Development and External Validation of a Prediction Model for Colorectal Cancer Among Patients Awaiting Surveillance Colonoscopy Following Polypectomy

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Background and Aims: Demand for surveillance colonoscopy can sometimes exceed capacity, such as during and following the coronavirus disease 2019 pandemic, yet no tools exist to prioritize the patients most likely to be diagnosed with colorectal cancer (CRC) among those awaiting surveillance colonoscopy. We developed a multivariable prediction model for CRC at surveillance comparing performance to a model that assigned patients as low or high risk based solely on polyp characteristics (guideline-based model). Methods: Logistic regression was used for model development among patients receiving surveillance colonoscopy in 2014-2019. Candidate predictors included index colonoscopy indication, findings, and endoscopist adenoma detection rate, and patient and clinical characteristics at surveillance. Patients were randomly divided into model development (n = 36,994) and internal validation cohorts (n = 15,854). External validation was performed on 30,015 patients receiving surveillance colonoscopy in 2020-2022, and the multivariable model was then updated and retested. Results: One hundred fourteen, 43, and 71 CRCs were detected at surveillance in the 3 cohorts, respectively. Polyp size ≥10 mm, adenoma detection rate <32.5% or missing, patient age, and ever smoked tobacco were significant CRC predictors; this multivariable model outperformed the guideline-based model (internal validation cohort area under the receiver-operating characteristic curve: 0.73, 95% confidence interval (CI): 0.66-0.81 vs 0.52, 95% CI: 0.45-0.60). Performance declined at external validation but recovered with model updating (operating characteristic curve: 0.72 95% CI: 0.66-0.77). Conclusion: When surveillance colonoscopy demand exceeds capacity, a prediction model featuring common clinical predictors may help prioritize patients at highest risk for CRC among those awaiting surveillance. Also, regular model updates can address model performance drift.

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