剖宫产产妇术中低体温风险预测模型的构建及应用效果研究

Establishment of a risk predictive model of maternal hypothermia during caesarean section and its application effect test

目的探讨剖宫产产妇术中低体温的影响因素,建立预测模型,并对应用效果进行检验。方法采用便利抽样法选取2018年7月—2019年1月长沙市3所三级甲等医院的369名剖宫产产妇为研究对象,依据产妇剖宫产期间是否发生低体温分为低体温组(n=83)和非低体温组(n=286),分析发生低体温的危险因素并建立预测模型,采用Hosmer-Lemeshow检验判断模型的拟合优度,ROC曲线检测模型的预测效能。结果共纳入麻醉后体温、体重指数、妊娠期亚临床甲状腺功能减退、术中液体丢失量、术中冲洗腹腔液体量、主动保温持续时间、进入手术室室温7个因素构建预测模型。该模型Hosmer-Lemeshow检验结果显示,P=0.425,ROC曲线下面积为0.888,当最佳界值为-1.844时,灵敏度为0.880,特异度为0.741,实际应用准确率为79.7%,提示模型具有较好的拟合效果及鉴别效度。结论该模型能较好地预测剖宫产术期间产妇低体温的发生风险,为医护人员及时对高危产妇采取预防性管理措施提供参考。

Objective To identify risk factors of maternal hypothermia during caesarean section,establish a risk prediction model,and test the application effect. Methods A total of 369 puerperas,who underwent caesarean section in 3 hospitals in Changsha City,Hunan Province from July 2018 to January 2019,were enrolled for this study and allocated into a hypothermia group and a non-hypothermia group based on whether hypothermia occurred during the caesarean section. Risk factors between hypothermia group(n=83) and non-hypothermia group(n=286) were compared using logistic regression for model construction. The goodness of fit of the model was verified by HosmerLemeshow test. The predictive validity of the model was evaluated by the area under the ROC curve(AUC).Result 7 factors,namely maternal body temperature after anesthesia,body mass index,subclinical hypothyroidism during pregnancy,intraoperative loss of fluid,intraoperative irrigation fluid in the abdominal cavity without warming,the time of active warming and preoperative ambient temperature,were included in the model. Logistic regression prediction model Hosmer-Lemeshow goodness-of-fit test showed that P=0.425. The area under the ROC curve was0.888. When the best boundary value was-1.844,the sensitivity and specificity of the logistic model were 0.880 and0.741,respectively. The accuracy of the model was 79.7%. It showed that the model had a good fitting effect and discriminating validity. Conclusion The risk prediction regression model had satisfactory prediction,high sensitivity and specificity,which can provide references for preventative treatment and nursing measures for high-risk mothers.