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Article(id=1240929921468527383, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240929920461886112, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202312263, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1702483200000, receivedDateStr=2023-12-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773790348634, onlineDateStr=2026-03-18, pubDate=1717948800000, pubDateStr=2024-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773790348634, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773790348634, creator=13701087609, updateTime=1773790348634, updator=13701087609, issue=Issue{id=1240929920461886112, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='11', pageStart='1921', pageEnd='2112', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1773790348400, creator=13701087609, updateTime=1773827281389, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241084828704109275, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240929920461886112, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241084828704109276, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240929920461886112, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1938, endPage=1943, ext={EN=ArticleExt(id=1240929921732768538, articleId=1240929921468527383, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of high-risk factors of preeclampsia in Urumqi and construction and verification of Nomogram prediction model, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=

Objective To investigate the high-risk factors for preeclampsia in Urumqi, establish a risk prediction model, and validate its effectiveness. Methods A total of 6 138 pregnant women undergoing antenatal examination from February 2021 to February 2023 were collected through the integrated platform of clinical research in Urumqi Maternal and Child Health Hospital. General data, pregnancy history, and pregnancy complications were recorded. The patients were randomly divided into a modeling group (n=4 308) and a validation group (n=1 830) in a 7:3 ratio. The logistic regression model analysis method was used to construct a Nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical applicability of the prediction model. Results The advanced age (OR=1.887, 95%CI: 1.535-2.319), overweight (OR=3.221, 95%CI: 2.699-3.844), family history (OR=1.575, 95%CI: 1.254-1.979), hypertension history (OR=17.552, 95%CI: 7.021-43.882), hypertension complicating pregnancy (OR=1.827, 95%CI: 1.388-2.405), preeclampsia history (OR=6.216, 95%CI: 3.578-10.770), and primiparity (OR=2.384, 95%CI:1.777-3.200) were identified as independent risk factors of preeclampsia in Urumqi. The results of ROC curve test showed that the area under the curve and 95%CI of the model group were 0.762 (0.742-0.782), and for the validation group were 0.765 (0.734-0.796). The calibration curve and DCA curve demonstrated that the Nomogram had good accuracy and clinical applicability. Conclusion The established nomogram prediction model exhibited good predictive ability and provides an important reference for clinical practitioners to identify high-risk patients. Early intervention measures should be implemented to prevent further progression and improve maternal and infant outcomes.

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目的 探索乌鲁木齐市子痫前期发病的高危因素，建立风险预测模型并验证。方法 通过乌鲁木齐市妇幼保健院临床科研一体化平台回顾性收集2021年2月—2023年2月产检孕妇6 138例，收集孕妇的一般资料、孕产史、妊娠期合并症，按7：3随机划分建模组4 308例与验证组1 830例。Logistic回归模型分析方法构建Nomogram预测模型，并采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估列线图预测模型的区分度、校准度和临床实用性。结果 高龄(OR=1.887，95%CI: 1.535～2.319)、超重(OR=3.221，95%CI: 2.699～3.844)、家族史(OR=1.575，95%CI: 1.254～1.979)、高血压病史(OR=17.552，95%CI: 7.021～43.882)、妊娠期高血压(OR=1.827，95%CI: 1.388～2.405)、子痫前期病史(OR=6.216，95%CI: 3.578～10.770)、首次怀孕(OR=2.384，95%CI: 1.777～3.200)均为乌鲁木齐市子痫前期发病的独立危险因素。ROC曲线检验结果表明，建模组和验证组曲线下面积及95%CI分别为0.762(0.742～0.782)、0.765(0.734～0.796)。校准曲线和DCA曲线显示列线图具有良好的精准性和临床实用性。结论 本研究建立的列线图预测模型具有良好的预测能力，为临床医护人员筛选高危患者提供重要的参考，应尽早采取干预措施预防其进一步进展，改善母婴结局。

