患者结肠镜检查前肠道准备失败风险预测模型的系统评价

目的 系统地评价患者结肠镜检查前肠道准备失败的风险预测模型.方法 计算机检索中国知网(CNKI)、中国生物医学文献数据库(SinoMed)、万方数据知识服务平台、维普中文科技期刊数据库(VIP)、PubMed MeDILINE、Cochrane Library、Embase、CINAHL、Web of Science数...

Risk Prediction Model for Inadequate Bowel Preparation before Colonoscopy:A Systematic Review

Objective To systematically evaluate the risk prediction model of patients with bowel preparation failure before colonoscopy. Methods We searched databases including CNKI, SinoMed, Wan fang Databases, VIP, PubMed, MeDLINE, Cochrane Library, Embase, CINAHL, and Web of Science databases for literature that met the criteria. Two researchers independently extracted the information, and used the PROBAST tool for quality evaluation. Results Eight studies were included, 6 of which were prospective cohort studies and 2 retrospective case-control studies. Three studies carried out internal verification and one study external verification. The area under the ROC curve of the eight studies was 0.63~0.87. The prediction performance was good, but there was still a risk of bias. The main reason was that processing method of missing data and the complexity of the data are not reported in the data analysis part. The method of screening predictors in some studies was not appropriate. And the model had not been verified internally and externally. Meta-analysis showed that chronic constipation OR=2.51, 95%CI(2.15, 2.92), Z=11.81, P<0.001], diabetes OR=2.14, 95%CI(1.79, 2.56), Z=8.26, P<0.05], antidepressant use OR=5.46, 95%CI(3.48, 8.58), Z=7.37, P<0.001]. All of them were independent risk factors for inadequate bowel preparation before colonoscopy. Conclusion Bowel preparation failure prediction model is still in the development stage, and each model has a certain risk of bias. In the future, model verification studies or the development of localized prediction models can be carried out, and they can be used in clinical practice to test their effectiveness. Medical staff should focus on the patients with chronic constipation, diabetes or those taking antidepressant medication, and give intervention strategies in advance.