Nomogram for predicting electrocoagulation syndrome after endoscopic submucosal dissection of esophageal tumors

Background Endoscopic submucosal dissection (ESD) was widely used for the removal of esophageal tumors, and post-endoscopic submucosal dissection electrocoagulation syndrome (PEECS) was one of the postoperative adverse events. The aim of this research was to develop and validate a model to predict electrocoagulation syndrome after endoscopic submucosal dissection of esophageal tumors. Materials and methods Patients who underwent esophageal ESD in our hospital were retrospectively included. A predictive nomogram was established based on the results of multivariate logistic regression analysis, and bootstrapping resampling was used for internal validation. Besides, the clinical usefulness of the nomogram was evaluated using decision curve analysis (DCA) and clinical impact curve. Results A total of 552 patients who underwent esophageal ESD were included in the study, and the incidence of PPECS was 12.5% (69/552). Risk factors associated with PEECS ( p < 0.1) were analyzed by multivariate logistic regression analysis, and the final model included four variables, namely gender, diabetes, tumor size and operation time. The predictive nomogram was constructed based on the above four variables, and the area under the ROC curve (AUC) was 0.811 (95% CI 0.767–0.855). The calibration curve of the nomogram presented good agreement between the predicted and actual probabilities. DCA showed that the model improved patient outcomes by helping to assess the risk of PEECS in patients compared to an all-or-no treatment strategy. In addition, the clinical impact curve of the model also indicates that the nomogram has a high clinical net benefit. Conclusion In conclusion, we have developed a predictive nomogram for PEECS after ESD for esophageal tumors with good predictive accuracy and discrimination. This predictive nomogram can be effectively used to identify high-risk patients with PEECS, which will help clinicians in clinical decision-making and early intervention.