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经尿道前列腺电切术后并发尿失禁的影响因素分析
作者:孔婷  朱梓佑  卫中庆 
单位:南京医科大学第二附属医院 盆底外科中心, 江苏 南京 210028
关键词:经尿道前列腺电切术  尿失禁  影响因素  预测模型  预防策略 
分类号:R697+.3
出版年·卷·期(页码):2025·53·第五期(785-790)
摘要:

目的: 分析经尿道前列腺电切术(TURP)后并发尿失禁(UI)的影响因素,并探讨其防范策略。方法: 回顾性选取2019年2月至2024年2月本院TURP术后并发UI的患者49例为UI组,TURP术后未并发UI的患者141例为非UI组。收集患者临床资料,采用Logistic回归分析TURP术后并发UI的影响因素,依据影响因素构建预测模型,受试者工作特征(ROC)曲线评估模型的预测价值,Hosmer-Lemeshow检验评估模型拟合度,Bootstrap法进行内部验证。结果: Logistic多因素结果显示,年龄(OR=3.911,95%CI 1.648~9.286)、合并糖尿病(OR=3.233,95%CI 1.408~7.425)、前列腺体积(OR=1.081,95%CI 1.014~1.154)、术前盆底肌训练(OR=0.221,95%CI 0.084~0.582)、术前膜性尿道长度(OR=0.713,95%CI 0.591~0.859)、导尿管气囊注水量(OR=1.204,95%CI 1.112~1.304)是TURP术后并发UI的影响因素(P<0.05)。构建预测模型Logit(P)=-11.762+1.364×年龄(<65岁=0,≥65岁=1)+1.173×合并糖尿病(否=0,是=1)+0.078×前列腺体积(mL)-1.508×术前盆底肌训练(否=0,是=1)-0.339×术前膜性尿道长度(mm)+0.186×导尿管气囊注水量(mL)。ROC曲线显示,该模型预测TURP术后并发UI的曲线下面积(AUC)为0.943;H-L检验显示,χ2=11.521,P=0.174,模型不存在过度拟合;Bootstrap法内部验证显示,C指数为0.940,内部验证一致性较高。结论: 年龄、合并糖尿病、前列腺体积、术前盆底肌训练、术前膜性尿道长度、导尿管气囊注水量均可影响TURP术后UI的发生,据此构建的模型具有良好的预测效能,临床可根据上述因素制定UI预防策略。

Objective: To analyze the influencing factors of urinary incontinence(UI) after transurethral resection of prostate(TURP), and explore the preventive strategies. Methods: From February 2019 to February 2024, 49 patients with UI after TURP operation in our hospital were retrospectively selected as UI group, and 141 patients without UI after TURP operation were selected as non-UI group. Clinical data were collected, and Logistic regression analysis was used to analyze the influencing factors of post-TURP UI. A prediction model was constructed based on these factors and evaluated using receiver operating characteristic(ROC) curve analysis, with Hosmer-Lemeshow test for model fitness assessment and Bootstrap method for internal validation. Results: Multivariate Logistic regression analysis revealed that age(OR=3.911, 95%CI 1.648-9.286), comorbid diabetes(OR=3.233, 95%CI 1.408-7.425), prostate volume(OR=1.081, 95%CI 1.014-1.154), preoperative pelvic floor muscle training(OR=0.221,95%CI 0.084-0.582), preoperative membranous urethral length(OR=0.713, 95%CI 0.591-0.859), and catheter balloon water volume(OR=1.204,95%CI 1.112-1.304) were influencing factors for post-TURP UI(P<0.05). The prediction model was constructed as: Logit(P)=-11.762+1.364×age(<65=0,≥65=1)+1.173×diabetes(no=0, yes=1)+0.078×prostate volume(mL)-1.508×preoperative pelvic floor muscle training(no=0, yes=1)-0.339×preoperative membranous urethral length(mm)+0.186×catheter balloon water volume(mL). ROC curve analysis showed an area under the curve(AUC) of 0.943; Hosmer-Lemeshow test showed χ2=11.521, P=0.174, indicating no overfitting; Bootstrap internal validation showed a C-index of 0.940, demonstrating high internal consistency. Conclusion: Age, comorbid diabetes, prostate volume, preoperative pelvic floor muscle training, preoperative membranous urethral length, and catheter balloon water volume all affect the occurrence of post-TURP UI. The prediction model based on these factors demonstrates good predictive performance, and clinical prevention strategies can be developed based on these factors.

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