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Article(id=1241102738776445060, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202312221, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1702396800000, receivedDateStr=2023-12-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773831551498, onlineDateStr=2026-03-18, pubDate=1721836800000, pubDateStr=2024-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773831551498, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773831551498, creator=13701087609, updateTime=1773831551498, updator=13701087609, issue=Issue{id=1241102730337505325, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='14', pageStart='2497', pageEnd='2688', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773831549486, creator=13701087609, updateTime=1773831697291, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241103350322745680, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241103350322745681, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2620, endPage=2627, ext={EN=ArticleExt(id=1241102739632083089, articleId=1241102738776445060, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of influencing factors and construction of nomogram model in elderly patients with hypertension complicated with stroke, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To identify the influencing factors of stroke in elderly patients with hypertension based on community health survey, and establish a nomogram prediction model according to the influencing factors.

Methods

In 2022, the multi-stage sampling method was used to select the residents of Zhengzhou City, Henan province who lived for more than half a year for community health survey, and univariate and multivariate analysis was used to screen the influential factors of elderly hypertension patients complicated with stroke, and a nomogram prediction model was constructed. ROC curve, calibration curve and DCA decision curve are used to verify the accuracy and stability of the nomogram model.

Results

A total of 15 995 elderly patients with hypertension were included in this study. The original data set was randomly divided into a training set and a validation set at 7:3. Model construction: The results of binary logistics analysis of the training set showed that age, gender, waist circumference, blood pressure control, overweight or obesity, exercise, smoking, drinking, balanced diet, dyslipidemia and heart disease were independent influencing factors for stroke. A nomogram model was constructed according to the results of multi-factor analysis. Model validation: the area under the ROC curve of the training set and the verification set is 0.748\0.779 respectively, and the calibration curve is well fitted, indicating that the model has good predictive value.

Conclusion

In this study, we established a nomogram prediction model for elderly hypertensive patients with stroke, including demographic characteristics, health status and lifestyle. The model is accurate and stable, which can help to screen high-risk individuals, provide clinical decision-making and prevention recommendations.

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目的

基于社区健康调查识别老年高血压患者患脑卒中的影响因素，并根据影响因素建立列线图预测模型。

方法

于2022年采用多阶段抽样的方法抽取河南省郑州市居住半年以上的居民进行社区健康调查，使用单因素和多因素分析筛选老年高血压患者合并脑卒中的影响因素并构建列线图预测模型，ROC曲线、校准曲线、DCA决策曲线用于验证列线图模型的准确性和稳定性。

结果

本次研究共纳入15 995例老年高血压患者，将总体以7:3随机分为训练集与验证集。模型构建：训练集二元logistics分析结果显示，年龄、性别、腰围、血压控制是否达标、是否超重或肥胖、是否运动、是否吸烟、是否饮酒、是否饮食均衡、是否患有血脂异常、是否有心脏病史是患脑卒中的独立影响因素。根据多因素分析结果构建列线图模型。模型验证：训练集和验证集ROC曲线下面积分别为0.748\0.779、校准曲线拟合良好，表明该模型具有良好的预测价值。

结论

本研究建立了一个老年高血压患者合并脑卒中的列线图预测模型，其中包括人口学特征、健康状况和生活方式，该模型具有较好的准确性和稳定性，有助于筛查高危个体，提供临床决策和预防建议。

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田庆丰，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=kkapc5pYtj3gkahj5Rx4mA==, magXml=nAQHz8kRnGMffofpXvSInw==, pdfUrl=null, pdf=NwW8SkgaS0uVR/V2O23YTw==, pdfFileSize=844891, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=p0V+O+1O3vyFWCOtfOBceA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=IJol67WxwiD+hjPpii3eqg==, mapNumber=null, authorCompany=null, fund=null, authors=

王瑞（2001—），女，硕士在读，研究方向：卫生事业管理

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王瑞（2001—），女，硕士在读，研究方向：卫生事业管理

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王瑞（2001—），女，硕士在读，研究方向：卫生事业管理

