Risk factor analysis and predictive model construction for postoperative bleeding in early esophageal cancer.

Abstract

Objective: A multivariate logistic regression model was developed to identify the risk factors for postoperative bleeding in patients undergoing endoscopic submucosal dissection (ESD) for early esophageal cancer.

Methods: The clinical data of 258 patients with early esophageal cancer who received ESD in Jiujiang Number One People's Hospital from April 2019 to March 2022 were retrospectively analyzed. Patients with or without postoperative bleeding were included into a bleeding group and a control group, respectively, and general information with statistically significant difference between the two groups was included in the multivariate logistic regression model to screen the risk factors for postoperative bleeding in the patients. The risk factors were then used to construct a nomogram prediction model for postoperative bleeding, and internal (training set) and external (validation set) validation was performed.

Results: (1) The incidence of post-ESD bleeding was 12.02% in the 258 patients with early esophageal cancer. (2) History of hypertension, lesion diameter, submucosal fibrosis, C-reactive protein (CRP), and albumin (ALB) were independent risk factors for postoperative bleeding after ESD in the patients (P<0.05). (3) The results of receiver operator characteristic curve (ROC) showed that the area under the curve (AUC) was 0.821 for the training set and 0.740 for the validation set. (4) The correction curve showed that the actual and predicted values of the training and validation sets were well fitted.

Conclusion: Hypertension history, lesion diameter, submucosal fibrosis, CRP, and ALB are risk factors for postoperative bleeding in patients with early esophageal cancer undergoing ESD. The nomograms established based on these factors has good predictive value for postoperative bleeding in these patients.