中国当代医药2026，Vol.33Issue(10)：29-33,5.DOI:10.3969/j.issn.1674-4721.2026.10.06
基于决策树算法的高龄产妇产后早期盆底肌力损伤预测模型构建
Construction of a predictive model for early postpartum pelvic floor muscle weakness in advanced maternal age using decision tree algorithms
摘要
Abstract
Objective To analyse factors influencing early postpartum pelvic floor muscle weakness in advanced maternal age women and construct a decision tree model.Methods Retrospectively,high-aged pregnant women who underwent 6-8 weeks postpartum follow-up at Pingxiang Mining Group Co.,Ltd.General Hospital from March 2022 to June 2024 were selected as the research subjects.Based on the measurement results,they were divided into the injury group and the non-injury group.Univariate analysis and binary logistic regression analysis were used to identify the injury factors,and a decision tree model was constructed,the model's efficacy was analyzed.Results A total of 226 parturients(56.50%)had pelvic floor muscle strength injury.Univariate analysis showed that the number of deliveries,second stage of labor duration,delivery mode,whether to perform late pregnancy exercise training,whether to receive pelvic floor rehabilitation training,and neonatal weight were associated with muscle strength injury.The results of multivariate analysis by the binary Logistic regression model showed that the number of deliveries was≥2(β=0.480,OR=1.615,95%CI:1.052-2.479),and the dura-tion of the second stage of labor was≥2 hours(β=0.774,OR=2.169,95%CI:1.425-3.300),neonatal weight≥4kg(β=0.463,OR=1.589,95%CI:1.042-2.422)are risk factors for pelvic floor muscle strength injury in the early postpartum period of elderly parturients.Exercise training in the third trimester of pregnancy(β=-0.589,OR=0.555,95%CI:0.362-0.850)and pelvic floor rehabilitation training(β=-0.478,OR=0.620,95%CI:0.407-0.944)are protective factors for pelvic floor muscle strength injury in the early postpartum period of elderly parturients.A decision tree model was constructed,consisting of 5 layers and 11 nodes.The model selected 6 clinical features as nodes,and the time of the second stage of labor ranked first in importance.The AUC of the decision tree model was 0.660(95%CI:0.607-0.713),and the AUC of the binary Logistic regression model was 0.674(95%CI:0.621-0.727).Conclusion The decision tree model constructed in this study for predicting early postpartum pelvic floor muscle weakness in older mothers demonstrated favourable predictive performance,facilitating early and precise identification of such weakness in this population.关键词
高龄产妇/盆底肌力损伤/影响因素/决策树算法Key words
Advanced maternal age/Pelvic floor muscle weakness/Influencing factors/Decision tree algorithm分类
医药卫生引用本文复制引用
张海霞,黄海莲,文娜..基于决策树算法的高龄产妇产后早期盆底肌力损伤预测模型构建[J].中国当代医药,2026,33(10):29-33,5.基金项目
江西省卫生健康委科技计划项目(20204471). （20204471）
