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Article(id=1241522766919291631, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522764012647140, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202307487, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1689955200000, receivedDateStr=2023-07-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931694011, onlineDateStr=2026-03-19, pubDate=1704816000000, pubDateStr=2024-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931694011, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931694011, creator=13701087609, updateTime=1773931694011, updator=13701087609, issue=Issue{id=1241522764012647140, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773931693318, creator=13701087609, updateTime=1773931808852, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241523248643494379, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522764012647140, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241523248643494380, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522764012647140, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=21, endPage=26, ext={EN=ArticleExt(id=1241522767242253043, articleId=1241522766919291631, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of risk factors related to recurrent hemorrhagic stroke and the construction and verification of predictive model, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the risk factors related to the recurrence in patients with hemorrhagic stroke and to establish a corresponding risk prediction model to provide reference for reducing the recurrence rate of hemorrhagic stroke.

Methods

The patients with hemorrhagic stroke treated in the Department of Encephalopathy, the First Affiliated Hospital of Henan University of Chinese Medicine from January 1, 2020 to December 31, 2022 were included in the study. The clinical data of patients with recurrent hemorrhagic stroke were collected to explore the risk factors related to recurrence of hemorrhagic stroke. The original data were divided into modeling sequence and verification sequence by random grouping method, and the modeling sequence was analyzed by univariate and multivariate analyses and model construction. The C index, receiver operator characteristic (ROC) curve, standard curve, and clinical decision curve were used to evaluate the discrimination, accuracy, and clinical utility of the model, and the prediction accuracy of the model was verified by verification sequence.

Results

A total of 1 571 patients with hemorrhagic stroke were included, including 253 patients with recurrent hemorrhagic stroke and 1 318 patients with initial hemorrhagic stroke. The original data set was constructed and randomly divided into modeling sequence and verification sequence according to 7:3 ratio. Logistic regression analysis showed that course of disease (OR=3.548, 95%CI: 2.852-4.415), smoking (OR=1.499, 95%CI: 1.136-1.978), drinking (OR=3.330, 95%CI: 2.356-4.709), male (OR=1.424, 95%CI:1.006-2.016), hyperlipidemia (OR=4.372, 95%CI: 2.227-8.584), cerebral infarction (OR=2.254, 95%CI: 1.294-3.928), high density lipoprotein cholesterol (OR=2.034, 95%CI: 1.220-3.389), prothrombin time (OR=1.103, 95%CI: 1.004-1.211), and homocysteine (OR=1.03, 95%CI: 1.014-1.047) were the influencing factors of recurrence in patients with hemorrhagic stroke, and a risk prediction model was established. The area under the ROC curve of the modeling sequence and the verification sequence was 0.834 (95%CI: 0.810-0.859) and 0.842 (95%CI: 0.804-0.880), respectively, and the standard curves were highly consistent. The results of Hosmer Lemeshow goodness-of-fit test were P=0.900 and P=0.736, respectively, and the thresholds of clinical decision curves were < 0.9 and <0.95, respectively, indicating that the model had high discrimination, calibration, and clinical effectiveness.

Conclusion

Course of disease, smoking, drinking, sex, hyperlipidemia, cerebral infarction, high density lipoprotein cholesterol, prothrombin time and homocysteine are independent risk factors of hemorrhagic stroke. The risk prediction model established in this study can timely identify high-risk patients with recurrent hemorrhagic stroke and prevent the occurrence of adverse events.