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Frontiers in Cardiovascular Medicine, 2022, 9: 770025., articleTitle=Incident stroke and its influencing factors in patients with type 2 diabetes mellitus and/or hypertension: a prospective cohort study, refAbstract=null), Reference(id=1241102757600481315, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2022, volume=49, issue=16, pageStart=2899, pageEnd=2902, 2907, url=null, language=null, rfNumber=[17], rfOrder=26, authorNames=陈昕, 赵海娜, 李晓波, journalName=现代预防医学, refType=null, unstructuredReference=陈昕，赵海娜，李晓波，等．中老年人血压水平与脑卒中患病率及患病风险的相关性研究[J]．现代预防医学，2022，49（16）：2899-2902, 2907., articleTitle=中老年人血压水平与脑卒中患病率及患病风险的相关性研究, refAbstract=null), Reference(id=1241102757684367398, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2022, volume=49, issue=16, pageStart=2899, pageEnd=2902, 2907, url=null, language=null, rfNumber=[17], rfOrder=27, authorNames=Chen X, Zhao HN, Li XB, journalName=Modern Preventive Medicine, refType=null, unstructuredReference=Chen X, Zhao HN, Li XB, et al. Correlation between blood pressure levels and the prevalence and risk of stroke in middle-aged and elderlypeople[J]. Modern Preventive Medicine, 2022, 49(16): 2899-2902, 2907., articleTitle=Correlation between blood pressure levels and the prevalence and risk of stroke in middle-aged and elderlypeople, refAbstract=null), Reference(id=1241102757751476267, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2023, volume=44, issue=28, pageStart=2626, pageEnd=2635, url=null, language=null, rfNumber=[18], rfOrder=28, authorNames=Shi WM, Huang X, Schooling CM, journalName=European Heart Journal, refType=null, unstructuredReference=Shi WM, Huang X, Schooling CM, et al. Red meat consumption, cardiovascular diseases, and diabetes: a systematic review and meta-analysis[J]. European Heart Journal, 2023, 44(28): 2626-2635., articleTitle=Red meat consumption, cardiovascular diseases, and diabetes: a systematic review and meta-analysis, refAbstract=null), Reference(id=1241102757852139567, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2019, volume=393, issue=10183, pageStart=1831, pageEnd=1842, url=null, language=null, rfNumber=[19], rfOrder=29, authorNames=Millwood IY, Walters RG, Mei XW, journalName=Lancet, refType=null, unstructuredReference=Millwood IY, Walters RG, Mei XW, et al. Conventional and genetic evidence on alcohol and vascular diseaseaetiology: a prospective study of 500 000 men and women in China[J]. Lancet, 2019, 393(10183): 1831-1842., articleTitle=Conventional and genetic evidence on alcohol and vascular diseaseaetiology: a prospective study of 500 000 men and women in China, refAbstract=null), Reference(id=1241102757982162996, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2022, volume=40, issue=12, pageStart=146, pageEnd=149, url=null, language=null, rfNumber=[20], rfOrder=30, authorNames=史海蛟, 丁莉莉, 周宇石, journalName=中华中医药学刊, refType=null, unstructuredReference=史海蛟，丁莉莉，周宇石，等．冠状动脉粥样硬化性心脏病合并脑卒中的中医病机认识及临床研究进展[J]．中华中医药学刊，2022，40（12）：146-149., articleTitle=冠状动脉粥样硬化性心脏病合并脑卒中的中医病机认识及临床研究进展, refAbstract=null), Reference(id=1241102758082826298, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2022, volume=40, issue=12, pageStart=146, pageEnd=149, url=null, language=null, rfNumber=[20], rfOrder=31, authorNames=Shi HJ, Ding LL, Zhou YS, journalName=Chinese Archives of Traditional Chinese Medicine, refType=null, unstructuredReference=Shi HJ, Ding LL, Zhou YS, et al. TCM pathogenesis of coronary heart disease complicated with stroke and clinical research progress[J]. Chinese Archives of Traditional Chinese Medicine, 2022, 40(12): 146-149., articleTitle=TCM pathogenesis of coronary heart disease complicated with stroke and clinical research progress, refAbstract=null), Reference(id=1241102758170906683, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2021, volume=41, issue=4, pageStart=348, pageEnd=364, url=null, language=null, rfNumber=[21], rfOrder=32, authorNames=Migdady I, Russman A, Buletko AB, journalName=Seminars in Neurology, refType=null, unstructuredReference=Migdady I, Russman A, Buletko AB. Atrial fibrillation and ischemic stroke: a clinical review[J]. Seminars in Neurology, 2021, 41(4): 348-364., articleTitle=Atrial fibrillation and ischemic stroke: a clinical review, refAbstract=null), Reference(id=1241102758275764287, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2022, volume=26, issue=2, pageStart=136, pageEnd=143, url=null, language=null, rfNumber=[22], rfOrder=33, authorNames=刘敏, 包含, 徐肖倩, journalName=中华疾病控制杂志, refType=null, unstructuredReference=刘敏，包含，徐肖倩，等．基于内蒙古地区的非传统脂质参数与脑卒中的关联性研究[J]．中华疾病控制杂志，2022，26（2）：136-143., articleTitle=基于内蒙古地区的非传统脂质参数与脑卒中的关联性研究, refAbstract=null), Reference(id=1241102758363844676, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2022, volume=26, issue=2, pageStart=136, pageEnd=143, url=null, language=null, rfNumber=[22], rfOrder=34, authorNames=Liu M, Bao H, Xu XQ, journalName=Chinese Journal of Disease Control & Prevention, refType=null, unstructuredReference=Liu M,Bao H, Xu XQ, et al. The associations of non-traditional lipid profiles with stroke:based on Inner Mongolia population[J]. Chinese Journal of Disease Control & Prevention, 2022, 26(2): 136-143., articleTitle=The associations of non-traditional lipid profiles with stroke:based on Inner Mongolia population, refAbstract=null), Reference(id=1241102758430953541, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2023, volume=44, issue=1, pageStart=49, pageEnd=53, url=null, language=null, rfNumber=[23], rfOrder=35, authorNames=徐建可, 刘宁, 韩苗, journalName=首都医科大学学报, refType=null, unstructuredReference=徐建可，刘宁，韩苗，等．缺血性脑卒中患者颈动脉斑块性质与非高密度脂蛋白胆固醇浓度的相关性[J]．首都医科大学学报，2023，44（1）：49-53., articleTitle=缺血性脑卒中患者颈动脉斑块性质与非高密度脂蛋白胆固醇浓度的相关性, refAbstract=null), Reference(id=1241102758519033929, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2023, volume=44, issue=1, pageStart=49, pageEnd=53, url=null, language=null, rfNumber=[23], rfOrder=36, authorNames=Xu JK, Liu N, Han M, journalName=Journal of Capital Medical University, refType=null, unstructuredReference=Xu JK, Liu N, Han M, et al. Analysis of the relationship between non-high-density lipoprotein cholesterol and arterial plaque characteristic in a cohort of ischemic stroke patients[J]. Journal of Capital Medical University, 2023, 44(1): 49-53., articleTitle=Analysis of the relationship between non-high-density lipoprotein cholesterol and arterial plaque characteristic in a cohort of ischemic stroke patients, refAbstract=null), Reference(id=1241102758607114315, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, doi=null, pmid=null, pmcid=null, year=2022, volume=122, issue=3, pageStart=685, pageEnd=693, url=null, language=null, rfNumber=[24], rfOrder=37, authorNames=Szlachetka WA, Pana TA, Mamas MA, journalName=Acta Neurologica Belgica, refType=null, unstructuredReference=Szlachetka WA, Pana TA, Mamas MA, et al. Predicting 10-year stroke mortality: development and validation of a nomogram[J]. Acta Neurologica Belgica, 2022, 122(3): 685-693., articleTitle=Predicting 10-year stroke mortality: development and validation of a nomogram, refAbstract=null)], funds=[Fund(id=1241102753540395930, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, awardId=2020YFC2006100, language=CN, fundingSource=国家重点研发计划资助项目(2020YFC2006100), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241102745034346937, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, xref=null, ext=[AuthorCompanyExt(id=1241102745042735546, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, companyId=1241102745034346937, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Social Medicine and Health Management, School of Public Health,Zhengzhou University, Zhengzhou, Henan 450000, China), AuthorCompanyExt(id=1241102745051124155, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, companyId=1241102745034346937, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=郑州大学公共卫生学院社会医学与卫生事业管理教研室，河南 郑州 450000)])], figs=[ArticleFig(id=1241102749874574084, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=EN, label=Fig.1, caption=Nomogram prediction model, figureFileSmall=BLpT9jKVZ8U2JbBwGL4kpw==, figureFileBig=p0V+O+1O3vyFWCOtfOBceA==, tableContent=null), ArticleFig(id=1241102749983626000, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=CN, label=图1, caption=列线图预测模型

