgow coma scale，GCS)评分、高血糖、高血压、初次CT扫描时间、基线脑出血体积、CT显示的血肿形态及密度、脑室内出血、抗凝药物服用史、岛征、卫星征及漩涡征等，但这些临床指标、非增强CT征象或sICH评分的预测效度和临床相关性尚未在大型前瞻性研究中得到充分验证[1]。由此可见，上述指标对预测HE有一定的价值，未来研究的重要目标之一是进一步完善临床指标及非增强CT征象，以最大限度地提高其诊断、预测效能和有效性。本研究分析sICH 患者发生HE的影响因素并构建相关的预测模型，如临床模型、CT平扫影像学表现(non-contrast computed tomography，NCCT)模型、GCS模型及列线图模型等，以期为临床制定有效的治疗方案提供帮助。
1 资料与方法
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1.1 研究对象
回顾性收集2017年1月-2022年12月西部战区总医院收治的238例sICH患者的相关临床资料。本研究获西部战区总医院伦理委员会审批(2021xjsxxm-2023-1-补)。
1.2 纳入与排除标准
纳入标准：(1)年龄>18岁；(2)符合2022版《自发性脑出血诊疗指南——美国心脏协会/美国卒中协会的健康职业者指南》中的脑出血诊断标准[1]；(3)首次CT检查时间及首次CT复查时间均<24 h；(4)临床资料、影像学检查及实验室检验结果完整；(5)签署知情同意书。排除标准：(1)继发性脑出血；(2)复查CT前实施了外科、介入等干预；(3)影像学图像质量差，影响判断及测量；(4)微小脑出血(<3 mm)及多发性脑出血。
1.3 CT检查方法与分组
扫描基线为听毗线，扫描范围为颅底至颅顶。患者仰卧位，头先进，双手自然置于身体两侧，平静呼吸，放松身体。采用128排256层螺旋CT机(iCT，荷兰飞利浦公司)检查；扫描参数：管电压120 kV，管电流380 mA，螺距1.0，层厚5 mm，视野(field of view，FOV)217 mm，转速1.0 s/圈。
多数学者认为，sICH患者复查时血肿体积与首次血肿体积比较增加>6 ml或>33%则判定为HE[5]；按照此标准，根据3D Slicer(Version 4.11.200930，美国哈佛大学)软件提取出的两次血肿体积差异，将sICH患者分为HE组(n=62)与血肿未扩大(NHE)组(n=176)。
1.4 临床资料与图像观察分析
本研究所有临床资料、实验室相关检查结果和影像学资料由两名具有5年以上诊断经验的影像科主治医师进行双盲法识别，并记录相关数据，当两名医师出现分歧时，则商量并最终达成一致。
1.5 图像分割
首先对CT图像进行归一化处理，包括图像体素重采样为1 mm×1 mm×1 mm、灰度离散化处理(256 bins)和固定的脑组织窗(窗宽90 HU，窗位35 HU)。
由两名具有5年以上神经系统疾病诊断经验的主治医师使用3D Slicer软件，沿血肿轮廓手动逐层勾画首次颅脑CT检查图像及复查颅脑CT图像上的血肿；再由1名具有3年神经系统疾病诊断经验的医师审查勾画及保存的数据，确保数据的完整性、准确性。
1.6 HE预测模型的构建与效能评估
比较HE组与NHE组sICH患者的临床特征(临床症状、实验室检查结果)、CT检查图像，采用多因素logistic回归分析筛选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的预测模型，分别构建临床模型、NCCT模型、列线图模型；采用一致性指数评价模型的区分度，采用校准曲线评价模型的校准度，使用决策曲线分析(decision curve analysis，DCA)评估模型的临床有效性。采用受试者工作特征(ROC)曲线下面积(AUC)评估各模型对HE的预测价值，采用Delong检验比较各模型的差异性，采用准确度、敏感度、特异度、约登指数评估模型预测效能。
1.7 统计学处理
采用SPSS 22.0软件进行统计分析。符合正态分布的计量资料以$\bar{x}±s$表示，组间比较采用独立样本t检验；非正态分布的计量资料以M(Q1，Q3)表示，组间比较采用秩和检验。计数资料以例(%)表示，组间比较采用χ2 检验。采用Lasso-Logistic回归模型[采用R 语言(版本4.0.0)中的glmnet包(版本4.1)进行分析]选择影响因素；采用ROC曲线分析模型的预测价值。P<0.05为差异有统计学意义。
2 结果
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2.1 两组sICH患者一般资料比较
HE组和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)；两组其他特征比较差异均无统计学意义(P>0.05，表1)。
2.2 血肿扩大影响因素的多因素logistic回归分析结果
多因素logistic回归分析结果显示，影响血肿扩大的独立危险因素包括5个临床特征(首次CT检查时间、均匀度、抗凝药物治疗史、体积、最大径)和4个影像学特征(低密度征、岛征、卫星征、旋涡征)(表2)。
2.3 列线图预测模型的构建与校正曲线及临床净收益分析
将首次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、旋涡征作为构建列线图模型的预测因子(图1)。校准曲线显示，观测值与预测值的一致性较好，拟合度也较好(图2)。红色曲线表示列线图模型在净获益高于深黑色虚线(所有样本均为阴性，不做任何干预)及灰色虚线(所有样本均为阳性，均接受干预)，使用列线图模型预测获益最高(图3)。