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目的

探讨出血性卒中患者复发相关危险因素，并构建相应风险预测模型，为降低出血性卒中复发率提供参考。

方法

纳入2020年1月1日—2022年12月31日河南中医药大学第一附属医院脑病科收治的出血性卒中患者为研究对象，以是否复发出血性卒中为因变量，收集患者的临床资料，探讨出血性卒中患者复发相关危险因素；采用随机分组法将原始数据分为建模队序列及验证序列，建模序列进行单因素、多因素分析及模型构建，用C指数、ROC曲线、标准曲线及临床决策曲线评估模型的区分度、准确性和临床效用；验证序列对模型的预测准确性进行验证。

结果

共纳入出血性卒中患者1 571例，其中再发出血性卒中253例，初发出血性卒中1 318例，构建原始数据集，以7:3原始数据集随机分为建模序列与验证序列。Logistic回归分析显示，病程（OR=3.548，95%CI：2.852～4.415）、吸烟（OR=1.499，95%CI：1.136～1.978）、饮酒（OR=3.330，95%CI：2.356～4.709）、男性（OR=1.424，95%CI：1.006～2.016）、高脂血症（OR=4.372，95%CI：2.227～8.584）、脑梗死（OR=2.254，95%CI：1.294～3.928）、高密度脂蛋白胆固醇（OR=2.034，95%CI：1.220～3.389）、凝血酶原时间（OR=1.103，95%CI：1.004～1.211）、同型半胱氨酸（OR=1.03，95%CI：1.014～1.047）是出血性卒中患者复发的相关影响因素，据此建立风险预测模型。该模型在建模序列及验证序列ROC曲线下面积分别为0.834（95%CI：0.810～0.859）和0.842（95%CI：0.804～0.880），标准曲线均具有较强一致性，且Hosmer-Lemeshow拟合优度检验的结果分别为P=0.900和P=0.736，临床决策曲线阈值分别为<0.9和<0.95,表明模型具有较高区分度、校准度及临床效用。

结论

病程、吸烟、饮酒、性别、高脂血症、脑梗死、高密度脂蛋白胆固醇、凝血酶原时间、同型半胱氨酸是出血性卒中的独立危险因素，本研究所建立的风险预测模型，可及时识别出血性卒中复发高危患者，预防不良事件发生，有望在临床中进行推广。

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刘向哲，E-mail:
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董稳悦（1998—），女，硕士在读，研究方向：中医药防治脑血管病研究

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董稳悦（1998—），女，硕士在读，研究方向：中医药防治脑血管病研究

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董稳悦（1998—），女，硕士在读，研究方向：中医药防治脑血管病研究

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Comparison of basic clinical data of patients with modeling sequence and validation sequence ［n（%），（）］

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项目分类分区统计量P值
建模（n=1 141）验证（n=430）
病程（月）<1507(44.43)200(46.51)0.9930.609
1～6451(39.53)169(39.30)
>6183(16.04)61(14.19)
吸烟从不吸烟867(75.99)316(73.49)1.7680.413
已戒烟128(11.22)48(11.16)
饮酒从不饮酒852(74.67)304(70.70)2.5980.273
已戒酒217(19.02)96(22.33)
现在饮酒72(6.31)30(6.98)
性别男776(68.01)306(71.16)1.4480.229
女365(31.99)124(28.84)
年龄（岁）55.73±13.9956.23±13.940.4010.527
BMI（kg/m2）24.41±8.0124.13±3.440.4930.483
高血压史无294(25.77)126(29.30)1.9930.158
有847(74.23)304(70.70)
糖尿病史无911(79.84)343(79.77)0.0010.974
有230(20.16)87(20.23)
高脂血症史无1 074(94.13)406(94.42)0.0480.826
有67(5.87)24(5.58)
冠心病史无1 028(90.10)382(88.84)0.5380.463
有113(9.90)48(11.16)
脑梗死史无1 050(92.02)396(92.09)0.0020.964
有91(7.98)34(7.91)
TC（mmol/L）4.27±1.034.21±1.010.9490.330
TG（mmol/L）1.64±1.101.54±0.912.6530.104
LDL-C（mmol/L）2.93±0.912.87±0.891.7800.182
HDL-C（mmol/L）1.08±0.311.10±0.311.1090.293
PT（s）10.74±1.7810.92±1.733.0700.080
FIB（mg/dl）3.96±1.043.90±1.041.0070.316
TT(s)14.82±1.6114.89±1.620.5620.453
DD(μg/L)0.63±1.530.79±2.981.8930.169
HCY(μmol/L)16.99±9.1117.65±9.301.5750.210
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建模序列和验证序列患者基本临床资料的比较［n（%），（）］

