Establishment and Validation of Risk Prediction Models for Postoperative Pain After Endoscopic Submucosal Dissection:...

Area of Science:

• Gastroenterology
• Surgical Oncology
• Pain Management

Background:

• Postoperative pain is a frequent complication following endoscopic submucosal dissection (ESD).
• Effective management strategies require accurate prediction of patients likely to experience significant pain.
• Developing reliable predictive tools is crucial for optimizing patient care after ESD.

Purpose of the Study:

• To develop and validate predictive models for postoperative pain in patients undergoing ESD.
• To identify key predictors of post-ESD pain using statistical modeling.
• To assess the clinical utility of these predictive models.

Main Methods:

• Retrospective analysis of 2162 patients who underwent ESD.
• Development and internal validation of three nomogram models using logistic regression.
• Evaluation of model performance using ROC curves, calibration plots, and decision curve analysis.

Main Results:

• Three nomograms were constructed, incorporating preoperative, preoperative/intraoperative, and perioperative risk factors.
• The models demonstrated good predictive performance, with AUCs ranging from 0.702 to 0.823 in training and validation cohorts.
• Calibration plots and decision curve analysis indicated favorable clinical applicability.

Conclusions:

• Three robust nomogram models were successfully developed and validated for predicting post-ESD pain.
• These models can identify patients at higher risk of postoperative pain after ESD.
• The developed nomograms show promise for clinical application in patient management.