注：1男性。

, figureFileSmall=BLpT9jKVZ8U2JbBwGL4kpw==, figureFileBig=p0V+O+1O3vyFWCOtfOBceA==, tableContent=null), ArticleFig(id=1241102750260450085, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=EN, label=Fig.2, caption=ROC curve of the validation set, figureFileSmall=MQd1m7BSObaUFbPKmOMXWw==, figureFileBig=6x5xxJzZCr1RRA+YV8LFPA==, tableContent=null), ArticleFig(id=1241102750390473518, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=CN, label=图2, caption=验证集的ROC曲线, figureFileSmall=MQd1m7BSObaUFbPKmOMXWw==, figureFileBig=6x5xxJzZCr1RRA+YV8LFPA==, tableContent=null), ArticleFig(id=1241102750562439992, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=EN, label=Fig.3, caption=Calibration curve of validation set, figureFileSmall=aAzFLyuKqN+4lp55tOD0lg==, figureFileBig=o6B41sfj6vuXfquIT3vAkA==, tableContent=null), ArticleFig(id=1241102750696657726, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=CN, label=图3, caption=验证集的校准曲线, figureFileSmall=aAzFLyuKqN+4lp55tOD0lg==, figureFileBig=o6B41sfj6vuXfquIT3vAkA==, tableContent=null), ArticleFig(id=1241102750944121670, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=EN, label=Fig.4, caption=DCA curve of the verification set, figureFileSmall=78VyncJvwf16LQPQtq32qg==, figureFileBig=s+vQTOwimgjGxdTd6ce/YA==, tableContent=null), ArticleFig(id=1241102751103505236, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=CN, label=图4, caption=验证集的DCA曲线, figureFileSmall=78VyncJvwf16LQPQtq32qg==, figureFileBig=s+vQTOwimgjGxdTd6ce/YA==, tableContent=null), ArticleFig(id=1241102751250305884, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=EN, label=Table 1, caption=

