Wu Hongjiao, Wu Rui, Zhang Yunyun, Lu Xinyi, Zhao Wei, Xu Biyun, Liu Jun, Zhang Nina
Department of Gastroenterology, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Sci Rep. 2025 Aug 2;15(1):28265. doi: 10.1038/s41598-025-13739-0.
Inadequate bowel preparation negatively impacts the quality of colonoscopy, potentially resulting in missed lesions and the need for repeat procedures. Although gut motility plays a key role in bowel cleansing, it is often neglected in risk prediction. This study developed and validated a predictive model and simplified scoring system that integrates gut motility parameters to identify individuals at risk of inadequate bowel preparation. A total of 1,165 patients from two hospitals were enrolled, with 815 forming the training set and 350 forming the external validation cohort. The overall rate of inadequate bowel preparation was 9.8%. Multivariate analysis revealed that altered bowel movement frequency, stool consistency based on the Bristol Stool Scale, low polyethylene glycol (PEG) volume, and delayed last bowel movement were significant predictors of poor preparation. These variables were incorporated into a user-friendly scoring system that demonstrated good discriminative ability, with area under the curve values of 0.778 and 0.774 in the training and validation cohorts, respectively. A cutoff score of 3.0 yielded a sensitivity of 75.0%, specificity of 66.9%, and a negative predictive value of 96.3% in external validation. This model provides a practical, motility-informed approach for risk stratification and personalized preparation regimens, offering potential to enhance the effectiveness and efficiency of colonoscopy in diverse clinical settings.
肠道准备不充分会对结肠镜检查的质量产生负面影响,可能导致病变漏诊以及需要重复检查。尽管肠道蠕动在肠道清洁中起关键作用,但在风险预测中常常被忽视。本研究开发并验证了一种预测模型和简化评分系统,该系统整合了肠道蠕动参数以识别肠道准备不充分风险的个体。来自两家医院的1165名患者被纳入研究,其中815名组成训练集,350名组成外部验证队列。肠道准备不充分的总体发生率为9.8%。多因素分析显示,排便频率改变、基于布里斯托大便分类法的大便性状、聚乙二醇(PEG)量少以及最后一次排便延迟是准备不佳的重要预测因素。这些变量被纳入一个用户友好的评分系统,该系统显示出良好的鉴别能力,训练队列和验证队列的曲线下面积值分别为0.778和0.774。在外部验证中,截断分数为3.0时,敏感性为75.0%,特异性为66.9%,阴性预测值为96.3%。该模型为风险分层和个性化准备方案提供了一种实用的、基于蠕动的方法,有可能提高结肠镜检查在不同临床环境中的有效性和效率。