Comparative Study of Predictive Models for the Detection of Patients at High Risk of Inadequate Colonic Cleansing

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Background: Various predictive models have been published to identify outpatients with inadequate colonic cleansing who may benefit from intensified preparations to improve colonoscopy quality. The main objective of this study was to compare the accuracy of three predictive models for identifying poor bowel preparation in outpatients undergoing colonoscopy. Methods: This cross-sectional study included patients scheduled for outpatient colonoscopy over a 3-month period. We evaluated and compared three predictive models (Models 1–3). The quality of colonic cleansing was assessed using the Boston Bowel Preparation Scale. We calculated the area under the curve (AUC) and the corresponding 95% confidence interval for each model. Additionally, we performed simple and multiple logistic regression analyses to identify variables associated with inadequate colonic cleansing and developed a new model. Results: A total of 649 consecutive patients were included in the study, of whom 84.3% had adequate colonic cleansing quality. The AUCs of Model 1 (AUC = 0.67, 95% CI [0.63–0.70]) and Model 2 (AUC = 0.62, 95% CI [0.58–0.66]) were significantly higher than that of Model 3 (AUC = 0.54, 95% CI [0.50–0.58]; p < 0.001). Moreover, Model 1 outperformed Model 2 (p = 0.013). However, the new model did not demonstrate improved accuracy compared to the older models (AUC = 0.671). Conclusions: Among the three compared models, Model 1 showed the highest accuracy for predicting poor bowel preparation in outpatients undergoing colonoscopy and could be useful in clinical practice to decrease the percentage of inadequately prepared patients.

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