Baseline information of research objects[()/ n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
项目训练集验证集χ2/tP
性别0.0010.978
男4 481(40.02)1 919(40.00)
女6 716(59.98)2 879(60.00)
民族0.2190.640
非汉族110(0.98)51(1.06)
汉族11 087(99.02)4 747(98.94)
教育水平1.4550.693
小学及以下5 419(48.40)2 303(48.00)
初中3 458(30.88)1 480(30.85)
高中/中专1 663(14.85)710(14.80)
大专/本科及以上657(5.87)305(6.36)
职业3.1170.077
体力劳动10 878(97.15)4 685(97.64)
脑力劳动319(2.85)113(2.36)
脑卒中家族史2.5430.111
是25(0.22)5(0.10)
否11 172(99.78)4 793(99.90)
糖尿病史0.0440.833
是3 039(27.14)1 310(27.30)
否8 158(72.86)3 488(72.70)
农村贫困人口0.0170.897
是98(0.88)43(0.90)
否11 099(99.12)4 755(99.10)
血压控制达标1.030.31
是7 293(65.13)3 085(64.30)
否3 904(34.87)1 713(35.70)
超重或肥胖0.3310.565
是6 787(60.61)2 885(60.13)
否4 410(39.39)1 913(39.87)
运动1.0980.295
是6 152(54.94)2 593(54.04)
否5 045(45.06)2 205(45.96)
饮食均衡0.0120.914
是10 253(91.57)4 391(91.52)
否944(8.43)407(8.48)
吸烟0.5850.444
是558(4.98)253(5.27)
否10 639(95.02)4 545(94.73)
饮酒1.0850.297
是412(3.68)193(4.02)
否10 785(96.32)4 605(95.98)
血脂异常0.0960.756
是1 568(14.00)663(13.82)
否9 629(86.00)4 135(86.18)
心脏病史0.7420.389
是369(3.30)171(3.56)
否10 828(96.70)4 627(96.44)
脑卒中史0.0080.93
是575(5.14)248(5.17)
否10 622(94.86)4 550(94.83)
年龄(,岁)72.86±5.8272.98±5.871.2200.223
脉率(次\分)72.78±9.9472.69±9.93-0.5170.605
腰围(cm)86.00±9.6585.80±9.93-1.1870.235
空腹血糖(mmol/L)5.88±1.705.90±1.770.7730.439
), ArticleFig(id=1241102752726700901, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=CN, label=表1, caption=

研究对象基线特征[()/n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
项目训练集验证集χ2/tP
性别0.0010.978
男4 481(40.02)1 919(40.00)
女6 716(59.98)2 879(60.00)
民族0.2190.640
非汉族110(0.98)51(1.06)
汉族11 087(99.02)4 747(98.94)
教育水平1.4550.693
小学及以下5 419(48.40)2 303(48.00)
初中3 458(30.88)1 480(30.85)
高中/中专1 663(14.85)710(14.80)
大专/本科及以上657(5.87)305(6.36)
职业3.1170.077
体力劳动10 878(97.15)4 685(97.64)
脑力劳动319(2.85)113(2.36)
脑卒中家族史2.5430.111
是25(0.22)5(0.10)
否11 172(99.78)4 793(99.90)
糖尿病史0.0440.833
是3 039(27.14)1 310(27.30)
否8 158(72.86)3 488(72.70)
农村贫困人口0.0170.897
是98(0.88)43(0.90)
否11 099(99.12)4 755(99.10)
血压控制达标1.030.31
是7 293(65.13)3 085(64.30)
否3 904(34.87)1 713(35.70)
超重或肥胖0.3310.565
是6 787(60.61)2 885(60.13)
否4 410(39.39)1 913(39.87)
运动1.0980.295
是6 152(54.94)2 593(54.04)
否5 045(45.06)2 205(45.96)
饮食均衡0.0120.914
是10 253(91.57)4 391(91.52)
否944(8.43)407(8.48)
吸烟0.5850.444
是558(4.98)253(5.27)
否10 639(95.02)4 545(94.73)
饮酒1.0850.297
是412(3.68)193(4.02)
否10 785(96.32)4 605(95.98)
血脂异常0.0960.756
是1 568(14.00)663(13.82)
否9 629(86.00)4 135(86.18)
心脏病史0.7420.389
是369(3.30)171(3.56)
否10 828(96.70)4 627(96.44)
脑卒中史0.0080.93
是575(5.14)248(5.17)
否10 622(94.86)4 550(94.83)
年龄(,岁)72.86±5.8272.98±5.871.2200.223
脉率(次\分)72.78±9.9472.69±9.93-0.5170.605
腰围(cm)86.00±9.6585.80±9.93-1.1870.235
空腹血糖(mmol/L)5.88±1.705.90±1.770.7730.439
), ArticleFig(id=1241102752877695853, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=EN, label=Table 2, caption=

Results of monofactor analysis of training set[()/n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
项目脑卒中组非脑卒中组χ2/tP
性别9.8360.002
男266(46.26)4 215(39.68)
女309(53.74)6 407(60.32)
民族1.0420.307
汉族567(98.61)10 520(99.04)
非汉族8(1.39)102(0.96)
教育水平7.1250.068
小学及以下307(53.39)5 112(48.13)
初中165(28.70)3 293(31.00)
高中/中专78(13.57)1 585(14.92)
大专/本科及以上25(4.35)632 (5.87)
职业1.9180.166
体力劳动564(98.09)10 314(97.10)
脑力劳动11(1.91)308(2.90)
脑卒中家族史2.4240.12
是3(0.52)22(0.21)
否572(99.48)10 600(99.79)
糖尿病史0.4620.497
是149(25.91)2 890(27.21)
否426(74.09)7 732(72.79)
农村贫困人口1.8610.173
是8(1.39)90(0.85)
否567(98.61)10 532(99.15)
血压控制达标175.672<0.001
是227(39.48)7 066(66.52)
否348(60.52)3 556(33.48)
超重或肥胖13.431<0.001
是391(68.00)6 409(60.34)
否184(32.00)4 213(39.66)
运动72.47<0.001
是217(37.74)5 935(55.87)
否358(62.26)4 687(44.13)
饮食均衡131.882<0.001
是452(78.61)9 801(92.27)
否123(21.39)821(7.73)
吸烟26.872<0.001
是55(9.57)503(4.74)
否520(90.43)10 119(95.26)
饮酒34.545<0.001
是47(8.17)365(3.44)
否528(91.83)10 257(96.56)
血脂异常50.294<0.001
是138(24.00)1 430(13.46)
否437(76.00)9 192(86.54)
心脏病史517.801<0.001
是114(19.83)256(2.41)
否461(80.17)10 366(97.59)
年龄（,岁）72.89±5.8572.37±5.172.3190.021
脉率（次\分）72.83±9.8171.87±12.041.8690.062
腰围（cm）85.87±9.6088.42±10.20-5.864<0.001
空腹血糖（mmol/L）5.88±1.685.89±1.95-0.0870.931
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训练集单因素分析结果[()/n(%)]

