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Published on: February 16, 2024
A risk prediction model for delayed bleeding after ESD for gastric precancerous lesions
Yiying Zhu1, Mengyao Ji1,2, Lei Yuan3,4,2
1Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, 430060, Hubei, China.
Objective:
To investigate the risk factors for delayed postoperative bleeding after endoscopic submucosal dissection (ESD) in patients with gastric precancerous lesions and to construct a risk prediction model.
Methods:
This retrospective analysis included clinical data from patients with gastric precancerous lesions who underwent ESD at Wuhan University People's Hospital between November 2016 and June 2022. An XGBoost model was built to predict delayed bleeding after ESD using risk factors identified by univariable and multivariate logistic regression analysis. The model was evaluated using receiver operating characteristic curves (ROC), and SHapely Additive exPlanations (SHAP) analysis was used to interpret the model.
Results:
Seven factors were statistically associated with delayed postoperative bleeding in gastric precancerous lesions after ESD: age, low-grade intraepithelial neoplasia, hypertension, lesion size ≥ 40 mm, operative time ≥ 120 min, female, and nonuse of hemoclips. A risk prediction model was established. In the training cohort, the model achieved an AUC of 0.97 (0.96-0.98), a sensitivity of 0.90, a specificity of 0.94, and an F1 score of 0.91. In the validation cohort, the AUC was 0.94(0.90-0.98), with a sensitivity of 0.85, a specificity of 0.89, and an F1 score of 0.85. In the test cohort, the AUC was 0.94 (0.89-0.99), the sensitivity was 0.80, the specificity was 0.92, and the F1 score was 0.84, indicating strong predictive capability.
Conclusion:
In this study, an XGBoost prediction model for assessing the risk of delayed postoperative bleeding after ESD in patients with gastric precancerous lesions was developed and validated. This model can be applied in clinical practice to effectively predict the risk of post-ESD bleeding for individual patients.
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