Wang Liyun, Zhang Minghui, Sha Kaihui, Qiao Yingqiao, Dong Qingqing
School of Nursing, Binzhou Medical University, Shandong, China.
The Affiliated Hospital of Binzhou Medical University, Shandong, China.
Heliyon. 2024 Sep 16;10(18):e37988. doi: 10.1016/j.heliyon.2024.e37988. eCollection 2024 Sep 30.
Postpartum stress urinary incontinence significantly impacts the quality of life and the physical and mental health of women. A reliable predictive model for postpartum stress urinary incontinence can serve as a preventive tool. Currently, there have been numerous studies developing predictive models to assess the risk of postpartum stress urinary incontinence, but the quality and clinical applicability of these models remain unknown.
To systematically review and evaluate existing models for predicting stressful postpartum risks.
PubMed, EBSCO, The Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, WanFang Data, SinoMed, and VIP Data databases were systematically searched from the time of database construction to October 2023. Two researchers used Critical appraisal and data extraction for systematic reviews of prediction modeling studies: the CHARMS checklist for data extraction. Three researchers used The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist for bias and applicability assessment.
Eight papers including ten postpartum stress urinary incontinence prediction models were finalized. The most common predictors in the prediction models were urinary incontinence (UI) during pregnancy, followed by mode of delivery, Maternal age, parity, and UI before pregnancy. Nine of the prediction models reported discrimination with an area under the ROC curve (AUC) or C-index between 0.680 and 0.850. All included studies were at high risk of bias, mainly due to mishandling of continuous predictors, unreported or mishandled missing data, and inadequate assessment of predictive model performance.
Postpartum stress urinary incontinence risk prediction models are in the initial development stage, and existing prediction models have a high risk of bias and poor modeling methodological quality, which may hinder their clinical application. In the future, healthcare practitioners should follow the norms of predictive model development and reporting to develop risk prediction models with superior predictive performance, low risk of bias, and easy clinical application.
产后压力性尿失禁严重影响女性的生活质量以及身心健康。可靠的产后压力性尿失禁预测模型可作为一种预防工具。目前，已有众多研究开发预测模型以评估产后压力性尿失禁的风险，但这些模型的质量和临床适用性仍不明确。
系统评价和评估现有的预测产后压力性尿失禁风险的模型。
从数据库建立至2023年10月，系统检索PubMed、EBSCO、Cochrane图书馆、Embase、科学网、中国知网、万方数据、中国生物医学文献数据库和维普数据库。两名研究人员使用“预测建模研究系统评价的关键评估与数据提取：数据提取的CHARMS清单”。三名研究人员使用“预测模型偏倚风险评估工具（PROBAST）清单”进行偏倚和适用性评估。
最终纳入8篇论文，其中包含10个产后压力性尿失禁预测模型。预测模型中最常见的预测因素是孕期尿失禁，其次是分娩方式、产妇年龄、产次和孕前尿失禁。9个预测模型报告的受试者工作特征曲线下面积（AUC）或C指数的区分度在0.680至0.850之间。所有纳入研究均存在较高的偏倚风险，主要原因是连续预测因素处理不当、未报告或处理缺失数据以及对预测模型性能评估不足。
产后压力性尿失禁风险预测模型尚处于初步发展阶段，现有预测模型存在较高的偏倚风险且建模方法学质量较差，这可能会阻碍其临床应用。未来，医疗从业者应遵循预测模型开发和报告的规范，以开发出预测性能优越、偏倚风险低且临床应用简便的风险预测模型。