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项目脑卒中组非脑卒中组χ2/tP
性别9.8360.002
男266(46.26)4 215(39.68)
女309(53.74)6 407(60.32)
民族1.0420.307
汉族567(98.61)10 520(99.04)
非汉族8(1.39)102(0.96)
教育水平7.1250.068
小学及以下307(53.39)5 112(48.13)
初中165(28.70)3 293(31.00)
高中/中专78(13.57)1 585(14.92)
大专/本科及以上25(4.35)632 (5.87)
职业1.9180.166
体力劳动564(98.09)10 314(97.10)
脑力劳动11(1.91)308(2.90)
脑卒中家族史2.4240.12
是3(0.52)22(0.21)
否572(99.48)10 600(99.79)
糖尿病史0.4620.497
是149(25.91)2 890(27.21)
否426(74.09)7 732(72.79)
农村贫困人口1.8610.173
是8(1.39)90(0.85)
否567(98.61)10 532(99.15)
血压控制达标175.672<0.001
是227(39.48)7 066(66.52)
否348(60.52)3 556(33.48)
超重或肥胖13.431<0.001
是391(68.00)6 409(60.34)
否184(32.00)4 213(39.66)
运动72.47<0.001
是217(37.74)5 935(55.87)
否358(62.26)4 687(44.13)
饮食均衡131.882<0.001
是452(78.61)9 801(92.27)
否123(21.39)821(7.73)
吸烟26.872<0.001
是55(9.57)503(4.74)
否520(90.43)10 119(95.26)
饮酒34.545<0.001
是47(8.17)365(3.44)
否528(91.83)10 257(96.56)
血脂异常50.294<0.001
是138(24.00)1 430(13.46)
否437(76.00)9 192(86.54)
心脏病史517.801<0.001
是114(19.83)256(2.41)
否461(80.17)10 366(97.59)
年龄（,岁）72.89±5.8572.37±5.172.3190.021
脉率（次\分）72.83±9.8171.87±12.041.8690.062
腰围（cm）85.87±9.6088.42±10.20-5.864<0.001
空腹血糖（mmol/L）5.88±1.685.89±1.95-0.0870.931
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Variable assignment table forbinomial logistic regression analysis

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项目赋值
年龄实测值
性别1=男，0=女
血压控制达标1=是，0=否
心脏病史1=是，0=否
饮食均衡1=是，0=否
血脂异常1=是，0=否
饮酒1=是，0=否
运动1=是，0=否
超重或肥胖1=是，0=否
吸烟1=是，0=否
腰围实测值
), ArticleFig(id=1241102753213240192, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=CN, label=表3, caption=

二元logistics回归变量赋值表

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项目赋值
年龄实测值
性别1=男，0=女
血压控制达标1=是，0=否
心脏病史1=是，0=否
饮食均衡1=是，0=否
血脂异常1=是，0=否
饮酒1=是，0=否
运动1=是，0=否
超重或肥胖1=是，0=否
吸烟1=是，0=否
腰围实测值
), ArticleFig(id=1241102753322292106, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=EN, label=Table 4, caption=

Binomial logistic regression analysis of stroke in elderly patients with hypertension

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项βWald χ2P 值OR值(95%CI)
年龄-0.0174.3770.0360.983(0.967~0.999)
性别(ref:女)
男0.36714.735<0.0011.444(1.197~1.741)
血压控制达标(ref:否)
是-0.87889.286<0.0010.416(0.347~ 0.499)
心脏病史(ref:否)
是1.970227.401<0.0017.173(5.553~9.267)
饮食均衡(ref:否)
是-0.79245.779<0.0010.453(0.360~0.570)
血脂异常(ref:否)
是0.68939.727<0.0011.991(1.607~2.467)
饮酒(ref:否)
是0.4195.3390.0211.521(1.066~2.170)
运动(ref:否)
是-0.75266.410<0.0010.471(0.393~0.565)
超重或肥胖(ref:否)
是0.0450.1800.6711.046(0.849~1.288)
吸烟(ref:否)
是0.1580.7310.3931.171(0.816~1.681)
腰围0.0061.4020.2361.006(0.996~1.016)
), ArticleFig(id=1241102753410372496, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102738776445060, language=CN, label=表4, caption=