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项目分类分区统计量P值
建模（n=1 141）验证（n=430）
病程（月）<1507(44.43)200(46.51)0.9930.609
1～6451(39.53)169(39.30)
>6183(16.04)61(14.19)
吸烟从不吸烟867(75.99)316(73.49)1.7680.413
已戒烟128(11.22)48(11.16)
饮酒从不饮酒852(74.67)304(70.70)2.5980.273
已戒酒217(19.02)96(22.33)
现在饮酒72(6.31)30(6.98)
性别男776(68.01)306(71.16)1.4480.229
女365(31.99)124(28.84)
年龄（岁）55.73±13.9956.23±13.940.4010.527
BMI（kg/m2）24.41±8.0124.13±3.440.4930.483
高血压史无294(25.77)126(29.30)1.9930.158
有847(74.23)304(70.70)
糖尿病史无911(79.84)343(79.77)0.0010.974
有230(20.16)87(20.23)
高脂血症史无1 074(94.13)406(94.42)0.0480.826
有67(5.87)24(5.58)
冠心病史无1 028(90.10)382(88.84)0.5380.463
有113(9.90)48(11.16)
脑梗死史无1 050(92.02)396(92.09)0.0020.964
有91(7.98)34(7.91)
TC（mmol/L）4.27±1.034.21±1.010.9490.330
TG（mmol/L）1.64±1.101.54±0.912.6530.104
LDL-C（mmol/L）2.93±0.912.87±0.891.7800.182
HDL-C（mmol/L）1.08±0.311.10±0.311.1090.293
PT（s）10.74±1.7810.92±1.733.0700.080
FIB（mg/dl）3.96±1.043.90±1.041.0070.316
TT(s)14.82±1.6114.89±1.620.5620.453
DD(μg/L)0.63±1.530.79±2.981.8930.169
HCY(μmol/L)16.99±9.1117.65±9.301.5750.210
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Analysis of prognostic factors for modeling cohort patients

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变量单因素logistic分析多因素logistic分析
OR值(95%CI)P值OR值(95%CI)P值
病程（月）3.464(2.747～4.367)<0.0013.548（2.852～4.415）<0.001
吸烟1.480(1.120～1.955）0.0061.499（1.136～1.978）0.004
饮酒3.339（2.348～4.749）<0.0013.330（2.356～4.709）<0.001
性别1.394（0.964～2.015）0.0281.424（1.006～2.016）0.046
年龄（岁）1.009（0.996～1.021）0.171
高血压史1.134（0.788～1.631）0.497
糖尿病史1.109（0.753～1.632）0.600
高脂血症史4.109（2.064～8.180）<0.0014.372（2.227～8.584）<0.001
冠心病史0.678（0.396～1.161）0.157
脑梗死史2.356（1.334～4.161）0.0032.254（1.294～3.928）0.004
BMI（kg/m2）1.014（0.987～1.042）0.312
TC（mmol/L）0.817（0.553～1.206）0.309
TG（mmol/L）0.907（0.748～1.099）0.319
LDL-C（mmol/L）1.200（0.816～1.765）0.353
HDL-C（mmol/L）2.202（1.144～4.235）0.0182.034（1.220～3.389）0.006
PT（s）0.915（0.817～1.025）0.126
FIB（mg/dl）1.090（0.910～1.306）0.350
TT（s）1.149（1.025～1.288）0.0171.103（1.004～1.211）0.042
DD（μg/L）0.941（0.826～1.073）0.366
HCY（μmol/L）1.030（1.013～1.048）<0.0011.03（1.014～1.047）<0.001
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建模队列患者的预后因素分析