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丁桂凤，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=Ccy9hX5AWku0lYi6YFTrOQ==, magXml=FWHEayRq34+02EudRB5GsQ==, pdfUrl=null, pdf=iYpHQBaiaTPn9Y+WUeF6sg==, pdfFileSize=719495, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=MoFopVo5XgZdsUDK51LiKQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=TcVTH11o3voPRHjYM6YDaA==, mapNumber=null, authorCompany=null, fund=null, authors=

程备（1998—），女，硕士在读，研究方向：妇幼保健研究

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程备（1998—），女，硕士在读，研究方向：妇幼保健研究

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Chinese Journal of Obstetric Emergency: Electronic Edition, 2021, 10(3): 133-137., articleTitle=Prevention of preeclampsia in its occurrence and development, refAbstract=null)], funds=[Fund(id=1240950876907557623, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, awardId=2022D01A29, language=CN, fundingSource=新疆维吾尔自治区自然科学基金项目(2022D01A29), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1240950874286117580, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, xref=1., ext=[AuthorCompanyExt(id=1240950874294506189, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, companyId=1240950874286117580, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region 830000, China), AuthorCompanyExt(id=1240950874302894798, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, companyId=1240950874286117580, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.新疆医科大学公共卫生学院，新疆 乌鲁木齐 830000)]), AuthorCompany(id=1240950874365809359, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, xref=2., ext=[AuthorCompanyExt(id=1240950874378392272, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, companyId=1240950874365809359, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.乌鲁木齐市妇幼保健院，新疆 乌鲁木齐 830000)])], figs=[ArticleFig(id=1240950875741541097, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=EN, label=Figure 1, caption=A Nomogram prediction model for the risk of preeclampsia, figureFileSmall=oVa1HR6sQm3+bnR+1jxTpg==, figureFileBig=CZ4fqSy/B0Jbhl10LCcO7Q==, tableContent=null), ArticleFig(id=1240950875804455658, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=CN, label=图1, caption=子痫前期发生风险的列线图预测模型, figureFileSmall=oVa1HR6sQm3+bnR+1jxTpg==, figureFileBig=CZ4fqSy/B0Jbhl10LCcO7Q==, tableContent=null), ArticleFig(id=1240950875905118955, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=EN, label=Figure 2, caption=ROC curve, calibration curve, and DCA curve of the verification group, figureFileSmall=upifYqJ0IimCfIXFmQUKVQ==, figureFileBig=LuMxXIGrQ021wSfYUFlhmw==, tableContent=null), ArticleFig(id=1240950875976422124, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=CN, label=图2, caption=验证组ROC曲线、校准曲线、DCA曲线

注：图A为ROC曲线；图B校准曲线；图C为DCA曲线。

, figureFileSmall=upifYqJ0IimCfIXFmQUKVQ==, figureFileBig=LuMxXIGrQ021wSfYUFlhmw==, tableContent=null), ArticleFig(id=1240950876056113901, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=EN, label=Table 1, caption=

Results of univariate comparative analysis of general data affecting the onset of preeclampsia ［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量病例组对照组χ2值P值
民族121.509<0.001
汉族354(43.9)2 271(64.9)
其他民族453(56.1)1 230(35.1)
血型10.669<0.014
A272(33.7)1 092(31.2)
B243(30.1)978(27.9)
AB208(25.8)1 107(31.6)
O84(10.4)324(9.3)
RH血型0.2860.593
阳性776(96.2)3 380(96.5)
阴性31(3.8)121(3.5)
年龄（岁）51.435<0.001
≥35231(28.6)613(17.5)
<35576(71.4)2 888(82.5)
BMI（kg/m2）352.321<0.001
<18.546(5.7)300(8.6)
18.5～<24.0312(38.7)2 334(66.7)
24.0～<28.0205(25.4)551(15.7)
≥28244(20.2)316(9)
学历8.3280.040
高中及以下207(25.7)890(25.4)
专科241(30.6)956(27.3)
本科322(39.9)1 445(41.3)
研究生31(4.9)210(6.0)
), ArticleFig(id=1240950876127417070, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=CN, label=表1, caption=