年高血压患者患脑卒中二元logistics回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
项βWald χ2P 值OR值(95%CI)
年龄-0.0174.3770.0360.983(0.967~0.999)
性别(ref:女)
男0.36714.735<0.0011.444(1.197~1.741)
血压控制达标(ref:否)
是-0.87889.286<0.0010.416(0.347~ 0.499)
心脏病史(ref:否)
是1.970227.401<0.0017.173(5.553~9.267)
饮食均衡(ref:否)
是-0.79245.779<0.0010.453(0.360~0.570)
血脂异常(ref:否)
是0.68939.727<0.0011.991(1.607~2.467)
饮酒(ref:否)
是0.4195.3390.0211.521(1.066~2.170)
运动(ref:否)
是-0.75266.410<0.0010.471(0.393~0.565)
超重或肥胖(ref:否)
是0.0450.1800.6711.046(0.849~1.288)
吸烟(ref:否)
是0.1580.7310.3931.171(0.816~1.681)
腰围0.0061.4020.2361.006(0.996~1.016)
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老年高血压患者合并脑卒中的影响因素分析及列线图模型构建
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王瑞 , 曾泓辑 , 王雯娟 , 刘雅慧 , 卫姝帆 , 田庆丰
现代预防医学 | 疾病预防控制 2024,51(14): 2620-2627
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现代预防医学 | 疾病预防控制 2024, 51(14): 2620-2627
老年高血压患者合并脑卒中的影响因素分析及列线图模型构建
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王瑞, 曾泓辑, 王雯娟, 刘雅慧, 卫姝帆, 田庆丰
作者信息
  • 郑州大学公共卫生学院社会医学与卫生事业管理教研室，河南 郑州 450000

    • 王瑞（2001—），女，硕士在读，研究方向：卫生事业管理

通讯作者:

田庆丰，E-mail：
Analysis of influencing factors and construction of nomogram model in elderly patients with hypertension complicated with stroke
Rui WANG, Hong-ji ZENG, Wen-juan WANG, Ya-hui LIU, Shu-fan WEI, Qing-feng TIAN
Affiliations
  • Department of Social Medicine and Health Management, School of Public Health,Zhengzhou University, Zhengzhou, Henan 450000, China
出版时间: 2024-07-25 doi: 10.20043/j.cnki.MPM.202312221
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摘要
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目的

基于社区健康调查识别老年高血压患者患脑卒中的影响因素，并根据影响因素建立列线图预测模型。

方法

于2022年采用多阶段抽样的方法抽取河南省郑州市居住半年以上的居民进行社区健康调查，使用单因素和多因素分析筛选老年高血压患者合并脑卒中的影响因素并构建列线图预测模型，ROC曲线、校准曲线、DCA决策曲线用于验证列线图模型的准确性和稳定性。

结果

本次研究共纳入15 995例老年高血压患者，将总体以7:3随机分为训练集与验证集。模型构建：训练集二元logistics分析结果显示，年龄、性别、腰围、血压控制是否达标、是否超重或肥胖、是否运动、是否吸烟、是否饮酒、是否饮食均衡、是否患有血脂异常、是否有心脏病史是患脑卒中的独立影响因素。根据多因素分析结果构建列线图模型。模型验证：训练集和验证集ROC曲线下面积分别为0.748\0.779、校准曲线拟合良好，表明该模型具有良好的预测价值。

结论

本研究建立了一个老年高血压患者合并脑卒中的列线图预测模型，其中包括人口学特征、健康状况和生活方式，该模型具有较好的准确性和稳定性，有助于筛查高危个体，提供临床决策和预防建议。

老年人  /  高血压  /  脑卒中  /  影响因素  /  列线图
Objective

To identify the influencing factors of stroke in elderly patients with hypertension based on community health survey, and establish a nomogram prediction model according to the influencing factors.

Methods

In 2022, the multi-stage sampling method was used to select the residents of Zhengzhou City, Henan province who lived for more than half a year for community health survey, and univariate and multivariate analysis was used to screen the influential factors of elderly hypertension patients complicated with stroke, and a nomogram prediction model was constructed. ROC curve, calibration curve and DCA decision curve are used to verify the accuracy and stability of the nomogram model.

Results

A total of 15 995 elderly patients with hypertension were included in this study. The original data set was randomly divided into a training set and a validation set at 7:3. Model construction: The results of binary logistics analysis of the training set showed that age, gender, waist circumference, blood pressure control, overweight or obesity, exercise, smoking, drinking, balanced diet, dyslipidemia and heart disease were independent influencing factors for stroke. A nomogram model was constructed according to the results of multi-factor analysis. Model validation: the area under the ROC curve of the training set and the verification set is 0.748\0.779 respectively, and the calibration curve is well fitted, indicating that the model has good predictive value.

Conclusion

In this study, we established a nomogram prediction model for elderly hypertensive patients with stroke, including demographic characteristics, health status and lifestyle. The model is accurate and stable, which can help to screen high-risk individuals, provide clinical decision-making and prevention recommendations.