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变量单因素logistic分析多因素logistic分析
OR值(95%CI)P值OR值(95%CI)P值
病程（月）3.464(2.747～4.367)<0.0013.548（2.852～4.415）<0.001
吸烟1.480(1.120～1.955）0.0061.499（1.136～1.978）0.004
饮酒3.339（2.348～4.749）<0.0013.330（2.356～4.709）<0.001
性别1.394（0.964～2.015）0.0281.424（1.006～2.016）0.046
年龄（岁）1.009（0.996～1.021）0.171
高血压史1.134（0.788～1.631）0.497
糖尿病史1.109（0.753～1.632）0.600
高脂血症史4.109（2.064～8.180）<0.0014.372（2.227～8.584）<0.001
冠心病史0.678（0.396～1.161）0.157
脑梗死史2.356（1.334～4.161）0.0032.254（1.294～3.928）0.004
BMI（kg/m2）1.014（0.987～1.042）0.312
TC（mmol/L）0.817（0.553～1.206）0.309
TG（mmol/L）0.907（0.748～1.099）0.319
LDL-C（mmol/L）1.200（0.816～1.765）0.353
HDL-C（mmol/L）2.202（1.144～4.235）0.0182.034（1.220～3.389）0.006
PT（s）0.915（0.817～1.025）0.126
FIB（mg/dl）1.090（0.910～1.306）0.350
TT（s）1.149（1.025～1.288）0.0171.103（1.004～1.211）0.042
DD（μg/L）0.941（0.826～1.073）0.366
HCY（μmol/L）1.030（1.013～1.048）<0.0011.03（1.014～1.047）<0.001
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出血性卒中复发相关危险因素分析及预测模型的构建与验证
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董稳悦 1 , 赵文举 1 , 徐慧娜 1 , 刘向哲 2
现代预防医学 | 流行病与统计方法 2024,51(1): 21-26
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现代预防医学 | 流行病与统计方法 2024, 51(1): 21-26
出血性卒中复发相关危险因素分析及预测模型的构建与验证
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董稳悦1, 赵文举1, 徐慧娜1, 刘向哲2
作者信息
  • 1.河南中医药大学，河南 郑州 450046
  • 2.河南中医药大学第一附属医院，河南 郑州 450000

    • 董稳悦（1998—），女，硕士在读，研究方向：中医药防治脑血管病研究

通讯作者:

刘向哲，E-mail:
Analysis of risk factors related to recurrent hemorrhagic stroke and the construction and verification of predictive model
Wen-yue DONG1, Wen-ju ZHAO1, Hui-na XU1, Xiang-zhe LIU2
Affiliations
  • Henan University of Chinese Medicine, Zhengzhou, Henan 450046, China
出版时间: 2024-01-10 doi: 10.20043/j.cnki.MPM.202307487
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摘要
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目的

探讨出血性卒中患者复发相关危险因素，并构建相应风险预测模型，为降低出血性卒中复发率提供参考。

方法

纳入2020年1月1日—2022年12月31日河南中医药大学第一附属医院脑病科收治的出血性卒中患者为研究对象，以是否复发出血性卒中为因变量，收集患者的临床资料，探讨出血性卒中患者复发相关危险因素；采用随机分组法将原始数据分为建模队序列及验证序列，建模序列进行单因素、多因素分析及模型构建，用C指数、ROC曲线、标准曲线及临床决策曲线评估模型的区分度、准确性和临床效用；验证序列对模型的预测准确性进行验证。

结果

共纳入出血性卒中患者1 571例，其中再发出血性卒中253例，初发出血性卒中1 318例，构建原始数据集，以7:3原始数据集随机分为建模序列与验证序列。Logistic回归分析显示，病程（OR=3.548，95%CI：2.852～4.415）、吸烟（OR=1.499，95%CI：1.136～1.978）、饮酒（OR=3.330，95%CI：2.356～4.709）、男性（OR=1.424，95%CI：1.006～2.016）、高脂血症（OR=4.372，95%CI：2.227～8.584）、脑梗死（OR=2.254，95%CI：1.294～3.928）、高密度脂蛋白胆固醇（OR=2.034，95%CI：1.220～3.389）、凝血酶原时间（OR=1.103，95%CI：1.004～1.211）、同型半胱氨酸（OR=1.03，95%CI：1.014～1.047）是出血性卒中患者复发的相关影响因素，据此建立风险预测模型。该模型在建模序列及验证序列ROC曲线下面积分别为0.834（95%CI：0.810～0.859）和0.842（95%CI：0.804～0.880），标准曲线均具有较强一致性，且Hosmer-Lemeshow拟合优度检验的结果分别为P=0.900和P=0.736，临床决策曲线阈值分别为<0.9和<0.95,表明模型具有较高区分度、校准度及临床效用。