影响子痫前期发病的一般资料单因素比较分析结果［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量病例组对照组χ2值P值
民族121.509<0.001
汉族354(43.9)2 271(64.9)
其他民族453(56.1)1 230(35.1)
血型10.669<0.014
A272(33.7)1 092(31.2)
B243(30.1)978(27.9)
AB208(25.8)1 107(31.6)
O84(10.4)324(9.3)
RH血型0.2860.593
阳性776(96.2)3 380(96.5)
阴性31(3.8)121(3.5)
年龄（岁）51.435<0.001
≥35231(28.6)613(17.5)
<35576(71.4)2 888(82.5)
BMI（kg/m2）352.321<0.001
<18.546(5.7)300(8.6)
18.5～<24.0312(38.7)2 334(66.7)
24.0～<28.0205(25.4)551(15.7)
≥28244(20.2)316(9)
学历8.3280.040
高中及以下207(25.7)890(25.4)
专科241(30.6)956(27.3)
本科322(39.9)1 445(41.3)
研究生31(4.9)210(6.0)
), ArticleFig(id=1240950876202914543, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=EN, label=Table 2, caption=

Results of univariate analysis of previous history and present history influencing preeclampsia onset ［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量子痫前期组对照组χ2值P值
高血压家族史124.338<0.001
是180(22.3)419(12)
否627(77.7)3 082(88)
高血压病史185.519<0.001
是50(6.2)6(0.2)
否757(93.8)3 495(99.8)
糖尿病病史10.2800.010
是9(1.1)10(0.3)
否798(98.9)3 491(99.7)
子痫前期病史67.818<0.001
是42(5.2)34(1)
否765(94.8)3 467(99)
妊娠期糖尿病8.3590.004
是247(30.6)897(25.6)
否560(69.4)2 604(74.4)
妊娠期高血压127.130<0.001
是143(1.7)202(5.8)
否664(82.3)3 299(94.2)
妊娠期合并贫血0.9670.326
是196(21.1)909(26)
否611(75.7)2 592(74)
妊娠合并甲减0.0830.774
是113 (14)504(14.4)
否694(86)2 997(85.6)
阴道炎0.0030.957
是111(13.8)479(13.7)
否696(86.2)3 022(86.3)
), ArticleFig(id=1240950876278412016, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=CN, label=表2, caption=

影响子痫前期发病的既往史与现病史单因素比较分析结果［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量子痫前期组对照组χ2值P值
高血压家族史124.338<0.001
是180(22.3)419(12)
否627(77.7)3 082(88)
高血压病史185.519<0.001
是50(6.2)6(0.2)
否757(93.8)3 495(99.8)
糖尿病病史10.2800.010
是9(1.1)10(0.3)
否798(98.9)3 491(99.7)
子痫前期病史67.818<0.001
是42(5.2)34(1)
否765(94.8)3 467(99)
妊娠期糖尿病8.3590.004
是247(30.6)897(25.6)
否560(69.4)2 604(74.4)
妊娠期高血压127.130<0.001
是143(1.7)202(5.8)
否664(82.3)3 299(94.2)
妊娠期合并贫血0.9670.326
是196(21.1)909(26)
否611(75.7)2 592(74)
妊娠合并甲减0.0830.774
是113 (14)504(14.4)
否694(86)2 997(85.6)
阴道炎0.0030.957
是111(13.8)479(13.7)
否696(86.2)3 022(86.3)
), ArticleFig(id=1240950876362298097, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=EN, label=Table 3, caption=

Results of comparative analysis of single factors affecting the maternal history of preeclampsia ［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量子痫前期组对照组χ2值P值
流产史114.262<0.001
是335(41.5)2 174(62.1)
否472(58.5)1 327(37.9)
剖宫史1.5920.207
是150(18.6)720(20.6)
否657(81.4)2 781(79.4)
产次≥26.2630.012
是55(6.8)337(9.6)
否752(93.2)3 164(90.4)
首次怀孕134.729<0.001
是373(46.2)895(25.6)
否434(53.8)2 606(74.4)
), ArticleFig(id=1240950876437795570, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=CN, label=表3, caption=

