Personalizing withdrawal time by insertion time to achieve target adenoma detection rate in colonoscopy

Abstract

BACKGROUND

Adenoma detection rate (ADR), a key colonoscopy quality metric, varies with patient demographics and procedural factors.

AIM

To identify independent predictors of ≥ 25% ADR, develop a risk model, and propose withdrawal durations based on different insertion times.

METHODS

We retrospectively analyzed 830 cases using logistic regression and identified four key factors, validated in a prospective cohort of 5699 patients. Their importance was confirmed using random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM). Attempts to determine target-achieving withdrawal time by grouping cases based on insertion time and Cox regression were inconclusive. Using the 5699-case dataset, we developed a predictive model combining support vector machine (SVM) with XGBoost. We built a Shiny app using this model for clinical application.

RESULTS

Multivariate logistic regression identified age [odds ratio (OR) = 1.05; 95% confidence interval (CI): 1.03-1.08; P < 0.001], male (OR = 1.79; 95%CI: 1.32-2.41; P = 0.005), higher endoscopist experience (OR = 1.79; 95%CI: 1.20-2.68; P = 0.005), and longer withdrawal time (P < 0.001) as independent risk factors for colorectal adenoma. A nomogram demonstrated strong discrimination [area under the curve (AUC) = 0.720], with robust calibration and decision-curve performance. Feature importance via RF, XGBoost, and LightGBM confirmed key predictors. A hybrid model combining SVM regression for withdrawal-time estimation and XGBoost classification achieved stable results, with XGBoost reporting AUCs of 0.640 in training and 0.610 in testing, and similar validation outcomes. Deployed via a Shiny app for clinical use. However, model discrimination was modest (AUC: 0.61-0.64), suggesting that clinical utility requires further refinement.

CONCLUSION

A hybrid SVM-XGBoost model using four key endoscopic factors was independently validated and is available as a Shiny app, delivering real-time decision support to streamline endoscopy and enhance clinical outcomes.

Keywords: Colonoscopy; Withdrawal time; Insertion time; Adenoma detection rate; Machine learning

Core Tip: This study identifies four key predictors of ≥ 25% adenoma detection rate (ADR), age, sex, endoscopist experience, and withdrawal time and develops a validated hybrid support vector machine extreme gradient boosting model to predict ADR. Using data from 6529 colonoscopies, the model demonstrated stable performance and was implemented in a Shiny app for real-time clinical use. This innovative tool offers personalized guidance to optimize colonoscopy quality and improve clinical outcomes, representing a significant advancement in endoscopic decision support.