结论

病程、吸烟、饮酒、性别、高脂血症、脑梗死、高密度脂蛋白胆固醇、凝血酶原时间、同型半胱氨酸是出血性卒中的独立危险因素，本研究所建立的风险预测模型，可及时识别出血性卒中复发高危患者，预防不良事件发生，有望在临床中进行推广。

出血性卒中  /  复发  /  危险因素  /  预测模型
Objective

To investigate the risk factors related to the recurrence in patients with hemorrhagic stroke and to establish a corresponding risk prediction model to provide reference for reducing the recurrence rate of hemorrhagic stroke.

Methods

The patients with hemorrhagic stroke treated in the Department of Encephalopathy, the First Affiliated Hospital of Henan University of Chinese Medicine from January 1, 2020 to December 31, 2022 were included in the study. The clinical data of patients with recurrent hemorrhagic stroke were collected to explore the risk factors related to recurrence of hemorrhagic stroke. The original data were divided into modeling sequence and verification sequence by random grouping method, and the modeling sequence was analyzed by univariate and multivariate analyses and model construction. The C index, receiver operator characteristic (ROC) curve, standard curve, and clinical decision curve were used to evaluate the discrimination, accuracy, and clinical utility of the model, and the prediction accuracy of the model was verified by verification sequence.

Results

A total of 1 571 patients with hemorrhagic stroke were included, including 253 patients with recurrent hemorrhagic stroke and 1 318 patients with initial hemorrhagic stroke. The original data set was constructed and randomly divided into modeling sequence and verification sequence according to 7:3 ratio. Logistic regression analysis showed that course of disease (OR=3.548, 95%CI: 2.852-4.415), smoking (OR=1.499, 95%CI: 1.136-1.978), drinking (OR=3.330, 95%CI: 2.356-4.709), male (OR=1.424, 95%CI:1.006-2.016), hyperlipidemia (OR=4.372, 95%CI: 2.227-8.584), cerebral infarction (OR=2.254, 95%CI: 1.294-3.928), high density lipoprotein cholesterol (OR=2.034, 95%CI: 1.220-3.389), prothrombin time (OR=1.103, 95%CI: 1.004-1.211), and homocysteine (OR=1.03, 95%CI: 1.014-1.047) were the influencing factors of recurrence in patients with hemorrhagic stroke, and a risk prediction model was established. The area under the ROC curve of the modeling sequence and the verification sequence was 0.834 (95%CI: 0.810-0.859) and 0.842 (95%CI: 0.804-0.880), respectively, and the standard curves were highly consistent. The results of Hosmer Lemeshow goodness-of-fit test were P=0.900 and P=0.736, respectively, and the thresholds of clinical decision curves were < 0.9 and <0.95, respectively, indicating that the model had high discrimination, calibration, and clinical effectiveness.

Conclusion

Course of disease, smoking, drinking, sex, hyperlipidemia, cerebral infarction, high density lipoprotein cholesterol, prothrombin time and homocysteine are independent risk factors of hemorrhagic stroke. The risk prediction model established in this study can timely identify high-risk patients with recurrent hemorrhagic stroke and prevent the occurrence of adverse events.