影响子痫前期发病孕产史单因素比较分析结果［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量子痫前期组对照组χ2值P值
流产史114.262<0.001
是335(41.5)2 174(62.1)
否472(58.5)1 327(37.9)
剖宫史1.5920.207
是150(18.6)720(20.6)
否657(81.4)2 781(79.4)
产次≥26.2630.012
是55(6.8)337(9.6)
否752(93.2)3 164(90.4)
首次怀孕134.729<0.001
是373(46.2)895(25.6)
否434(53.8)2 606(74.4)
), ArticleFig(id=1240950876513293043, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=EN, label=Table 4, caption=

Independent variable assignment table

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量赋值
年龄（岁）1=≥35，2=<35
入院记录文化程度1=高中及以下, 2=大专,3=本科4=研究生
超重1=是，2=否
家族史1=是，2=否
高血压病史1=是，2=否
妊娠期高血压1=是，2=否
子痫前期病史1=是，2=否
首次怀孕1=是，2=否
), ArticleFig(id=1240950876580401908, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=CN, label=表4, caption=

研究自变量赋值表

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量赋值
年龄（岁）1=≥35，2=<35
入院记录文化程度1=高中及以下, 2=大专,3=本科4=研究生
超重1=是，2=否
家族史1=是，2=否
高血压病史1=是，2=否
妊娠期高血压1=是，2=否
子痫前期病史1=是，2=否
首次怀孕1=是，2=否
), ArticleFig(id=1240950876672676597, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=EN, label=Table 5, caption=

Logistic regression analysis of multiple factors influencing the onset of preeclampsia

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Wald χ2值P值OR值(95%CI)
高龄0.6350.08974.453<0.0011.887（1.535～2.319）
超重1.1700.090168.059<0.0013.221（2.699～3.844）
本科1.000
高中及以下文化程度0.2450.1134.695<0.051.278（1.024～1.595）
大专0.2470.1065.449<0.051.280（1.040～1.574）
研究生-0.5040.2225.145<0.050. 604（0.390～0.934）
家族史0.4540.11615.242<0.0011.575（1.254～1.979）
高血压病史2.8650.46837.558<0.00117.552（7.021～43.882）
妊娠期高血压0.6030.14018.508<0.0011.827（1.388～2.405）
子痫前期病史1.8720.28042.442<0.0016.216（3.578～10.770）
首次怀孕0.8690.15033.509<0.0012.384（1.777～3.200）
), ArticleFig(id=1240950876756562678, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240929921468527383, language=CN, label=表5, caption=

影响子痫前期发病的多因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Wald χ2值P值OR值(95%CI)
高龄0.6350.08974.453<0.0011.887（1.535～2.319）
超重1.1700.090168.059<0.0013.221（2.699～3.844）
本科1.000
高中及以下文化程度0.2450.1134.695<0.051.278（1.024～1.595）
大专0.2470.1065.449<0.051.280（1.040～1.574）
研究生-0.5040.2225.145<0.050. 604（0.390～0.934）
家族史0.4540.11615.242<0.0011.575（1.254～1.979）
高血压病史2.8650.46837.558<0.00117.552（7.021～43.882）
妊娠期高血压0.6030.14018.508<0.0011.827（1.388～2.405）
子痫前期病史1.8720.28042.442<0.0016.216（3.578～10.770）
首次怀孕0.8690.15033.509<0.0012.384（1.777～3.200）
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乌鲁木齐市子痫前期发病的高危因素分析及Nomogram预测模型的构建与验证
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程备 1 , 段茉莉 2 , 丁桂凤 2
现代预防医学 | 流行病与统计方法 2024,51(11): 1938-1943
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现代预防医学 | 流行病与统计方法 2024, 51(11): 1938-1943
乌鲁木齐市子痫前期发病的高危因素分析及Nomogram预测模型的构建与验证
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程备1, 段茉莉2, 丁桂凤2
作者信息
  • 1.新疆医科大学公共卫生学院，新疆 乌鲁木齐 830000
  • 2.乌鲁木齐市妇幼保健院，新疆 乌鲁木齐 830000

