关键词: 妊娠；压力性尿失禁；症状识别；决策树预测模型

Abstract: Objective: To understand the occurrence of urinary incontinence during pregnancy, to establish an early warning model for urinary incontinence during pregnancy, and to test its effectiveness. Methods: A retrospective survey of 707 primiparas was conducted. The primiparas were asked to complete a questionnaire included demographic information, risk factor evaluated scale and the International Consultation on Incontinence Questionnaire Short Form (ICI-Q-SF). Results:① If the primipara have this condition of without urinary incontinence family history, with stretch marks and constipation, the incidence rate of urinary incontinence patients accounted for 63.2% of the node. And when the primiparas have this condition of without urinary incontinence family history, with stretch marks, no constipation, pre-pregnancy BMI ≤ 23.96, pelvic floor muscle exercise during pregnancy (1-2 times per week), who eat fruit 1-3 days or 4-6 days a week, with abortion history, the incidence rate of urinary incontinence patients accounted for 58.8% of the node.②The importance of risk factors were as follows: constipation, pre-pregnancy BMI level, family history of urinary incontinence, weekly fruit consumption, stretch marks, history of abortion, strength of pelvic floor muscle exercise during pregnancy, physical exercise before pregnancy, pelvic floor muscle exercise before pregnancy, vomiting, education level, daily water intake. ③This model has an accuracy of 76.7%, a sensitivity of 84.8%. The area under the ROC curve was 0.767 (95% CI: 0.719-0.814), P<0.001. The risk of cross-validation was estimated at 29.8%, P<0.001. Conclusion: The early warning model of urinary incontinence in pregnancy based on the decision tree algorithm has more accurate predictive ability. Among the population in the region, the overall forecast accuracy is better.

Key words: pregnancy; stress urinary incontinence; symptom recognition; decision tree prediction model