Hemorrhagic stroke  /  Recurrence  /  Risk factors  /  Predictive model
董稳悦, 赵文举, 徐慧娜, 刘向哲. 出血性卒中复发相关危险因素分析及预测模型的构建与验证. 现代预防医学, 2024 , 51 (1) : 21 -26 . DOI: 10.20043/j.cnki.MPM.202307487
Wen-yue DONG, Wen-ju ZHAO, Hui-na XU, Xiang-zhe LIU. Analysis of risk factors related to recurrent hemorrhagic stroke and the construction and verification of predictive model[J]. Modern Preventive Medicine, 2024 , 51 (1) : 21 -26 . DOI: 10.20043/j.cnki.MPM.202307487
正文
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全球疾病负担研究(GBD)数据显示，卒中是我国成人致死、致残的首位病因，中国作为最大的发展中国家，人口约占世界总人口的1/5，目前卒中患病人数居于世界首位[1]。而出血性卒中是卒中的主要类型之一，具有起病急剧、病程凶险、预后差等致病特点，可引发患者不同程度的功能障碍，如言语、运动功能和感觉障碍等，严重威胁人们的健康及生活质量。对于出血性卒中患者的治疗，除了致力于恢复患者的功能障碍外，预防疾病再次发作也是至关重要的考虑因素。有研究报道，全球范围内复发性脑出血的死亡率为23%～70%，且其致残率远高于初发性脑出血[2]。因此，国内外广泛将脑卒中再入院率作为疗效评价及病程监测的重要指标之一[3]，如何有效预防出血性卒中复发有着非常重要的临床意义。本研究对1 571例出血性卒中患者复发的危险因素进行分析，建立复发预测模型，并利用该模型预测出血性卒中的复发情况，以便识别高风险的出血性卒中复发人群，并提供科学依据用于治疗干预和预防复发。
1　对象与方法
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1.1　研究对象
纳入2020年1月1日—2022年12月31日河南中医药大学第一附属医院脑病科收治的出血性卒中患者1 571例为研究对象，其中复发出血性卒中253例，初发出血性卒中1 318例，构建原始数据集，以7:3原始数据集随机分为建模序列与验证序列。纳入标准：（1）头颅CT或核磁明确诊断为出血性卒中者；（2）临床病理资料记录完整。排除标准：（1）出血性卒中由脑肿瘤、脑外伤、血液病、感染性疾病、遗传性疾病、风湿免疫疾病等引起者；（2）合并严重精神、心理疾病不能配合者；（3）临床资料不全者。本研究所有患者和（或）家属均签署知情同意书，并经河南中医药大学第一附属医院伦理委员会审查批准，审查批准号：2020HL-019。
1.2　数据收集与检测
收集患者基本信息包括年龄、性别、体质指数（BMI）、病程、吸烟、饮酒，既往史包括高血压、冠心病、糖尿病、脑梗死、高脂血症及实验室检查数据包括总胆固醇(TC)、甘油三酯(TG)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、凝血酶原时间（PT）、凝血酶时间（TT）、纤维蛋白原（FIB）、D-二聚体（DD）、同型半胱氨酸（HCY）等。所有实验室检查由河南中医药大学第一附属医院检验科检测。
1.3　统计学方法
采用SPSS25.0统计软件，正态分布的计量资料用（）表示，采用独立样本t检验；偏态分布的计量资料用M（P1，P3）表示，采用非参数检验；计数资料用百分率表示，采用χ2检验；采用logistic回归分析筛选出血性卒中复发独立危险因素，并建立风险预测模型，通过C指数（C-index）、受试者工作特征曲线（ROC）、校准曲线和临床决策曲线（DCA分析）评估预测模型在建模序列和验证序列中的区分度、准确性和临床效用。检验水准α=0.05。
2　结果
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2.1　一般资料
