Constructing and validating a risk prediction model for postoperative bleeding after colorectal EMR in the Chinese
Bingfeng He1,2, Jiawei Zhang2,3, Mingli Su2,3
1Wuzhou Medical College, Xuzhou, China.
Background:
Endoscopic mucosal resection (EMR) is a minimally invasive treatment for early colorectal lesions. However, post-EMR clinically significant delayed bleeding (CSPEB) is a common complication affecting patient outcomes. Accurate risk prediction is essential for optimizing management and reducing complications.
Methods:
We conducted a retrospective study of 3888 patients who underwent colorectal EMR at Sun Yat-sen University's Sixth Affiliated Hospital from January 2018 to September 2024. External validation was performed using data from 1000 patients at Shenzhen Hospital of Southern Medical University (2022-2024). CSPEB was defined as postoperative lower gastrointestinal bleeding requiring endoscopic intervention within 30 days. Risk factors were identified using logistic regression. A random forest model with weighted sampling was developed and evaluated using ROC curves, calibration plots, and decision curve analysis (DCA). The model was implemented in a web-based application.
Results:
CSPEB occurred in 1.4% of patients. Six independent risk factors were identified: sigmoid location, lesion size, number of lesions, APTT, fibrinogen, and hemoclip count. The model achieved an AUC of 0.87 ± 0.04 (sensitivity 71%, specificity 86%). External validation showed an AUC of 0.80 (sensitivity 61.5%, specificity 89.0%). SHAP methods enhanced model interpretability. Calibration and DCA confirmed strong predictive performance and clinical utility.
Conclusion:
We developed and validated the first machine learning model for predicting post-EMR bleeding in the Chinese population. The model provides accurate, interpretable risk predictions and has been integrated into a user-friendly web tool to support clinical decision-making and improve patient outcomes.