2.4 各预测模型的构建、预测效能比较
列线图模型预测sICH患者发生HE的准确度、敏感度、约登指数、AUC值均高于临床模型及NCCT模型(表3，图4)。Delong检验结果显示，临床模型与NCCT模型、列线图模型的AUC比较，差异无统计学意义(P>0.05)；列线图模型的预测价值高于GCS模型与临床模型(P<0.05)，NCCT模型与列线图模型的AUC比较，差异有统计学意义(P<0.05，表4)。
3 讨论
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Lord等[6]的一项纳入376例患者的研究显示， GCS是临床预测脑出血患者死亡和严重残疾的重要参考依据；伍发等[7]也得出相似的结果。本研究结果显示，由GCS构建的预测模型对sICH患者发生HE的预测值与临床模型、NCCT模型及列线图模型有明显差异，提示GCS评分影响各模型的预测价值；但GCS量表与HE之间的关系仍存在争议，该指标存在一定的主观性与选择偏倚，其临床预测价值尚待进一步研究、验证。Steiner等[8]纳入54例患者的研究显示，长期服用抗凝药物是HE的独立预测因子，更容易出现脑出血扩大，其出现3个月死亡或功能依赖的比例更高；但其因果关系及生理机制尚不清楚，期待更多关于治疗抗血小板药物相关脑出血治疗的研究。
NCCT 为脑出血患者诊断的基础性影像学检查，具有无创、成像快、费用低等优点，2022版《自发性脑出血诊疗指南——美国心脏协会/美国卒中协会的健康职业者指南》提出，sICH患者尽早行头部 CT以预测血肿扩大风险的重要性较高(Ⅱa 类推荐，B 级证据)[1]，可单独使用或作为基于临床变量预测评分的一部分，用其指导做出对伴有HE风险的脑出血患者分诊和监测的决策是有吸引力的。部分与血肿扩大相关的影像学指标与HE密切相关，本研究结果显示，初次CT扫描时间[9]、基线脑出血体积[10]、最大径[11]、CT显示的血肿密度均匀度[12]是sICH 患者发生HE的独立影响因素。基于NCCT 标记物被提出用于预测脑出血患者早期HE，包括岛征[13]、卫星征[14]、混合征[15]、漩涡征[16]等。但大多数NCCT标记物的敏感度和准确度均较低，缺乏病理基础，出现的机制也未完全清楚。无论sICH患者神经功能状况如何，初次CT扫描后进行常规CT复查，用以评估脑出血HE，在临床实践中并不少见。未来研究应评估脑出血后系列成像的成本/效益影响，并明确应考虑系列成像的患者特征和条件。
R语言的列线图目前主要应用于肿瘤、基因等相关专业，而用于脑出血的研究相对较少，但其在数据建模方面具有一定的优势[17]。Yu等[18]及Cui等[19]将列线图用于脑出血患者的数据建模，发现其预测效能较好，具有较高的预测效能；本研究结果也显示，列线图模型对于预测sICH患者发生HE有较高的效能。另外，随着人工智能的不断进步，临床医师可根据该影像组学标签来筛选HE高风险的sICH患者，采取更为积极的治疗措施，从而改善患者预后[20]。Zhong等[21]发现，深度学习算法在预测脑出血患者早期HE方面具有优势，深度学习模型表现出最高的AUC为0.80，由此可见，基于组学及深度学习技术对预测HE有很大的潜力，可能成为未来的研究热点。
本研究通过多因素分析，探索了脑出血患者血肿扩大的危险因素；基于上述危险因素分析结果构建了列线图模型，同时验证了该模型的预测价值，并与临床及影像预测模型对比，结果显示该模型具有较好的预测能力，可用于预测脑出血患者早期血肿扩大的风险，希望能够指导临床医师改善患者预后，提高生存质量。
本研究仍存在一定的局限性：样本量较小；测试数据集来自单中心，还需要更多的外部验证；作为回顾性研究，可能存在选择偏倚，影像特征的收集可能存在一定的主观性。
综上，本研究的多因素logistic回归分析结果显示，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sICH患者发生早期HE的独立影响因素；由此构建的列线图模型，从各项评价指标看，预测效能较高，具有一定的临床应用价值。
基金
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  • 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)
  • 西部战区总医院院管课题(2021-XZYG-C05)
文献
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参考文献 引证文献
排序方式：
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Greenberg SM, Ziai WC, Cordonnier C, et al. 2022 guideline for the management of patients with spontaneous intracerebral hemorrhage: a guideline from the American Heart Association/American Stroke Association[J]. Stroke, 2022, 53(7): e282-e361.
