Multivariable Models for Advanced Colorectal Neoplasms in Screen-Eligible Individuals at Low-to-Moderate Risk of Colorectal Cancer: Towards Improving Colonoscopy Prioritization 

Background: Advanced colorectal neoplasms (ACNs), including colorectal cancers (CRC) and high-risk adenomas (HRA), are detected in less than 20% of persons aged 50 years or older who undergo colonoscopy. We sought to derive predictive models that identify persons at high risk of harbouring ACNs to improve the rational allocation of colonoscopy resources. Methods We characterized neoplastic findings through chart review for consecutive individuals aged 50 years or older who underwent outpatient colonoscopy at The Ottawa Hospital (Ottawa, Canada) between April 1, 2008 and March 31, 2012 for low-to-moderate risk indications. We collected candidate predictors from chart review and population-level health administrative datasets to derive multivariable logistic regression models for risk of harboring ACNs at colonoscopy. We assessed model discriminatory capacity and calibration and ability of the models to improve colonoscopy specificity while maintaining excellent sensitivity for ACN capture. Results We modelled 17 candidate predictors in 11,724 individuals. The final CRC model comprised 8 variables and had a c-statistic value of 0.957 and a goodness-of-fit p-value of 0.527. Application of the models to our cohort permitted 100% sensitivity for identifying persons with CRC and > 90% sensitivity for identifying persons with HRA, while improving colonoscopy specificity for ACNs by 23.8%. Conclusions Our multivariable models show excellent discriminatory capacity for persons with ACNs and can significantly increase colonoscopy specificity. If validated, these models could allow more efficient allocation of colonoscopy resources, potentially reducing wait times for those at higher risk while deferring unnecessary colonoscopies in low-risk individuals.