    • 程备（1998—），女，硕士在读，研究方向：妇幼保健研究

通讯作者:

丁桂凤，E-mail:
Analysis of high-risk factors of preeclampsia in Urumqi and construction and verification of Nomogram prediction model
Bei CHENG1, Mo-li DUAN2, Gui-feng DING2
Affiliations
  • School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region 830000, China
出版时间: 2024-06-10 doi: 10.20043/j.cnki.MPM.202312263
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摘要
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目的 探索乌鲁木齐市子痫前期发病的高危因素，建立风险预测模型并验证。方法 通过乌鲁木齐市妇幼保健院临床科研一体化平台回顾性收集2021年2月—2023年2月产检孕妇6 138例，收集孕妇的一般资料、孕产史、妊娠期合并症，按7：3随机划分建模组4 308例与验证组1 830例。Logistic回归模型分析方法构建Nomogram预测模型，并采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估列线图预测模型的区分度、校准度和临床实用性。结果 高龄(OR=1.887，95%CI: 1.535～2.319)、超重(OR=3.221，95%CI: 2.699～3.844)、家族史(OR=1.575，95%CI: 1.254～1.979)、高血压病史(OR=17.552，95%CI: 7.021～43.882)、妊娠期高血压(OR=1.827，95%CI: 1.388～2.405)、子痫前期病史(OR=6.216，95%CI: 3.578～10.770)、首次怀孕(OR=2.384，95%CI: 1.777～3.200)均为乌鲁木齐市子痫前期发病的独立危险因素。ROC曲线检验结果表明，建模组和验证组曲线下面积及95%CI分别为0.762(0.742～0.782)、0.765(0.734～0.796)。校准曲线和DCA曲线显示列线图具有良好的精准性和临床实用性。结论 本研究建立的列线图预测模型具有良好的预测能力，为临床医护人员筛选高危患者提供重要的参考，应尽早采取干预措施预防其进一步进展，改善母婴结局。

子痫前期  /  高危因素  /  风险预测模型  /  校准曲线

Objective To investigate the high-risk factors for preeclampsia in Urumqi, establish a risk prediction model, and validate its effectiveness. Methods A total of 6 138 pregnant women undergoing antenatal examination from February 2021 to February 2023 were collected through the integrated platform of clinical research in Urumqi Maternal and Child Health Hospital. General data, pregnancy history, and pregnancy complications were recorded. The patients were randomly divided into a modeling group (n=4 308) and a validation group (n=1 830) in a 7:3 ratio. The logistic regression model analysis method was used to construct a Nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical applicability of the prediction model. Results The advanced age (OR=1.887, 95%CI: 1.535-2.319), overweight (OR=3.221, 95%CI: 2.699-3.844), family history (OR=1.575, 95%CI: 1.254-1.979), hypertension history (OR=17.552, 95%CI: 7.021-43.882), hypertension complicating pregnancy (OR=1.827, 95%CI: 1.388-2.405), preeclampsia history (OR=6.216, 95%CI: 3.578-10.770), and primiparity (OR=2.384, 95%CI:1.777-3.200) were identified as independent risk factors of preeclampsia in Urumqi. The results of ROC curve test showed that the area under the curve and 95%CI of the model group were 0.762 (0.742-0.782), and for the validation group were 0.765 (0.734-0.796). The calibration curve and DCA curve demonstrated that the Nomogram had good accuracy and clinical applicability. Conclusion The established nomogram prediction model exhibited good predictive ability and provides an important reference for clinical practitioners to identify high-risk patients. Early intervention measures should be implemented to prevent further progression and improve maternal and infant outcomes.