Old people  /  Hypertension  /  Stroke  /  Influencing factors  /  Nomogram
王瑞, 曾泓辑, 王雯娟, 刘雅慧, 卫姝帆, 田庆丰. 老年高血压患者合并脑卒中的影响因素分析及列线图模型构建. 现代预防医学, 2024 , 51 (14) : 2620 -2627 . DOI: 10.20043/j.cnki.MPM.202312221
Rui WANG, Hong-ji ZENG, Wen-juan WANG, Ya-hui LIU, Shu-fan WEI, Qing-feng TIAN. Analysis of influencing factors and construction of nomogram model in elderly patients with hypertension complicated with stroke[J]. Modern Preventive Medicine, 2024 , 51 (14) : 2620 -2627 . DOI: 10.20043/j.cnki.MPM.202312221
正文
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脑卒中是全球第二大死因和第三大致残原因。在过去三十年内，脑卒中患病率和发病率显著增加，近十年内尤为明显[1]。随着年龄增长，动脉血管发生退行性变，血管弹性变差，老年人脑卒中发病率变高。许多研究表明，高血压是脑卒中的主要影响因素[2-4]，具体情况受到许多因素的影响。老年高血压患者作为脑卒中高风险人群，现存研究大多是基于住院人群开展的调查，样本量较小，调查对象一般为住院人群，预测模型所覆盖变量不够全面或缺乏外部验证[5]。基于此，本研究拟对老年高血压人群开展社区健康调查，开发用于预测老年高血压患者合并脑卒中的列线图模型，该预测模型将有助于筛查和识别脑卒中的高危人群，并提供临床决策和预防建议。
1　对象和方法
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1.1　研究对象
本研究数据源于2022年河南省郑州市社区人群健康调查，采用多阶段抽样的方法，抽取郑州市 12个区县≥65岁的居民进行健康体检。抽样共划分为3个阶段，第一阶段:初级抽样单位为市辖 12个区县，包括6个区、5个县级市、1个县。第二阶段:采用简单随机抽样，每个区县抽取3个乡镇(街道)。第三阶段:采用简单随机抽样，每个乡镇(街道)老年人抽取比例为10%。本次研究共登记16 112名老年高血压患者，剔除不完整资料517名，共纳入15 595名居民，所有研究对象知情同意并自愿参与本研究。纳入标准：（1）居住半年以上的户籍和非户籍居民；（2）年龄≥65岁；（3）入选高血压患者均符合《中国高血压防治指南（2018版）》高血压诊断标准[6]。排除标准：（1）不配合完全可信的各项检查者或严重认知障碍的患者；（2）患有脑卒中外其他脑部疾病。
本研究经郑州大学生命科学伦理审查委员会审核批准（批号：ZZUIRB2022-07）。
1.2　资料收集
1.2.1　一般资料收集
通过问卷调查和自我报告的方式收集居民相关资料，包括（1）一般资料：年龄、性别、民族、教育水平、职业、是否农村贫困人口等；（2）生活方式：饮食均衡、吸烟、饮酒、运动等；（3）健康状况：糖尿病史、心脏病史、脑卒中家族史等。
1.2.2　体格和实验室检查
体格检查包括身高、体重、腰围、脉率、舒张压(DBP)、收缩压(SBP)等测量，实验室检查包括空腹血糖、总胆固醇、三酰甘油、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇等检测。
1.2.3　诊断标准及定义
(1)高血压：SBP≥140 mmHg和 (或) DBP≥90 mmHg；或患者自述有高血压既往史，目前正在服药。(2)血压控制达标：患者SBP<140 mmHg且DBP<90 mmHg为血压控制达标。 (3)脑卒中：研究对象自报并且经专业影响学资料和神经内科医师确诊为脑卒中患者。(4)饮酒：过去一年内，无论喝过何种酒类，均定义为饮酒[7]。(5)运动:每周运动一次及以上且每次运动时间大于或等于20 min。(6)超重或肥胖：体质指数≥24 kg/m2[8]。(7)血脂异常：总胆固醇≥5.2 mmol/L、三酰甘油≥ 1.7 mmol/L、高密度脂蛋白胆固醇≥3.4 mmol/L、低密度脂蛋白胆固醇≤1.0 mmol/L,上述血脂指标一项及以上异常则可诊断为血脂异常[9]。(8)糖尿病史：存在糖尿病史。(9)心脏病史：存在心脏病史，如冠心病和房颤等。(10)脑卒中家族史：父母和兄弟姐妹中至少1人曾患脑卒中。
1.3　统计学分析
采用SPSS 21.0软件进行数据处理和统计学分析，计量资料以（）表示，采用t检验；计数资料用频数与率或构成比表示，组间比较采用χ2检验。以7:3将总体随机分成训练集和验证集。训练集logistic回归用于筛选影响因素，以构建预测模型。使用R 4.3.0软件构建模型，训练集和验证集受试者操作员特征（ROC）曲线、曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）用于评估列线图预测价值。检验水准α=0.05。
2　结果
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2.1　研究对象基线特征
本次研究共纳入15 995例老年高血压患者，脑卒中患病率为5.28%（95%CI：4.93%，5.63%）。将总体按7:3随机分成训练集和验证集。训练集和验证集变量差异均无统计学意义（P>0.05）。见表1。
2.2　训练集单因素分析和二元logistics回归分析
训练集单因素分析显示，脑卒中组和非脑卒中组在年龄、性别、腰围、血压控制是否达标、是否超重或肥胖、是否运动、是否吸烟、是否饮酒、是否饮食均衡、是否患有血脂异常、是否患有心脏病等方面的差异具有统计学意义（P<0.05），见表2。
