构建预测剖宫产后产褥感染发生风险的列线图模型

 目的 基于单中心数据构建预测剖宫产术后产褥感染发生风险的列线图模型。方法 回顾性分析2018年1月—2020年1月某院行剖宫产术的孕妇临床资料，分别使用单因素和logistic回归多因素分析孕妇行剖宫产术后发生产褥感染的独立危险因素，并建立相关列线图预测模型。结果 妊娠期生殖道炎性感染（OR=3.457，95%CI：1.205~9.917）、妊娠期糖尿病（OR=4.901，95%CI：1.247~19.259）、胎膜早破（OR=8.513，95%CI：3.041~23.830）、产后阴道反复出血（OR=10.000，95%CI：3.404~29.373）、血红蛋白<90 g/L （OR=4.657，95%CI：1.689~12.840）及清蛋白<40 g/L （OR=5.163，95%CI：2.062~12.926）是孕妇行剖宫产术后发生产褥感染的独立危险因素（均P<0.05）。基于以上6项独立危险因素建立预测孕妇行剖宫产术后发生产褥感染的列线图模型，并对该模型进行内外部验证，结果显示训练集和验证集的校正曲线与理想曲线拟合均较好，预测值同实测值基本一致，C指数分别为0.774（95%CI：0.739~0.809）、0.765（95%CI：0.734~0.796），该列线图模型具有良好的预测能力。结论 孕妇行剖宫产术后发生产褥感染的独立危险因素较多，此研究建立的列线图模型具有较好的预测能力和区分度，可为临床筛查高风险孕妇和采取有效的护理对策提供参考。

Constructing a nomogram model for predicting the risk of occurrence of puerperal infection after cesarean section

Objective To construct a nomogram model to predict the risk of occurrence of puerperal infection after cesarean section based on single-center data. Methods Clinical data of pregnant women undergoing cesarean section in a hospital from January 2018 to January 2020 were analyzed retrospectively, univariate and logistic regression multivariate analysis were adopted to analyze independent risk factors for puerperal infection after cesarean section, relevant nomogram prediction model was constructed. Results Inflammatory infection of genital tract during pregnancy (OR=3.457, 95%CI: 1.205-9.917), gestational diabetes (OR=4.901, 95%CI: 1.247-19.259), premature rupture of membrane (OR=8.513, 95%CI: 3.041-23.830), postpartum recurrent vaginal bleeding (OR=10.000, 95%CI: 3.404-29.373), hemoglobin < 90 g/L (OR=4.657, 95%CI: 1.689-12.840) and albumin < 40 g/L (OR=5.163, 95%CI: 2.062-12.926) were all independent risk factors for puerperal infection in pregnant women after cesarean section (all P < 0.05). Based on the above 6 independent risk factors, a nomogram model for predicting puerperal infection after cesarean section for pregnant women was constructed, internal and external verification of the model showed that calibration curve of training set and verification set were well fitted to the ideal curve, predicted value was basically consistent with the measured value. C-index were 0.774 (95%CI: 0.739-0.809) and 0.765 (95%CI: 0.734-0.796) respectively, indicating that the nomogram model has good predictive ability. Conclusion There are multiple independent risk factors for occurrence of puerperal infection in pregnant women after cesarean section, nomogram model constructed in this study has good predictive ability and differentiation, which can be used for clinical screening of high-risk pregnant women and adopting effective nursing care.