首页|基于"正常"人群子痫前期风险预测模型的建立

基于"正常"人群子痫前期风险预测模型的建立

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目的:探讨子痫前期(PE)孕妇发病前的预警信息,并建立子痫前期发病风险的预测模型,降低子痫前期的发病率.方法:收集2021-2022年于中国人民解放军北部战区总医院进行规律产前检查的176例最终确诊为子痫前期的患者作为病例研究组,并以1∶2比例收集同期在本机构接受规律产前检查的正常单胎妊娠者352例作为对照组.记录并比较两组孕妇的临床资料,对与子痫前期有关的影响因素进行单因素分析,再选取其中显著的影响因素进行二元logistic回归分析并建立子痫前期的预测模型,应用ROC曲线下面积(AUC)及Hosmer-Lemeshow检验评价该模型的能力,并构建相关森林图.结果:二元logistic回归分析显示:血清白蛋白(ALB)[OR=0.547,95%CI 0.481~0.622]、BMI 变化量(OR=2.167,95%CI 1.664~2.821)、尿蛋白(OR=2.700,95%CI 1.448~5.033)、胎儿宫内生长受限(FGR)(OR=3.030,95%CI 1.369~6.708)、水肿(OR=3.643,95%CI 1.603~8.281)对子痫前期的发生有显著影响.构建子痫前期发病风险的预测模型,P=1/1+exp(W),其中W=-13.911+0.603×血清白蛋白ALB(g/L)-0.773 ×BMI变化量(kg/m2)-0.993×尿蛋白(尿蛋白阳性=1,尿蛋白阴性或弱阳性=0)-1.109 ×胎儿宫内生长受限FGR(FGR=1,未FGR=0)-1.293×水肿(出现水肿=1,未出现水肿=0).该预测模型的ROC曲线的AUC为0.932(95%CI 0.906~0.957),进行Hosmer-Lemeshow检验,差异无统计学意义(P>0.05).根据预测模型,绘制预测模型相关森林图.结论:ALB、BMI变化量、尿蛋白、FGR、水肿是子痫前期发生的预警信息,具备这些预警信息者应纳入重点产检范围,利用该5项指标建立子痫前期发病风险的预测模型,可以提前预测子痫前期的发生,做到提前预防、及时干预,减少子痫前期对母婴的危害.
Establishing of a model for predicting the risk of preeclampsia based on the"normal"pregnant women
Objective:To explore the warning information of pregnant women before their preeclampsia(PE)onset,and to establish a predictive model for the risk of PE occurrence,so as to reduce the incidence of PE.Methods:176 pregnant women with PE who had undergone regular prenatal checkups in the general hospital of the northern war zone of the Chinese people's liberation army from 2021 to 2022 were collected in study group,and 352 healthy pregnant women with singleton pregnancy who had undergone regular prenatal checkups in hospital during the same period were collected in control group at the ratio of 1∶2.The clinical data of the women were recorded and were compared between the two groups.Univariate analysis was used to analyze the influential factors associated with preeclampsia of the women,and the significant influential factors were then selected for the binary logistic regression analysis.A predictive model for preeclampsia was developed,and the area under the curve(AUC)of receiver operator characteristic(ROC)curve and the Hosmer-Lemeshow test were applied to evaluate the efficacy of this model,and the relevant forest dia-gram was constructed.Results:Binary logistic regression analysis showed that the serum albumin(ALB)level(OR=0.547,95%CI 0.481-0.622),the body mass index(BMI)value(OR=2.167,95%CI 1.664-2.821),the urine protein level(OR=2.700,95%CI 1.448-5.033),the fetal intrauterine growth restriction(FGR)rate(OR=3.030,95%CI 1.369-6.708)and the edema rate(OR=3.643,95%CI 1.603-8.281)of the women had significant effects on their pre-eclampsia occurrence.The predictive model for the risk of the preeclampsia occurrence was established,that is P=1/1+exp(W),and W=-13.911+0.603 × serum ALB level(g/L)-0.773 × the change value of BMI(kg/m2)-0.993 × u-rine protein positive situation(1 for the positive urine protein and 0 for the negative or weakly positive urine protein)-1.109 × the intrauterine FGR situation(1 for FGR occurrence and 0 for no FGR occurrence)-1.293 × the edema situa-tion(1 for edema occurrence and 0 for no edema occurrence).The AUC of ROC curve of this predictive model for the preeclampsia occurrence was 0.932(95%CI 0.906-0.957),and the Hosmer-Lemeshow test performed had showed that there was no statistically significant difference(P>0.05).According to this predictive model,the predictive model-re-lated forest diagram was drawn.Conclusion:The serum ALB level,the change of BMI value,the urine protein level,and the FGR and edema occurrences of the pregnant women are the warning information for their preeclampsia occur-rence,and those women who have the warning information should be included in the scope of the key maternal exami-nation.The established prediction model for the risk of preeclampsia occurrence of the pregnant women based on above five indicators can predict the occurrence of preeclampsia in advance,so the achieve early prevention and timely inter-vention should be conducted to reduce the harm of the preeclampsia of the pregnant women to the mother and child.

PreeclampsiaPredictive modelLogistic regression

张晓红、李亚萌、张婷、刘劲松

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锦州医科大学北部战区总医院研究生培养基地(锦州,121000)

中国人民解放军北部战区总医院

子痫前期 预测模型 logistic回归

2024

中国计划生育学杂志
国家人口计生委科学技术研究所

中国计划生育学杂志

CSTPCD
影响因子:1.759
ISSN:1004-8189
年,卷(期):2024.32(5)
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