Prediction models for inadequate bowel preparation in adults with colonoscopy: a scoping review
10.3760/cma.j.cn115682-20240116-00200-1
- VernacularTitle:成人结肠镜检查肠道准备不充分预测模型的范围综述
- Author:
Gairong MA
1
;
Chunfeng RUAN
1
;
Xinxian ZHAO
1
;
Yan SONG
1
Author Information
1. 上海交通大学医学院附属仁济医院护理部，上海 200127
- Publication Type:Journal Article
- Keywords:
Review;
Colonoscopy;
Bowel preparation;
Prediction model
- From:
Chinese Journal of Modern Nursing
2025;31(11):1520-1528
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To summarize the prediction model for inadequate bowel preparation in adults with colonoscopy to inform clinical practice.Methods:Literature on the prediction model of inadequate bowel preparation for colonoscopy was electronically searched in China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc, VIP, Yiigle, PubMed, Embase, Web of Science, CINAHL, PsycINFO, Cochrane Library and Google Scholar. The search period was from database establishment to December 31, 2023. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias and applicability of the included literature using the Prediction Model Risk of Bias Assessment Tool (PROBAST) .Results:A total of 22 articles covering 18 models were included. The incidence of inadequate bowel preparation for colonoscopy in adults ranged from 11.6% to 39.0%. The model construction method was dominated by Logistic regression, and some models had good predictive performance but lacked high-quality external validation results. Diabetes, chronic constipation, antidepressants, age, and body mass index were significant predictors of inadequate bowel preparation in colonoscopy.Conclusions:Nursing staff need to be aware of the influencing factors for inadequate bowel preparation and can choose models with good performance to guide clinical practice. Prediction models for inadequate bowel preparation in colonoscopy are currently in the developmental stage, and future research could leverage artificial intelligence to build high-performance, actionable models with extensive external validation.
