Construct a nomogram model to predict the risk of preeclampsia
10.3969/j.issn.1673-9701.2025.02.005
- VernacularTitle:构建列线图模型预测子痫前期的发病风险
- Author:
Lu QIAN
1
;
Huifeng GU
;
Weihui YANG
Author Information
1. 湖州市妇幼保健院重症监护室,浙江湖州 313000
- Publication Type:Journal Article
- Keywords:
Preeclampsia;
Disease risk;
Predictive model
- From:
China Modern Doctor
2025;63(2):16-19
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the risk of preeclampsia(PE)and construct an individualized column chart predictive model.Methods A total of 209 PE patients admitted to Huzhou Maternal and Child Health Hospital from January 2020 to July 2023 were selected as research group,and 162 healthy pregnant women who underwent prenatal examination during the same period were selected as control group.Logistic regression model was used for data analysis,and nomogram prediction model based on Logistic regression results was constructed by R statistical software,receiver operating characteristic(ROC)curve and calibration curve were drawn,and Hosmer-Lemeshow goodness-of-fit test was conducted to evaluate prediction efficiency.Results The occurrence of PE had no correlation with body mass index(BMI),gestational age,pregnancy times,abortion history and whether there are multiple pregnancies(P＞0.05),but age,education level,gestational diabetes,gestational hypertension and standardized prenatal examination of pregnant women are related factors(P＜0.05).Age≥30 years old,education below senior high school,gestational diabetes,pregnancy-induced hypertension and irregular prenatal examination were risk factors for PE(P＜0.05).The area under ROC curve of nomograph model was 0.813(95％CI:0.770-0.855).Conclusion The nomogram model based on age,education level,diabetes in pregnancy,hypertension in pregnancy and irregular prenatal examination screened by Logistic regression model has a good predictive effect on the occurrence of PE.
