Department of Gastroenterology, Lanzhou University Second Hospital, No. 82 Cuiying Men, Cheng Guan District, Lanzhou, 730030, Gansu, China.
Xijing Hospital of Digestive Diseases, Fourth Military Medical University, 127 Changle West Road, Xi'an, 710032, Shanxi, China.
Clin Transl Oncol. 2024 Sep;26(9):2262-2273. doi: 10.1007/s12094-024-03443-2. Epub 2024 Apr 2.
Adequate bowel preparation (BP) is crucial for the diagnosis of colorectal diseases. Identifying patients at risk of inadequate BP allows for targeted interventions and improved outcomes. We aimed to develop a model for predicting inadequate BP based on preparation-related factors.
Adult outpatients scheduled for colonoscopy between May 2022 and October 2022 were enrolled. One set (N = 913) was used to develop and internally validate the predictive model. The primary predictive model was displayed as a nomogram and then modified into a novel scoring system, which was externally validated in an independent set (N = 177). Inadequate BP was defined as a Boston Bowel Preparedness Scale (BBPS) score of less than 2 for any colonic segment. The model was evaluated by the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA).
Independent factors included in the prediction model were stool frequency ≤ 5 (15 points), preparation-to-colonoscopy interval ≥ 5 h (15 points), incomplete dosage (100 points), non-split dose (90 points), unrestricted diet (88 points), no additional water intake (15 points), and last stool appearance as an opaque liquid (0-80 points). The training set exhibited the following performance metrics for identifying BP failure: area under the curve (AUC) of 0.818, accuracy (ACC) of 0.818, positive likelihood ratio (PLR) of 2.397, negative likelihood ratio (NLR) of 0.162, positive predictive value (PPV) of 0.850, and negative predictive value (NPV) of 0.723. In the internal validation set, these metrics were 0.747, 0.776, 2.099, 0.278, 0.866, and 0.538, respectively. The external validation set showed values of 0.728, 0.757, 2.10, 0.247, 0.782, and 0.704, respectively, indicating strong discriminative ability. Calibration curves demonstrated close agreement, and DCA indicated superior clinical benefits at a threshold probability of 0.73 in the training cohort and 0.75 in the validation cohort for this model.
This novel scoring system was developed from a prospective study and externally validated in an independent set based on 7 easily accessible variables, demonstrating robust performance in predicting inadequate BP.
充分的肠道准备(BP)对于结直肠疾病的诊断至关重要。识别可能存在肠道准备不充分的患者,可以进行有针对性的干预,改善治疗效果。我们旨在基于与准备相关的因素,建立一个预测肠道准备不充分的模型。
2022 年 5 月至 2022 年 10 月期间,招募了接受结肠镜检查的成年门诊患者。其中一组(n=913)用于建立和内部验证预测模型。主要预测模型以列线图的形式呈现,然后修改为新的评分系统,在独立的一组(n=177)中进行外部验证。肠道准备不充分的定义为任何结肠段的波士顿肠道准备量表(BBPS)评分<2。该模型通过接受者操作特征(ROC)曲线、校准图和决策曲线分析(DCA)进行评估。
预测模型中纳入的独立因素包括粪便频率≤5(15 分)、准备至结肠镜检查间隔时间≥5 h(15 分)、剂量不全(100 分)、非分剂量(90 分)、不限饮食(88 分)、无额外水分摄入(15 分)和最后粪便呈不透明液体(0-80 分)。训练集在识别肠道准备失败方面的表现如下:曲线下面积(AUC)为 0.818,准确度(ACC)为 0.818,阳性似然比(PLR)为 2.397,阴性似然比(NLR)为 0.162,阳性预测值(PPV)为 0.850,阴性预测值(NPV)为 0.723。在内部验证集中,这些指标分别为 0.747、0.776、2.099、0.278、0.866 和 0.538。外部验证集中,这些指标分别为 0.728、0.757、2.10、0.247、0.782 和 0.704,表明该模型具有较强的鉴别能力。校准曲线显示出良好的一致性,决策曲线分析表明,在训练队列中阈值概率为 0.73,验证队列中阈值概率为 0.75 时,该模型具有较好的临床获益。
该新型评分系统基于 7 个易于获取的变量,从前瞻性研究中建立,并在独立的一组中进行外部验证,在预测肠道准备不充分方面表现出稳健的性能。