Please wait a minute...
文章检索
预防医学  2026, Vol. 38 Issue (4): 423-428    DOI: 10.19485/j.cnki.issn2096-5087.2026.04.022
  妇幼保健 本期目录 | 过刊浏览 | 高级检索 |
阴道分娩初产妇盆腔器官脱垂的预测模型构建
王超, 张路平, 陈梦宇, 花霞, 王静
邯郸市第一医院,河北 邯郸 056000
Construction of a prediction model for pelvic organ prolapse among primiparous women after vaginal delivery
WANG Chao, ZHANG Luping, CHEN Mengyu, HUA Xia, WANG Jing
Handan First Hospital, Handan, Hebei 056000, China
全文: PDF(953 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 目的 构建阴道分娩初产妇盆腔器官脱垂(POP)预测模型,为POP早期筛查和预防提供依据。方法 选择2022年1月—2025年1月在邯郸市第一医院阴道分娩初产妇为研究对象,收集年龄、居住地等基本资料和新生儿出生体重、产钳助产等临床特征。依据《盆腔器官脱垂中国诊治指南(2020年版)》诊断POP。采用LASSO回归和多因素logistic回归模型分析筛选阴道分娩初产妇POP的独立预测因素,构建列线图预测模型。采用受试者操作特征(ROC)曲线下面积(AUC)、灵敏度、特异度、准确率、校准曲线、Hosmer-Lemeshow检验、Brier分数和决策曲线分析评估列线图预测模型的预测效能。结果 纳入阴道分娩初产妇200人,<35岁169人,占84.50%。孕前体质指数(BMI)<24 kg/m2 149人,占74.50%。发生POP 53例,发生率为26.50%。多因素logistic回归分析结果显示,年龄(≥35岁,OR=5.300 ,95%CI: 1.824~15.399)、孕前BMI(≥24 kg/m2,OR=4.371 ,95%CI: 1.862~10.263)、产钳助产(OR=5.001 ,95%CI: 1.847~13.536)、第二产程时间延长(OR=3.659 ,95%CI: 1.415~9.464)和新生儿出生体重(OR=4.695 ,95%CI: 2.332~9.450)是阴道分娩初产妇POP的独立预测因素。构建的列线图预测模型AUC值为0.902(95%CI:0.842~0.962),灵敏度为0.811,特异度0.912,准确率为0.885,区分度良好;校准曲线、Hosmer-Lemeshow检验(P>0.05)和Brier分数(0.081)显示模型校准度良好;决策曲线分析结果显示,风险阈值概率为0.12~0.70时,净收益较高。结论 本研究构建的POP列线图预测模型有较好的区分度、校准度和临床实用性,对阴道分娩初产妇POP风险有一定预测价值。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
王超
张路平
陈梦宇
花霞
王静
关键词 盆腔器官脱垂阴道分娩初产妇列线图    
AbstractObjective To construct a prediction model for pelvic organ prolapse (POP) among primiparous women after vaginal delivery, so as to provide a basis for early screening and prevention of POP. Methods Primiparous women after vaginal delivery at the First Hospital of Handan City from January 2022 to January 2025 were selected as study participants. Basic information including age and residence, as well as clinical characteristics including neonatal birth weight and forceps-assisted delivery, were collected. POP was diagnosed according to the Chinese Guidelines for the Diagnosis and Treatment of Pelvic Organ Prolapse (2020 Edition). LASSO regression and multivariable logistic regression model were used to analyze and screen independent predictors of POP among primiparous women after vaginal delivery, and a nomogram prediction model was constructed. The predictive performance of the nomogram prediction model was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, calibration curve, Hosmer-Lemeshow test, Brier score, and decision curve analysis. Results A total of 200 primiparous women after vaginal delivery were included, of whom 169 (84.50%) were aged <35 years and 149 (74.50%) had a preconception body mass index (BMI) <24 kg/m2. POP occurred in 53 cases, with an incidence of 26.50%. Multivariable logistic regression analysis showed that age (≥35 years, OR=5.300, 95%CI: 1.824-15.399), preconception BMI (≥24 kg/m2, OR=4.371, 95%CI: 1.862-10.263), forceps-assisted delivery (OR=5.001, 95%CI: 1.847-13.536), prolonged duration of the second stage of labor (OR=3.659, 95%CI: 1.415-9.464), and neonatal birth weight (OR=4.695, 95%CI: 2.332-9.450) were independent predictors of POP among primiparous women after vaginal delivery. The constructed nomogram prediction model achieved an AUC of 0.902 (95%CI: 0.842-0.962), a sensitivity of 0.811, a specificity of 0.912, and an accuracy of 0.885, indicating good discrimination. The calibration curve, Hosmer-Lemeshow test (P>0.05), and Brier score (0.081) demonstrated good calibration. Decision curve analysis showed that when the risk threshold probability ranged from 0.12 to 0.70, the net benefit was relatively high. Conclusion The nomogram prediction model for POP constructed in this study exhibits good discrimination, calibration, and clinical utility, and has certain predictive value for POP risk among primiparous women after vaginal delivery.