本研究共纳入出血性卒中患者1 571例，其中复发患者253例，复发率为16.1%。建模序列共纳入患者1 141例，验证序列共纳入患者430例，两个队列间的差异均无统计学意义（P>0.05）。见表1。
2.2　出血性卒中复发独立危险因素分析
建模序列的单因素分析结果显示，病程、性别、吸烟、饮酒、高脂血症病史、脑梗死病史、高密度脂蛋白、凝血酶原时间、同型半胱氨酸是出血性卒中复发的相关因素（P<0.05）。将以上单因素分析有统计学意义的指标纳入多因素logistic回归方程，结果显示：病程、吸烟、饮酒、性别、高脂血症史、脑梗死史、高密度脂蛋白、凝血酶原时间、同型半胱氨酸是预测出血性卒中患者复发的独立危险因素。见表2。
2.3　出血性卒中复发风险预测模型构建与评价
通过logistic回归分析中与出血性卒中复发相关因素，构建风险预测模型。以是否复发为结局事件，复发危险因素为：X1=病程，X2=吸烟,X3=饮酒,X4=性别,X5=高脂血症史,X6=脑梗死史,X7=高密度脂蛋白,X8=凝血酶原时间,X9=同型半胱氨酸。风险预测模型为：Y=-8.435+1.266X1+0.405X2+1.203X3+0.354X4+1.475X5+0.813X6+0.71X7+0.098X8+0.03X9+0.857。预测模型在建模序列及验证序列经受试者工作特征曲线（ROC）分析发现模型的曲线下面积分别为0.834（95%CI：0.810～0.859）和0.842（95%CI：0.804～0.880），见图1、2。预测模型在建模序列及验证序列经校准曲线验证结果均显示预测值和实际值基本接近一致，另外，H-L拟合优度检验结果显示分别为P=0.900和P=0.736，均>0.05，说明模型校准度较好，见图3、4。预测模型在建模序列及验证序列经临床决策曲线（DCA分析）验证结果显示阈值分别为<0.9和<0.95，表明该模型整体上预测性能较好，见图5、6。
3　讨论
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出血性卒中复发是首发出血性卒中后最为严重的并发症之一，也是其幸存者病残和病死的主要原因。既往研究表明，出血性卒中的复发率由于研究背景、诊疗条件、群体特征等因素的影响，国内外各地区不同研究结果的复发率存在差异，但总体上趋于5%～20%之间[4-7]。本研究通过调查出血性卒中患者再入院情况，观察到16.1%（253/1 571）的复发率，与既往研究相符。尽管目前临床上有越来越多的诊治手段，但在长期康复过程中，患者注重恢复日常生活功能和认知能力，而对于预防出血性卒中的复发缺乏认识。而且大多出血性卒中复发患者，临床治疗效果和预后往往不理想。因此，早期识别出血性卒中的复发能最大限度地减少其复发风险，且对评估患者状况和改善预后具有显著意义。
性别差异、吸烟、酒精摄入等是脑卒中复发的危险因素[5，8]。本研究结果显示，男性复发率显著高于女性。一方面，男性长期吸烟、饮酒等不良生活习惯,可能增加出血性卒中的复发风险，这与国外多项研究结果基本一致[9-10]。另一方面，可能由于女性绝经前体内的内源性雌激素水平下降，发挥对血管的潜在保护作用[11]。既往研究表明，健康的生活方式可以降低70%的卒中风险[12]，美国脑卒中协会、中国心脑血管疾病防治指南明确提出了将戒烟、限酒作为脑卒中防治的重要措施[13-14]，提示需要采取个性化的方法和策略，提高重点人群对酒精和烟草的认识，从而减少甚至戒除其使用。
既往研究表明，出血性卒中复发的间隔时间从1个月到14年不等,但以首次发病后1年以内复发较为多见[15-16]。吴智平等[17]研究发现出血性卒中复发间隔时间存在较大的差异，最短的复发间隔时间只有2个月，而最长的复发间隔时间可达13年，在发病后的第1年内，复发比例最高，为34.4%，随访的3年内复发比例依然相对较高，持续到5年后才开始降低且逐渐保持稳定，提示初发出血性卒中患者在患病5年之内特别是1年之内要尤其警惕复发，但之后仍然存在着复发的可能性。因此，对于出血性卒中患者来说，终身的预防措施和管理至关重要。