Preeclampsia  /  High-risk factors  /  Risk prediction model  /  Calibration curve
程备, 段茉莉, 丁桂凤. 乌鲁木齐市子痫前期发病的高危因素分析及Nomogram预测模型的构建与验证. 现代预防医学, 2024 , 51 (11) : 1938 -1943 . DOI: 10.20043/j.cnki.MPM.202312263
Bei CHENG, Mo-li DUAN, Gui-feng DING. Analysis of high-risk factors of preeclampsia in Urumqi and construction and verification of Nomogram prediction model[J]. Modern Preventive Medicine, 2024 , 51 (11) : 1938 -1943 . DOI: 10.20043/j.cnki.MPM.202312263
正文
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子痫前期是妊娠期所特有的高血压疾病，全球发病率为2%～8%[1]。子痫前期的发病机制较为复杂，临床症状也较为复杂多样，可影响多个器官，不同妇女不同程度，严重程度呈动态性持续进展[2]。由于目前对于子痫前期的病因尚未完全阐明，现普遍认为子痫前期与胎盘缺血缺氧有关，子宫胎盘缺血缺氧是子痫前期的主要机制，胎盘形成时滋养层浅侵和螺旋动脉的不完全转化是子痫前期的标志，一直应用于子痫前期二阶段学说。第一阶段为异常胎盘形成，是由滋养层的浅侵袭引起的，导致螺旋动脉重塑不充分。第二阶段为母体综合征的发展，第一阶段被认为是导致第二阶段的原因，母体对内皮功能障碍的反应以及血管生成因子和抗血管生成因子之间的失衡，从而导致疾病的临床特征[3]。其危险因素较多，包括高龄、家族史、超重、既往子痫前期病史等[4]，会引起严重并发症，造成母儿死亡等严重不良妊娠结局。本研究旨在筛选乌鲁木齐市子痫前期的危险因素并建立相关列线图预测模型，有助于早期识别乌鲁木齐市子痫前期的风险，采取相应的预防措施以降低各项危险因素带来的负面效应，并对此类高危因素进行针对性的干预，降低子痫前期的发生率，为临床医护人员筛选高危患者提供重要的参考。
1　资料与方法
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1.1　资料来源
通过乌鲁木齐市妇幼保健院临床科研一体化平台回顾性收集2021年2月—2023年2月期间在妇产科收治的孕妇6 138例，按7∶3随机划分建模组4 308例与验证组1 830例。本次研究已获得乌鲁木齐市妇幼保健院医学伦理委员会批准（XJFYLL2021032）。
1.2　方法
1.2.1　纳入标准
（1）符合子痫前期的诊断标准（第九版妇产科学）[5]；（2）居住地为乌鲁木齐市；（3）单胎妊娠；（4）临床资料完整。
1.2.2　排除标准
运用辅助生殖技术怀孕者、肿瘤、心脏疾病、精神病、原发性高血压、肾病、血液系统疾病。
1.2.3　观察指标
民族、血型、年龄、身体质量指数(BMI)、文化程度、产次、自然流产史、首次怀孕、高血压家族史、妊娠期糖尿病、妊娠期合并贫血、妊娠期高血压疾病、妊娠期合并甲状腺功能减退、子痫前期病史、阴道炎、孕妇高血压病史、糖尿病病史。
1.3　统计学分析
应用SPSS 26. 0进行数据分析，单因素分析采用χ2检验，将单因素比较具有统计学意义的影响因素纳入到多因素logistic回归中进行分析，进行指标筛选，通过R语言构建Nomogram预测模型，并以列线图展示。并采用受试者工作特征（ROC）曲线下面积（AUC）、Hosmer-Lemeshow拟合优度检验、校准曲线以及决策曲线（DCA）评估列线图评价预测模型效能。检验水准α=0.05。
2　结果
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2.1　建模组影响子痫前期发病的一般资料单因素比较
建模组根据是否发生子痫前期进一步分为子痫前期组及对照组，分别为807和3 501例。对照组和子痫前期组在民族、血型、BMI、高龄、文化程度方面进行比较，差异均具有统计学意义（P<0.05）。见表1。
