女性护士无尿意排尿行为现状及预测模型分析

Analysis of the current status and prediction model of unintentional urination behavior in female nurses

目的:探索女性护士无尿意排尿行为现状,分析其影响因素,建立预测女性护士无尿意排尿行为的列线图模型。方法:采用便利抽样法选取天津市某三级甲等医院170名女性护士(训练集)为研究对象,根据无尿意排尿行为量表,确定研究对象是否存在无尿意排尿行为。应用单因素、二元Logistic回归模型分析女性护士无尿意排尿行为的影响因素,应用R语言软件建立预测列线图模型。另选50名女护士(验证集),采用ROC曲线分析该模型预测训练集、验证集无尿意排尿行为的效能。结果:170名女性护士中,83名(48.82%)护士存在无尿意排尿行为,纳入排尿行为异常组,另外87名(51.18%)护士纳入正常组。二元Logistic回归分析显示,年龄大于35岁、多次分娩、有憋尿行为、工作方式久坐或久站、未规律进行盆底肌训练、消极排尿行为信念及职业紧张感高均是无尿意排尿行为的独立影响因素(P<0.05)。列线图模型预测训练集产生的受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC)下面积(Area Under Curve,AUC)为0.965(95%CI 0.940~0.989)、验证集的C指数(C-index)为0.968(95%CI 0.946~0.990)。结论:构建的列线图预测模型可有效预测女性护士无尿意排尿行为的发生概率,医院管理人员可根据影响因素制定相关措施,以改善女性护士无尿意排尿行为,提升女性护士的生活质量。

Objective:To explore the status quo of female nurses′unintentional urination behavior,analyze the influencing factors,and establish a line graph model for predicting female nurses′unintentional urination behavior.Methods:A total of 170 female nurses(training set)in a class A hospital in Tianjin were included and divided into urinary incontinence group and non-urinary incontinence group according to whether there was no intention to urinate.Single and multiple logistic regression models were used to analyze the risk factors,and R software was used to establish the prediction line graph model.Another fifty female nurses in the hospital were selected as the validation set,and the ROC curve was used to analyze the efficacy of the model in predicting the non-urination behavior of the training set and the validation set.Results:There were 83 cases(48.82%)in the incontinence group and 87 cases(51.18%)in the non-incontinence group.Logistic regression analysis showed that age over 35,the number of labor,the behavior of holding urine,sedentary or standing for a long time,irregular pelvic floor muscle training,negative urination behavior,and high occupational tension were all independent factors influencing the behavior of urination without intention(P<0.05).ROC curve analysis showed that the AUC of the line graph model was 0.965(95%CI 0.940~0.989)for the training set and the C-index of the validation set was 0.968(95%CI 0.946~0.990).Conclusion:The nomogram model established based on the above independent risk factors has good predictive efficacy.Medical staff can develop relevant measures based on influencing factors to decrease female nurses′unintentional urination behavior and enhance their quality of life.