Wang Qi, Manodoro Stefano, Lin Huifang, Li Xiaofang, Lin Chaoqin, Jiang Xiaoxiang
Department of Gynecology, Fujian Maternity and Child Health Hospital, Fuzhou, PR China.
College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, PR China.
Digit Health. 2025 Apr 3;11:20552076251333661. doi: 10.1177/20552076251333661. eCollection 2025 Jan-Dec.
This study aims to identify independent risk factors for mixed urinary incontinence (MUI) in parous women using a multicenter epidemiological study and to establish and validate a predictive nomogram.
A large-scale survey was conducted from June 2022 to September 2023, including parous women aged over 20 selected through stratified random sampling. Data encompassed sociodemographic and obstetric histories, comorbidities, and standardized questionnaires. The primary goal was to identify high-risk factors for MUI, while the secondary was to develop a nomogram. Risk factors were determined using univariable and multivariable analyses. The nomogram's performance was assessed via concordance index (C-index) and calibration plots through internal and external validation.
A total of 7709 women participated, with an MUI prevalence of 6.8%. Independent risk factors included higher body mass index, urban residence, postmenopausal status, multiple vaginal deliveries, history of pelvic surgery and macrosomia, family history of pelvic floor dysfunction, hypertension, and constipation. The area under the curve for the nomogram model was 0.717 in the training set, 0.714 for internal validation, and 0.725 for external validation. The calibration plots showed a good agreement between the predicted and observed outcomes.
This study identifies key risk factors for MUI in parous women and introduces a validated nomogram with high but not perfect predictive accuracy. The model enables early identification and management of MUI, though further refinement could enhance accuracy.
本研究旨在通过一项多中心流行病学研究确定经产妇混合性尿失禁（MUI）的独立危险因素，并建立和验证一个预测列线图。
于2022年6月至2023年9月进行了一项大规模调查，纳入通过分层随机抽样选取的20岁以上经产妇。数据包括社会人口统计学和产科病史、合并症以及标准化问卷。主要目标是确定MUI的高危因素，次要目标是开发一个列线图。使用单变量和多变量分析确定危险因素。通过一致性指数（C指数）和校准图，通过内部和外部验证评估列线图的性能。
共有7709名女性参与，MUI患病率为6.8%。独立危险因素包括较高的体重指数、城市居住、绝经后状态、多次阴道分娩、盆腔手术史和巨大儿、盆底功能障碍家族史、高血压和便秘。列线图模型在训练集中的曲线下面积为0.717，内部验证为0.714，外部验证为0.725。校准图显示预测结果与观察结果之间具有良好的一致性。
本研究确定了经产妇MUI的关键危险因素，并引入了一个经过验证的列线图，其预测准确性较高但并不完美。该模型能够早期识别和管理MUI，不过进一步优化可提高准确性。