研究报道，升高的血浆Hcy水平与脑出血发生风险显著相关[18]。其作用机制可能是通过刺激平滑肌细胞增殖，导致血管内皮损伤，加速血管收缩，诱导血管痉挛等降低动脉血管弹性，加速粥样硬化进程[19]，还可增加血小板聚集，促进血栓形成。Zhou等[20]多元线性回归分析显示，丘脑神经节脑出血患者Hcy水平升高与血肿体积增大显著相关(B=0.604，P=0.004)。Cui等[21]研究结果报道，血浆Hcy水平的升高与卒中死亡风险的增加存在关联，其中，血浆Hcy每升高5 μmol/L，卒中死亡风险增加49%。因此，临床诊治过程中可以对同型半胱氨酸进行常规个体检测，并积极采取措施降低高血浆Hcy水平，以降低出血性卒中的发生风险。
本研究发现，出血性卒中复发伴随危险因素中包括既往高脂血症、脑梗死病史等，符合《中国心血管病风险评估和管理指南》[22]相应结果。血脂异常是动脉粥样硬化的主要原因，从而加剧脑卒中的发生。既往研究已证实脂质代谢异常对于缺血性卒中的发生发展过程起到至关重要的作用，降脂治疗能显著降低缺血性卒中发生的风险性。然而，血脂水平、降脂治疗与出血性卒中风险之间的关系尚无定论。Jamil[23]一项关于他汀类药物对脑出血复发的影响结果中提出需谨慎使用他汀类药物，提示降脂治疗可能具有潜在的脑出血风险。既往研究显示，脑梗死后再出血属于其自然病程的一部分，与血栓迁移、血管再通、侧支循环形成、抗凝溶栓治疗等密切相关[24]。另外，本研究发现凝血酶原时间测定与出血性卒中复发的相关性，也可能与既往脑梗死患者长期口服抗凝药物有关。因此，临床上对于既往存在高脂血症、脑梗死病史的患者，需要根据患者的个体情况开展综合性的治疗，在治疗患者基础疾病的同时能够更好的控制患者的临床症状，确保治疗的有效性。
因此，对于存在以上危险因素的出血性卒中患者，制定详细的诊治和出院管理方案至关重要。本研究通过收集和分析多个高危因素建立风险预测模型，以提高预测的准确性。临床工作者通过评估模型中的自变量因素，可以快速识别高危群体，并及时采取措施来改善治疗效果和临床结局，减轻患者和医疗卫生系统的负担。当然，本研究也有不足之处：首先，本研究纳入的患者群体主要来自本院脑病科2020年1月1日—2022年12月31日的住院患者群体，所以导致样本量不够大，可能使结果具有一定偏差。其次，本研究选取的患者主要来自河南省郑州市，具有一定的局限性，可能使结果产生偏差。最后，影响出血性卒中复发的危险因素较多，本研究主要纳入了一些可能的危险因素和实验室指标。因此未来为了更准确地评估出血性卒中患者的复发风险，构建良好的预警模型，仍需要进行多中心、大样本的前瞻性研究。
基金
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  • 河南省中医药传承与创新人才工程中医药学科领军人才项目(豫卫中医函［2021］8号)
  • 河南省中医学“双一流”创建科学研究专项(HSRP-DFCTCM-2023-2-18)
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2024年第51卷第1期
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doi: 10.20043/j.cnki.MPM.202307487
  • 接收时间：2023-07-22
  • 首发时间：2026-03-19
  • 出版时间：2024-01-10
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  • 收稿日期：2023-07-22
基金
河南省中医药传承与创新人才工程中医药学科领军人才项目(豫卫中医函［2021］8号)
河南省中医学“双一流”创建科学研究专项(HSRP-DFCTCM-2023-2-18)
作者信息
    1.河南中医药大学，河南 郑州 450046
    2.河南中医药大学第一附属医院，河南 郑州 450000

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刘向哲，E-mail:
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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