Risk factors and prediction algorithm for advanced neoplasia on screening colonoscopy for average-risk individuals

BACKGROUND: Screening with colonoscopy for all average-risk population is probably not cost-effective due to the limited sources and over-generalization of the risk, and risk stratification can be used to optimize colorectal cancer screening. OBJECTIVES: We aimed to assess risk factors for advanced neoplasia (AN) and a classification tree algorithm to predict the risk. DESIGN: This is a retrospective cross-sectional study. METHODS: This study was composed of consecutive asymptomatic average-risk individuals undergoing first screening colonoscopy between 2008 and 2019. Detailed characteristics including background diseases, habits, and medications were collected. We used multivariable logistic regression to investigate the associations between clinical variables and the presence of AN and built a classification algorithm to predict AN. RESULTS: A total of 3856 patients were included (73.2% male, median age 55). Adenoma and AN detection rate were 15.8% and 3.4%, respectively. On multivariable analysis, predictors of AN [odds ratio (OR), 95% confidence interval (CI)] were age (1.04, 1.01–1.06, p = 0.003), male sex (2.69, 1.56–4.64, p < 0.001), and smoking (1.97, 1.38–2.81, p < 0.001). A classification tree algorithm showed that smoking was the most important risk factor for prediction of AN (4.9% versus 2.4%, p < 0.001), followed by age with a cutoff value of 60 in the smokers (8.4% versus 3.8%, p = 0.001) and 50 in the non-smokers (2.9% versus 0.9%, p = 0.004). CONCLUSION: Smoking habits, old age, and male gender are highly associated with an increased risk for AN and should be incorporated in the individualized risk-assessment to adapt a screening program.