Key wordspelvic organ prolapse    vaginal delivery    primiparous women    nomogram
收稿日期: 2025-11-24      修回日期: 2026-02-13      出版日期: 2026-04-10
中图分类号:  R71  
基金资助:2025年度河北省医学科学研究课题计划项目(20251290)
作者简介: 王超,本科,主治医师,主要从事妇产超声工作,E-mail:nb6092@sina.com
引用本文:   
王超, 张路平, 陈梦宇, 花霞, 王静. 阴道分娩初产妇盆腔器官脱垂的预测模型构建[J]. 预防医学, 2026, 38(4): 423-428.
WANG Chao, ZHANG Luping, CHEN Mengyu, HUA Xia, WANG Jing. Construction of a prediction model for pelvic organ prolapse among primiparous women after vaginal delivery. Preventive Medicine, 2026, 38(4): 423-428.
链接本文:  
https://www.zjyfyxzz.com/CN/10.19485/j.cnki.issn2096-5087.2026.04.022      或      https://www.zjyfyxzz.com/CN/Y2026/V38/I4/423
[1] HADIZADEH-TALASAZ Z,KHADIVZADEH T,MOHAJERI T,et al.Worldwide prevalence of pelvic organ prolapse:a systematic review and meta-analysis[J].Iran J Public Health,2024,53(3):524-538.
[2] JEPPSON P C,BALGOBIN S,WHEELER T,et al.Impact of lifestyle modifications on the prevention and treatment of pelvic organ prolapse[J].Int Urogynecol J,2025,36(1):59-69.
[3] 陈莉,侯涛,邓婷婷,等.经阴道自然腔道内镜下阴道残端高位骶韧带悬吊术治疗中盆腔脱垂的临床效果分析[J].实用妇产科杂志,2024,40(6):501-504.
[4] HWANG W Y,JEON M J,SUH D H.Minimally invasive sacrohysteropexy versus vaginal hysterectomy with uterosacral ligament suspension for pelvic organ prolapse:a prospective randomized non-inferiority trial[J].J Minim Invasive Gynecol,2024,31(5):406-413.
[5] 肖雅,洪莉,李素廷,等盆腔器官脱垂发生发展相关危险因素及机制研究进展[J].医学综述,2023,29(4):706-710.
[6] OBSA M S,WORJI T A,KEDIR N A,et al.Risk factors of pelvic organ prolapse at Asella Teaching and Referral Hospital:unmatched case control study[J/OL].Front Glob Womens Health,2022,3[2026-02-13].http://doi.org/10.3389/fgwh.2022.833823.
[7] 中华医学会妇产科学分会妇科盆底学组.盆腔器官脱垂的中国诊治指南(2020年版)[J].中华妇产科杂志,2020,55(5):300-306.
[8] SCHULTEN S F M,CLAAS-QUAX M J,WEEMHOFF M,et al.Risk factors for primary pelvic organ prolapse and prolapse recurrence:an updated systematic review and meta-analysis[J].Am J Obstet Gynecol,2022,227(2):192-208.
[9] ANDEBRHAN S B,CARON A T,SZLACHTA-MCGINN A,et al.Pelvic organ prolapse recurrence after pregnancy following uterine-sparing prolapse repair:a systematic review and meta-analysis[J].Int Urogynecol J,2023,34(2):345-356.
[10] OLIVEIRA I K B,DA SILVA CALISTO S L,FERREIRA C W S,et al.Occult urinary incontinence,diabetes,obesity,prolapse severity,and type of surgical repair as risk factors for de novo stress urinary incontinence in women undergoing surgical repair of pelvic organ prolapse:a systematic review and meta-analysis[J].Neurourol Urodyn,2025,44(1):194-206.
[11] 谭辉,李小英,吴晓艺,等.阴道分娩初产妇产后早期盆腔器官脱垂简易风险预测评分模型的构建与验证[J].医学理论与实践,2023,36(9):1460-1463.
