决策树模型与Logistic回归模型在产后尿潴留发生影响因素分析中的作用

The role of decision tree model and Logistic regression model in postpartum urinary retention influencing factors analysis

目的探讨决策树模型与Logistic回归模型在产后尿潴留(PUR)发生影响因素分析中的作用。方法收集2014年1月1日~2017年12月31日在浙江省丽水市人民医院(以下简称“我院”)经阴道分娩后发生PUR的180例产妇作为病例组,随机选取同期于我院经阴道分娩且未发生PUR的200例产妇作为对照组。采用决策树模型与Logistic回归模型回顾性分析PUR发生的相关影响因素。结果决策树模型和多因素Logistic回归模型分析均显示分娩镇痛(P=0.047)、产钳助产(P=0.001)以及会阴侧切(P <0.001)是发生PUR的独立危险因素。此外,Logistic回归模型分析结果还提示巨大儿(P=0.023)是PUR的影响因素,而决策树模型中未提示其对PUR的发生有影响。结论 PUR的影响因素众多,决策树模型和Logistic回归模型互为补充,可从不同方面描述PUR发生的影响因素,为进一步制订预防措施提供参考依据。

Objective To investigate the role of decision tree model and Logistic regression model in postpartum urinary retention(PUR) influencing factors analysis. Methods From January 1, 2014 to December 31, 2017, 180 puerpera with PUR after vaginal delivery in Lishui People′s Hospital of Zhejiang Province( "our hospital" for short) were selected as case group, and 200 puerpera who without PUR in our hospital at the same period were selected randomly as control group. Decision tree model and Logistic regression model were used to determine influential factors for PUR. Results Decision tree model and Logistic regression model indicated that delivery analgesia(P = 0.047), forceps delivery(P =0.001) and episiotomy(P < 0.001) were independent risk factors of PUR, and fetal macrosomia(P = 0.023) was also the influencing factor in the Logistic regression model, but the decision tree model did not indicate its influence. Conclusion There are many factors influencing PUR. Decision tree model and Logistic regression model are complementary to each other, which can describe the factors from different aspects, and provide basis and reference for the further formulation of preventive measures.