Published online Nov 16, 2023. doi: 10.4253/wjge.v15.i11.649
Peer-review started: September 5, 2023
First decision: September 13, 2023
Revised: September 21, 2023
Accepted: October 16, 2023
Article in press: October 16, 2023
Published online: November 16, 2023
Processing time: 65 Days and 15.5 Hours
Gas-related complications present a potential risk during transoral endoscopic resection of upper gastrointestinal submucosal lesions. Therefore, the identification of risk factors associated with these complications is essential.
To develop a nomogram to predict risk of gas-related complications following transoral endoscopic resection of the upper gastrointestinal submucosal lesions.
We collected patient data from the First Affiliated Hospital of the Army Medical University. Patients were randomly allocated to training and validation cohorts. Risk factors for gas-related complications were identified in the training cohort using univariate and multivariate analyses. We then constructed a nomogram and evaluated its predictive performance based on the area under the curve, decision curve analysis, and Hosmer-Lemeshow tests.
Gas-related complications developed in 39 of 353 patients who underwent transoral endoscopy at our institution. Diabetes, lesion origin, surgical resection method, and surgical duration were incorporated into the final nomogram. The predictive capability of the nomogram was excellent, with area under the curve values of 0.841 and 0.906 for the training and validation cohorts, respectively.
The ability of our four-variable nomogram to efficiently predict gas-related complications during transoral endoscopic resection enhanced postoperative assessments and surgical outcomes.
Core Tip: This is a retrospective study to create a nomogram that efficiently evaluates the risk of gas-related complications in patients undergoing transoral endoscopic resection of upper gastrointestinal submucosal lessions. Our study excluded upper gastrointestinal malignancies and explored risk factors for gas-related complications during transoral endoscopic resection. Predictive models were developed based on diabetes status, lesion origin layer, operative resection technique, and duration of the operation.