Risk prediction models of postoperative urinary retention: a systematic review
10.3760/cma.j.cn115682-20230731-00275
- VernacularTitle:术后患者尿潴留发生风险预测模型的系统评价
- Author:
Xuefan DONG
1
;
Jianli TIAN
;
Jingyi MA
;
Yang LI
;
Qiyue JIA
Author Information
1. 承德医学院护理学院，承德 067000
- Keywords:
Systematic review;
Postoperative urinary retention;
Risk prediction models;
Assessment tools
- From:
Chinese Journal of Modern Nursing
2024;30(10):1352-1358
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically retrieve, analyze and evaluate risk prediction models of postoperative urinary retention, so as to provide a basis for the application and optimization of the model.Methods:The research on the risk prediction model of postoperative urinary retention was electronically retrieved in PubMed, Web of Science, Embase, Cochrane Library, CINAHL, China National Knowledge Infrastructure, WanFang Data, VIP, China Biology Medicine disc and other databases. The language of the literature was Chinese or English. The search period was from database establishment to February 20, 2023. Two researchers independently conducted literature screening and data extraction, and independently evaluated the bias risk and applicability of the included literature using the Prediction Model Risk of Bias Assessment Tool.Results:A total of 10 articles were included, including 17 risk prediction models for postoperative urinary retention. The areas under the receiver operating characteristic curve of 17 models were 0.700 to 0.920. The five most common predictors included in the model were age, gender, postoperative analgesia, diabetes, and operation time. The applicability of the model was good among the 10 studies, but there was some bias, mainly due to insufficient sample size, neglect of missing data and processing methods, overfitting issues, conversion of continuous variables into binary variables, and use of single factor screening for predictive factors.Conclusions:The risk prediction model of postoperative urinary retention has good prediction performance, but there is a certain risk of bias. The clinical value of the model needs further verification. External validation and continuous optimization are required for existing prediction models. Prospective research should also be carried out to develop a universal prediction model with good prediction performance, so as to provide an accurate and practical tool for clinical evaluation of postoperative urinary retention.