[12] COSTA E SILVA C L,BORTOLINI M A T,BATISTA N C,et al.The rs2165241 polymorphism of the Loxl1 gene in postmenopausal women with pelvic organ prolapse[J].Climacteric,2022,25(4):407-412.
[13] DA SILVA R S P,BORTOLINI M A T,TEIXEIRA J B,et al.Association between the rs1036819 polymorphism of the ZFAT gene and pelvic organ prolapse:a case-control study[J].Int Urogynecol J,2023,34(10):2611-2617.
[14] 姚秀华,刘剀,于敬会.初产妇盆腔器官脱垂现况及影响因素分析[J].中国妇幼卫生杂志,2023,14(3):7-11.
[15] VILA POUCA M C P,PARENTE M P L,NATAL JORGE R M,et al.Investigating the birth-related caudal maternal pelvic floor muscle injury:the consequences of low cycle fatigue damage[J/OL].J Mech Behav Biomed Mater,2020,110[2026-02-13].http://doi.org/10.1016/j.jmbbm.2020.103956.
[16] ZHANG P H,DU W J,GUO G,et al.Influencing factors of recurrence after pelvic organ prolapse surgery and construction of a nomogram risk prediction model[J/OL].Rev Assoc Med Bras(1992),2024,70(12)[2026-02-13].http://doi.org/10.1590/1806-9282.20240849.
[17] XIE X H,SHEN J J.Analysis of risk factors of pelvic organ prolapse in postmenopausal women and construction of prediction model[J].Altern Ther Health Med,2024,30(1):265-269.
[1] 章涛, 林君芬, 古雪, 徐乐, 李傅冬, 吴晨. 社区老年人阿尔茨海默病列线图预测模型构建[J]. 预防医学, 2025, 37(9): 875-880.
[2] 楚楚, 徐红, 蔡波, 韩颖颖, 穆海祥, 郑会燕, 林玲. 中老年人群脑卒中风险预测模型研究[J]. 预防医学, 2025, 37(7): 649-653.
[3] 刘明坤, 张丰香, 韩彩静, 王霞, 陈世坤, 金梅, 孙金月. 2型糖尿病患者周围神经病变风险预测模型研究[J]. 预防医学, 2025, 37(7): 692-696.
[4] 龚亮亮, 戎志东. 10~13岁儿童非自杀性自伤行为风险预测模型研究[J]. 预防医学, 2025, 37(6): 546-550.
[5] 龚海英, 薛凤玉, 刘晓芬, 邢瑞婷, 苗雨阳, 赵耀. 18~79岁居民高血压风险预测模型研究[J]. 预防医学, 2025, 37(10): 1075-1080.
[6] 沈丽丽, 潘亚慧, 冯佳峰. 化纤企业倒班工人睡眠障碍预测模型研究[J]. 预防医学, 2025, 37(1): 51-54.
[7] 郑帅印, 李丽丹, 陈佩弟, 谢尔瓦妮古丽·阿卜力米提, 李砥. 2型糖尿病合并非酒精性脂肪肝的预测模型研究[J]. 预防医学, 2024, 36(9): 741-745,749.
[8] 陆艳, 李琼珊, 孟迪云, 梅丽娜, 丁忠英, 李雯雯, 储华, 秦玲. 双胎妊娠孕妇子痫前期风险预测模型研究[J]. 预防医学, 2024, 36(4): 283-287.
[9] 周梓萌, 洪忻. 心血管病高危人群预测模型研究[J]. 预防医学, 2024, 36(3): 211-214.
[10] 高梦阳, 娄鹏威, 马丽, 李惠, 黄玉婷, 王璐, 王凯. 甲状腺乳头状癌中央区淋巴结转移预测模型研究[J]. 预防医学, 2023, 35(3): 229-234.
[11] 杨秀琳, 马宗康, 马霞, 马鑫. 东乡族自治县农村居民传染病健康素养调查[J]. 预防医学, 2023, 35(2): 166-170.
[12] 王晓薇, 许艳岚. 老年2型糖尿病患者认知衰弱风险预测研究[J]. 预防医学, 2023, 35(12): 1037-1042.
[13] 谢庆堂, 罗健, 陈开, 雷少颖. 应用列线图模型预测噪声作业工人高频听力损失情况[J]. 预防医学, 2020, 32(7): 715-719.
[14] 卢蓉, 余睿. 顺产初产妇产后性功能障碍及影响因素分析[J]. 预防医学, 2020, 32(2): 190-192.
[15] 施明明, 张晓, 李娜, 胡锦峰. 居民血脂异常影响因素的列线图分析[J]. 预防医学, 2019, 31(5): 460-464.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed