Personalizing withdrawal time by insertion time to achieve target adenoma detection rate in colonoscopy
Year: 2025
Type: article
Study design: Observational Study
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.
Institutions: Wuxi People's Hospital, Nanjing Medical University
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Awards: G1062629391, G1198043368, G1300722292, G1309204960, China Kadoorie Biobank of 0.5 million adults, China Kadoorie Biobank (CKB) prospective study of 0.5 million adults, G1630586902, China Kadoorie Biobank (CKB) of 0.5 million adults., G1915379896, G2196673094, G2226497579, G2343743920, G2579291025, G2602018492, G2750614948, G2756121469, G2769288755, G2787469147, G2804614353, G297046553, G3615493706, The Kadoorie Biobank study., G3910655000, G4051821870, G4336526930, G4370450388, G4393287264, G4420358607, G4592251203, G5158796881, China Kadoorie Biobank (CKB) of 0.5 million adults., The Kadoorie Biobank study., G5514164640, G5619910883, G5984111722, G6132904278, G6215260116, G6959217156, G7086945402, G7855137468, China Kadoorie Biobank of 0.5 million adults, G8368010179, G8643922336, G8894623477, G948687719
OpenAlex ID: W4415114396