1.Prediction models for de novo stress urinary incontinence after pelvic organ prolapse surgery: a systematic review
Xiaoxiao WANG ; Xiuhuan LIU ; Lili SUI ; Haimei CHA ; Yanhuan WU ; Wenwen DIAO ; Qianqian MA ; Chao XU ; Xiao XU ; Xueyun XU
Chinese Journal of Modern Nursing 2024;30(33):4501-4507
Objective:To systematically review the predictive model for de novo stress urinary incontinence (de novo SUI) after pelvic organ prolapse (POP) surgery, with the aim of providing reference for preventing the occurrence of de novo SUI.Methods:Literature on the prediction model of de novo SUI after POP surgery was electronically retrieved in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, WanFang, and VIP. The search period was from the establishment of the database to December 31, 2023, and the language was limited to Chinese and English. Two researchers independently screened literature, extracted data, and used the prediction model risk of bias assessment tool (PROBAST) to evaluate the quality of the models.Results:A total of 13 articles were included, including 13 de novo SUI risk prediction models. One literature was a prospective study, one literature was a secondary analysis of data, and the rest were retrospective studies. The area under the receiver operating characteristic curve in nine models ranged from 0.595 to 0.842, and the C-index of three models ranged from 0.710 to 0.738. Five models were not validated or only internally validated after construction. Six models were validated in one external population. The predictive performance of one model was validated in six external populations. The overall applicability of the 13 prediction models was good, but there was a certain risk of bias in all of them. Conclusions:There is a significant difference in the predictive performance of the de novo SUI risk prediction model after POP surgery, and the number is relatively small, indicating that it is still in the development stage. Future research should continuously optimize existing models and conduct external validation, and construct predictive models suitable for postoperative de novo SUI in POP patients in China.