2.2　建模组影响子痫前期发病的既往史与现病史单因素比较
对照组和子痫前期组在高血压病史、高血压家族史、子痫前期病史、糖尿病病史、妊娠期糖尿病、妊娠期高血压疾病方面进行比较，差异有统计学意义（P<0.05）。见表2。
2.3　建模组影响子痫前期发病孕产史单因素比较
病例组与对照组在剖宫史、流产史、产次≥2、首次怀孕方面进行比较，差异有统计学意义（P<0.05）。见表3。
2.4　建模组影响子痫前期发病的多因素分析
以研究对象是否发生子痫前期作为因变量，将单因素比较差异具有统计学意义的因素作为自变量进行多因素logistic回归分析，采用双侧检验，纳入标准和排除标准为0.05、0.10。影响子痫前期发病的高危因素包括超重、高龄、家族史、妊娠期高血压疾病、高血压病史、子痫前期病史、首次怀孕、高中及以下文化程度。见表4、5。
2.5　子痫前期列线图预测模型的建立与验证
2.5.1　风险预测模型的构建
基于上述logistic回归分析筛选出有统计学意义(P<0.05)的变量放入R软件中建立列线图预测模型，各个危险因素对应的分值为影响子痫前期发生的权重，各因素分值相加对应的风险为个体出现子痫前期的概率，见图1。
2.5.2　风险预测模型的验证
结果显示建模组AUC及95%CI为0.763(0.744～0.781)，验证组AUC及95%CI为0.765(0.734～0.796)，其AUC值所在范围在0.7～0.9之间，表明该模型具有良好的预测能力，有较好的准确性。通过使用Bootstrap法重复抽样1 000次进行内部验证，绘制校准曲线。Hosmer-Lemeshow在训练集和验证集P值均大于0.05（χ2=9.742，P= 0.204；χ2=7.105，P=0.213），校准曲线与理想曲线走形较为一致，表明预测结果与观测结果一致性较好。采用DCA对预测模型的临床实用性进行评价，虚线为预测模型的净获益率，斜线为所有样本均为子痫前期患者，所有人接受干预的净获益率，灰色横线为所有样本均为非子痫前期患者，所有人均不采取干预措施，净获益为0，在三种曲线所相交的范围内都可获益。本研究训练集和验证集均位于All线及None线右上方，表明子痫前期发病风险列线图预测模型的临床实用价值较好。见图2。综上所述，本研究所构建的列线图模型区分度、校准度和临床实用性均表现良好，具有良好的的预测能力。
3　讨论
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子痫前期是一种全身性综合征疾病，表现为妊娠20周以后出现蛋白尿、高血压和水肿等。它可以表现为一种轻微的疾病，进展缓慢，或者迅速发展到危及生命的状况[6]。尽管现阶段进行了深入的研究，仍然无法充分预测、治疗或预防子痫前期[7]。目前没有治愈性治疗，唯一确定的治疗方法是娩出胎儿和胎盘才能缓解母亲的症状[8]。有研究发现子痫前期发病与营养因素、炎症免疫因素、遗传因素有关[9-10]。本研究多因素分析发现，乌鲁木齐市子痫前期发病的高危因素包括高龄、超重、家族史、高血压病史、妊娠期高血压疾病、子痫前期病史、首次怀孕、高中及以下学历。
我国自二孩和三孩政策放开后，高龄孕产妇的比例不断增加，高龄是子痫前期发病的独立危险因素，本研究发现，乌鲁木齐市子痫前期中高龄孕妇占比为28.6%(OR=1.887，95%CI: 1.535～2.319)，随着孕妇妊娠年龄的增大，子痫前期的发病风险也呈现逐步上升的趋势[11]。随着孕妇年龄的增加，患基础疾病的概率会增加，孕妇的血管也会随着年龄的增加而增大动脉硬化的概率[12]。随着三孩政策的实施，二胎及三胎孕妇数量明显增加，既往有子痫前期病史的孕妇再次妊娠发生子痫前期的风险增加(OR=6.216，95%CI: 3.578～10.770)，子痫前期病史是子痫前期再发的独立危险因素，与陈远婷等[13]学者的研究结果一致。
目前,普遍认为孕前肥胖是引起子痫前期的主要独立危险因素[14]。本研究发现，乌鲁木齐市子痫前期中BMI≥24 kg/m2的孕妇占比超过45%，新疆地区为多民族聚居区域，由于气候环境等原因，形成了独特的饮食结构，高脂肪、高碳水化合物的饮食摄入，喜食甜食、腌制肉食以及奶茶，而蔬菜水果摄入较少，可能是导致新疆地区肥胖问题较为严重的主要原因[15]。本研究结果显示，孕前超重可使子痫前期的发病风险增加(OR=3.221，95%CI: 2.699～3.844)，肥胖孕妇更易患妊娠期高血压疾病和子痫前期，肥胖和子痫前期之间关联的原因是代谢紊乱[16]。孕妇肥胖会增加孕妇引产、剖宫产，产程延长以及产后并发症的可能性，肥胖孕妇的胎儿更容易增加生长受限的概率，并且胎儿成为巨大儿的可能性也会增加[17]。超重与肥胖均可导致孕妇出现高脂、高胰岛素血症、水钠潴留等合并症,以及诱发胎盘发生病理生理性改变,引起全身血管内皮损伤,全身小动脉痉挛从而导致子痫前期的发生[18-19]。
