An Algorithm to Predict Advanced Proximal Colorectal Neoplasia in Chinese Asymptomatic Population

This study aims to develop and validate a new algorithm that incorporates distal colonoscopic findings to predict advanced proximal neoplasia (APN) in a Chinese asymptomatic population. We collected age, gender, and colonoscopic findings from a prospectively performed colonoscopy study between 2013 and 2015 in a large hospital-based endoscopy unit in Shanghai, China. Eligible subjects were allocated to a derivation group (n = 3,889) and validation group (n = 1,944) by random sampling. A new index for APN and its cut-off level were evaluated from the derivation cohort by binary logistic regression. The model performance was tested in the validation cohort using area under the curve (AUC). Age, gender, and distal finding were found to be independent predictors of APN in the derivation cohort (p < 0.001). Subjects were categorized into Average Risk (AR) and High Risk (HR) based on a cut-off score of 2. The AUC of the derivation and validation cohorts were 0.801 (0.754–0.847) and 0.722 (0.649–0.794), respectively. In the validation cohort, those in the HR group had a 3.57 fold higher risk of APN when compared with the AR group (P < 0.001), requiring 18 (95% CI = 12–28) follow-up colonoscopies to detect 1 APN. This new clinical index is useful to stratify APN risk in Chinese population.