以老年高血压患者是否患脑卒中为因变量，以年龄、性别、腰围、血压控制是否达标、是否超重或肥胖、是否运动、是否吸烟、是否饮酒、是否饮食均衡、是否患有血脂异常、是否有心脏病史为自变量，进行二元logistics回归分析（变量赋值见表3）。结果显示年龄、性别、血压控制是否达标、是否有心脏病史、是否饮食均衡、是否血脂异常、是否饮酒、是否运动为老年高血压患者患脑卒中的相关因素。见表4。
2.3　列线图的开发和验证
基于二元logistics回归分析筛选出的老年高血压患者患脑卒中的8个独立影响因素，使用R软件构建列线图模型（图1）。运用pROC包计算AUC评估模型的预测价值：训练集AUC为0.748(95%CI：0.725~0.771，P<0.001)，特异度0.808, 灵敏度0.603，模型准确性良好。验证集中AUC为0.779（95%CI：0.747~0.809）（图2）。使用PredictABEL包对模型的准确度进行评价，验证集校准曲线中的实际概率和预测概率拟合良好，表明预测模型具有较好的准确性和稳定性（图3）。使用rmda包做DCA曲线，验证集DCA曲线显示当风险阈值在0~0.40之间时，该模型具有净收益（图4）。
3　讨论
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许多研究表明，衰老增加了脑血管疾病的易感性[10]。然而，有研究显示70岁以下的人群中，年龄标准化发病率和患病率逐渐增加，说明脑卒中发病逐渐年轻化[1]。据报道，我国心脑血管发病年轻化趋势明显，55~69岁是发病高峰[11]。本研究显示年龄是65岁以上老年高血压患者患脑卒中的相关因素(OR=0.983)，这并不代表年龄越大患脑卒中发病概率降低，可能与发病年龄结构前移有关、不良生活方式和心血管疾病等有关[12]。有研究指出男性脑卒中发病率高于女性[13]，这与本研究结果一致，男性是老年高血压患者患脑卒中的相关因素(OR=1.444)。高血压、糖尿病、心血管病、吸烟、饮酒等是脑卒中的高风险因素，男性高风险比例人群高于女性，使得男性发病概率高于女性[14]。
脑卒中的一级预防指出，降低高血压患者的血压对预防缺血性卒中非常有效：收缩压每降低10mmHg，舒张压每降低5 mmHg，脑卒中风险降低41%[15]，本研究结果显示血压控制达标的患者患脑卒中风险比不达标患者低（OR=0.416），降低血压和减少脑卒中发病风险之间呈线性关系。可能的机制是：高血压会损害血管内膜并激活凝血系统，进而导致脑部血管狭窄并最终可能导致脑卒中。适量运动与健康生活方式相结合，可以将脑卒中的发病概率降低55%[16]。每周坚持适量身体活动的高血压患者脑卒中风险低于缺乏运动的患者（OR=0.471），这与以前的研究一致[17]。运动能够改善血管弹性，减少血栓形成，同时可预防动脉硬化，降低脑卒中风险。研究表明，改变饮食是预防心脑血管疾病过早死亡的最有效方法[18]，这与我们的研究结果一致（OR=0.453）明酒精摄入量与血压呈线性增加，每周280克的酒精会使收缩压升高约5毫米汞柱，缺血性脑卒风险增加约15%\出血性脑卒中风险增加约30%[19]，这与本研究结果一致（OR=1.521）。
本研究结果显示伴有心脏病的高血压患者患脑卒中风险是不患心脏病的7.173倍。其中，冠心病与脑卒中具有相似的发病机制和共同的影响因素，经常伴随发生：老年脑卒中患者中56%合并有冠心病，32%的冠心病患者合并有脑卒中[20]；心房颤动可通过大脑动脉心源性栓塞引起缺血性脑卒中，其栓塞体积大，致命性、致残性高[21]。血脂异常会使脑卒中发病风险增加（OR=1.991），研究表明TG和LDL较高会增加脑卒中首发风险[22]，这与我们的研究一致。低密度脂蛋白长期较高会导致颈动脉粥样硬化，易引起脑卒中[23]。
脑卒中影响因素往往相互作用，列线图可以将这些复杂的因素整合，并区分出风险人群[24]。相较于以前的模型，本次建立的列线图包括人口学特征、健康状况、生活方式，对于识别尚未发生脑卒中的高风险个体是全面而敏感的，该列线图模型具有较好的准确性和稳定性，且所含预测变量数量较小，计算方法易于掌握。然而，本研究基于横断面数据，无法证明因果关系。且一些变量是自我报变量，缺少影像学资料支持，存在偏倚的可能性。且本研究基于郑州市社区健康调查，缺乏代表性，有待后续研究完善。
基金
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  • 国家重点研发计划资助项目(2020YFC2006100)
文献
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doi: 10.20043/j.cnki.MPM.202312221
  • 接收时间：2023-12-13
  • 首发时间：2026-03-18
  • 出版时间：2024-07-25
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  • 收稿日期：2023-12-13
基金
国家重点研发计划资助项目(2020YFC2006100)
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    郑州大学公共卫生学院社会医学与卫生事业管理教研室，河南 郑州 450000

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田庆丰，E-mail：
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2种不同金属材料的力学参数
科
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
Percentage of total
species (%)
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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