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盆腔器官脱垂术后新发压力性尿失禁风险预测模型的构建验证

Construction and validation of risk prediction model for newly stress urinary incontinence after pelvic organ prolapse
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摘要 目的探讨盆腔器官脱垂术后新发压力性尿失禁的发生率及相关危险因素,构建预测模型并进行验证。方法回顾性分析因盆腔器官脱垂(POPⅡ期及以上)行全盆底重建术并按时随访的患者422例,将患者按7∶3随机分为训练集和验证集。收集患者的一般临床资料[年龄、绝经年龄、阴道分娩史、身体质量指数(BMI)等]、POP-Q分期、相关辅助检查(盆底B超包括膀胱颈活动度、残余尿、逼尿肌厚度等)、既往史(高血压、糖尿病等)、盆腔手术史(包括子宫手术、附件手术、阑尾手术等)及脱垂程度,结合丹麦泌尿妇科数据库数据对所收数据进行logistic多因素回归分析后,建立术后新发压力性尿失禁(SUI)的风险预测模型并绘制列线图。绘制受试者工作特征曲线(ROC)对模型的性能进行评价。结果本研究共纳入422例盆腔器官脱垂患者,新发SUI的患者有99例,发生率为23.46%。盆腔器官脱垂术后新发SUI影响因素的单因素分析显示年龄、绝经、BMI、糖尿病、阴道分娩史、巨大儿分娩史、便秘史、盆腔手术史、残余尿、使用网片、膀胱颈活动度、尿道旋转角及阴道前壁脱垂的严重程度等相关因素均与盆腔器官脱垂术后新发SUI相关(P<0.05)。根据年龄、BMI、糖尿病、阴道分娩史、残余尿、膀胱颈活动度6个危险因素绘制预测模型的ROC,计算ROC曲线的AUC值为0.781,模型的区分度较好,绘制的校准曲线散点基本沿着斜率为45°的斜线分布,表明模型的一致性较好。结论患者的年龄、阴道分娩史、超声残余尿量、膀胱颈活动度、糖尿病史和BMI是术后新发SUI的相关危险因素。构建盆腔器官脱垂术后新发SUI的风险预测模型,该模型预测效果较好。 Objective To investigate the incidence and related risk factors of newly stress urinary incontinence after pelvic organ prolapse,and to establish aprediction model and to verify it.Methods A retrospective analysis was performed on 450 patients who underwent total pelvic floor reconstruction for pelvic organ prolapse(POP stage II and above)were followed up on time.There were 422 patients who met the criteria and were followed up.The patients were randomly divided into the training set and the verification set according to 7:3 ratio.General clinical data(age,menopause age,vaginal delivery history,BMI,etc.),POP-Q staging,and related auxiliary examinations were collected(pelvic floor B-ultrasound:Bladder neck mobility,residual urine,detrusor muscle thickness,etc.),previous history(hypertension,diabetes,etc.),history of pelvic surgery(including uterine surgery,adnexal surgery,appendix surgery,etc.)and degree of prolapse,combined with Danish Urogynecological database data and Logistic multivariate regression analysis of the collected data,the risk prediction model of new postoperative SUI was established and the nomogram was drawn.Receiver operating characteristic curve(ROC)was drawn to evaluate the performance of the model.Results A total of 422 patients with pelvic organ prolapse were included in this study,and 99 patients with new SUI were involved,with an incidence of 23.46%.A single-factor analysis of new SUI after pelvic organ prolapse shows age,menopause,BMI,diabetes,vaginal delivery history,huge child delivery history,constipation history,pelvic surgery history,residual urine,use of mesh,bladder neck activity,the severity of urethra rotation angle and the severity of the anterior vaginal wall prolapse are all related to the new SUI after pelvic organ prolapse(P<0.05).The subject working characteristic curve(ROC)of the predicted model was drawn based on 6 risk factors of age BMI,diabetes,vaginal delivery history,residual urine,bladder neck mobility,and ROC curves were calculated.The final risk pre-line design of the AUC value was 0.781,the model has a good distinction.The drawn calibration curve shows that the scatter points are basically distributed along the slope with a slope of 45°,indicating that the model is more consistent.The model verification method was performed by Bootstrap resampling(1000 times)and showed that the C statistic was 0.781.Drawing clinical curves comprehensively evaluates the clinical usefulness of this model.Conclusion The patients′age,history of vaginal delivery,ultrasound residual urine volume,bladder neck mobility,history of diabetes mellitus and BMI as the risk factors associated with new onset of SUI after surgery are analyzed.A risk prediction model for new-onset SUI after pelvic organ prolapse is constructed,which has a good predictive effect.
作者 马燕 王卓 王志芳 何瑶 李锁菲 李妍 MA Yan;WANG Zhuo;WANG Zhifang;HE Yao;LI Suofei;LI Yan(Ningxia Medical University,Yinchuan 750004,China;Department of Gynecology,General Hospital of Ningxia Medical University,Yinchuan 750004,China)
出处 《宁夏医学杂志》 2025年第3期204-209,共6页 Ningxia Medical Journal
基金 国家重点研发计划项目(2021YFC2701302) 宁夏重点研发计划项目(2022BEG03617)。
关键词 盆腔器官脱垂 术后新发压力性尿失禁 风险预测模型 列线图 Pelvic Floor Dysfunctional Disease De novo Stress urinary incontinence Risk prediction model nomogram
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