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Fu F, Sun SJ, Liu LP, et al. Iodine sign as a novel predictor of hematoma expansion and poor outcomes in primary intracerebral hemorrhage patients[J]. Stroke, 2018, 49(9): 2074-2080.
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Lord AS, Gilmore E, Choi HA, et al. Time course and predictors of neurological deterioration after intracerebral hemorrhage[J]. Stroke, 2015, 46(3): 647.
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伍发, 蒋锐, 杜飞舟, 等. 探究脑出血早期血肿扩大的影响因素及预测模型构建与验证[J]. 中国临床医学影像杂志, 2022, 33(10): 696-699,709.
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Lv XN, Deng L, Yang WS, et al. Computed tomography imaging predictors of intracerebral hemorrhage expansion[J]. Curr Neurol Neurosci Rep, 2021, 21(5): 22.
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Xu W, Ding ZX, Shan YN, et al. A nomogram model of radiomics and satellite sign number as imaging predictor for intracranial hematoma expansion[J]. Front Neurosci, 2020,14: 491.
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Yang WS, Zhang SQ, Shen YQ, et al. Noncontrast computed tomography markers as predictors of revised hematoma expansion in acute intracerebral hemorrhage[J]. J Am Heart Assoc, 2021, 10(3): e018248.
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Ng D, Churilov L, Mitchell P, et al. The CT swirl sign is associated with hematoma expansion in intracerebral hemorrhage[J]. Am J Neuroradiol, 2018, 39(2): 232-237.
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2024年第49卷第5期
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文章信息
doi: 10.11855/j.issn.0577-7402.0855.2023.1206
  • 接收时间：2023-06-19
  • 首发时间：2025-11-21
  • 出版时间：2024-05-28
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出版历史
  • 收稿日期：2023-06-19
  • 录用日期：2023-08-15
基金
Boosting Fund of General Hospital of Western Theater Comm(2019ZT09)
西部战区总医院助推基金(2019ZT09)
Special Research Project of Sichuan Medical Association (Hengrui) Research Fund(2021HR75)
四川省医学会(恒瑞)科研基金专项科研课题(2021HR75)
Foundation of General Hospital of Western Comm(2021-XZYG-C04)
西部战区总医院院管课题(2021-XZYG-C04)
Foundation of General Hospital of Western Comm(2021-XZYG-C05)
西部战区总医院院管课题(2021-XZYG-C05)
作者信息
    1解放军西部战区总医院放射诊断科，四川成都 610083
    2成都市第五人民医院超声科，四川成都 611100
    3解放军西部战区总医院神经外科，四川成都 610083

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