研究发现[9]直系亲属有高血压病史具有遗传倾向，均会对子代造成影响。本研究结果显示孕妇的母亲或父亲患有高血压疾病时，发生子痫前期的风险明显增加(OR=1.575，95%CI: 1.254～1.979)，高血压家族史是影响子痫前期发生的独立危险因素，与Miller等[11]学者的研究结果一致。本研究发现，子痫前期孕妇有高血压家族史，其高血压的发生率为14.4%，本研究发现有高血压病史的孕妇发生子痫前期的风险系数高达17.552(7.021～43.882)。由于患者身体内有容易激活和受损的血管内皮细胞产生的较少一氧化氮，促凝血物质的分泌,对血管加压因子的灵敏度的提高,从而导致子痫前期的发生[20]。
本研究结果显示，首次怀孕的妇女患子痫前期的风险较高(OR=2.384，95%CI: 1.777～3.200)。可能由于初次妊娠时胎儿来源的绒毛初次暴露,而母体对其免疫应答异常所导致子痫前期[21]。有研究[22]进行回顾性研究发现一半以上的孕妇为初次妊娠,其中30%的孕妇没有进行产前检查,这可能是导致子痫前期的原因之一。文化程度较低的孕妇对孕期知识淡薄，对产前检查不重视，不规律产检，对孕期疾病的症状和征兆缺乏认识，孕妇及其家人也缺乏对子痫前期的认识和了解。应开展健康教育宣教，加强对孕妇孕期保健的重视，提高对育龄妇女早期产前检查益处的认识[23]。
综上所述，本研究发现乌鲁木齐市子痫前期发病的高危因素包括高龄、超重、家族史、高血压病史、妊娠期高血压疾病、子痫前期病史、首次怀孕、高中及以下学历。根据筛选出的独立危险因素建立列线图模型，结果表明，本研究所构建的列线图模型区分度、校准度和临床适用度均表现良好，具有良好的预测能力。医护人员可以根据该模型分析本地区子痫前期发病的高危因素，采取相应的预防措施以降低各项危险因素带来的负面效应，并对此类高危因素进行针对性的干预，降低子痫前期的发生率。本研究有一些不足之处，一是所纳入的数据为单中心数据，存在一定局限性；二是样本比较单一，后续可进行多中心大样本研究；三是尚未纳入生化指标，具有局限性，后续的研究可将生化指标及影像学、生物标志物等因素纳入模型。
基金
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  • 新疆维吾尔自治区自然科学基金项目(2022D01A29)
文献
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2024年第51卷第11期
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doi: 10.20043/j.cnki.MPM.202312263
  • 接收时间：2023-12-14
  • 首发时间：2026-03-18
  • 出版时间：2024-06-10
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  • 收稿日期：2023-12-14
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新疆维吾尔自治区自然科学基金项目(2022D01A29)
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    1.新疆医科大学公共卫生学院，新疆 乌鲁木齐 830000
    2.乌鲁木齐市妇幼保健院，新疆 乌鲁木齐 830000

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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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