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经产妇首次经剖宫产分娩预测模型的构建与验证

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目的:根据连续两次妊娠情况,建立经产妇首次经剖宫产分娩的预测模型。方法:收集在苏州大学附属第一医院第2次分娩时间范围在2018年1月1日至2021年12月31日的连续两次分娩单活胎,且前次分娩为经阴道分娩经产妇的资料进行回顾性分析。根据经产妇再次妊娠是否行首次剖宫产进行分组(阴道分娩组和剖宫产组),应用单因素分析、逐步回归、多因素Logistic回归分析筛选经产妇首次经剖宫产分娩的影响因素,并建立预测模型,利用R语言构建模型的列线图。利用bootstrap重抽样方法进行内部验证。建立模型后,回顾性收集2022年1月1日至2023年4月1日连续两次分娩单活胎经产妇的临床资料,对模型进行外部验证。结果:①本研究共纳入2709例经产妇进行建模,其中首次经剖宫产分娩的患者占6。31%(171/2709);纳入603例经产妇进行外部验证。②根据单因素、逐步回归及多因素Logistic回归分析,筛选出所有影响经产妇首次经剖宫产分娩的变量包括:前次分娩产程异常、本次分娩年龄、辅助生殖技术助孕、妊娠期高血压疾病、妊娠合并血小板减少、羊水过少、羊水过多、巨大儿、胎儿生长受限、胎位异常、胎儿窘迫(P<0。05),并纳入最终的预测模型。③该模型的曲线下面积(AUC)为0。949(95%CI 0。928~0。969),校准曲线显示模型截距为0,斜率为1,Hosmer-Lemeshow检验P>0。05,模型准确度较高。④该模型外部验证的AUC为0。958,校准曲线的斜率为0。972,Hosmer-Lemeshow检验P=0。49。结论:初步建立了经产妇首次经剖宫产分娩的预测模型,该模型的预测效能较好,可为临床工作中经产妇的个体化评估提供工具。
Construction and Validation of the Prediction Model for the First Cesarean Section Delivery in Multiparas
Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31%(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P<0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95%CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P>0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

MultiparaFirst cesarean sectionPrediction modelNomogram

徐骁鹏、张雅文、沈敏红、黄沁

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苏州大学附属第一医院妇产科,江苏苏州 215006

经产妇 首次剖宫产 预测模型 列线图

2024

实用妇产科杂志
四川省医学会

实用妇产科杂志

CSTPCD北大核心
影响因子:2.564
ISSN:1003-6946
年,卷(期):2024.40(8)