To explore the influencing factors of post endoscopic submucosal dissection (ESD) electrocoagulation syndrome (PEECS) in esophageal tumors, construct a nomogram prediction model, and validate it.

A total of 1 336 patients with esophageal tumor admitted to Jiangsu Provincial People's Hospital from January 2019 to January 2024 were selected as the study objects. Clinical data such as gender, tumor location and pathological classification of patients were collected. Patients were randomly divided into modeling group and validation group at a ratio of 7∶3, and clinical data of patients in the two groups were compared. The clinical data of patients in the modeling group were used, and the occurrence of PEECS was taken as the dependent variable to conduct univariate and multi-factor analysis. Based on the results of multi-factor analysis, the nomogram model was constructed, and the differentiation, prediction accuracy, fit degree and clinical effectiveness of the model were verified.

Among 1 336 patients, 135 developed PEECS, with an incidence rate of 10.10%. Based on the clinical data of patients in the modeling group, the results of single factor analysis showed that there were statistically significant differences between non PEECS and PEECS patients in gender, pathological classification, tumor nature, intraoperative bleeding, diabetes, age, tumor diameter, and operation time (P<0.05); Multivariate analysis showed that gender (OR=3.228, 95% CI: 1.521-6.851), diabetes (OR=7.666, 95% CI: 1.614-36.400), tumor diameter (OR=1.240, 95% CI: 1.167-1.317), and operation time (OR=1.185, 95% CI: 1.142-1.230) were all independent risk factors for PEECS in patients with esophageal tumors (P<0.05). A nomogram graphical model was constructed based on the results of the multifactorial analysis of the modeling group, and the results of the ROC curve analysis showed that the AUC=0.954 (95% CI: 0.930-0.979); the Hosmer-Lemeshow test showed that χ²=3.678 (P=0.885). The results of the DCA curves and the CIC analyses showed that, with the risk thresholds in the range of 0.0 to 1.0, the use of the model to predict PEECS in esophageal tumors resulted in a high positive net benefit, and the use of the model was more accurate in predicting whether PEECS occurred in esophageal tumors.

This study constructs a nomogram model with good discrimination and high prediction accuracy for PEECS in esophageal tumors, which has good clinical efficacy and can provide reference for early intervention of PEECS in esophageal tumor patients.