Endoscopic sleeve gastroplasty reintervention score using supervised machine learning

Abstract

Reintervention after endoscopic sleeve gastroplasty (ESG) can be indicated because of postprocedural adverse events from various preinterventional or postprocedural comorbidities. We developed and internally validated an ESG reintervention score (ESG-RS) that determines the individualized risk of reintervention within the first 30 days after ESG. We used data from a sample of 3583 patients who underwent ESG in the Metabolic and Bariatric Surgery Accreditation Quality Improvement Program database (2016-2021). The least absolute shrinkage and selection operator (LASSO)-penalized regression was used to select the most promising predictors of reintervention after ESG within 30 days. The predictive variables extracted by LASSO regression were entered into multivariate analysis to generate an ESG-RS by using the coefficients of the statistically significant variables. The model performance was assessed using receiver-operator curves by 10-fold cross-validation. Eleven variables were selected by LASSO regression and used in the final multivariate analysis. The ESG-RS was inferred using 5 factors (history of previous foregut surgery, preoperative anticoagulation use, female gender, American Society of Anesthesiologists class≥II, and hypertension) weighted by their regression coefficients in the multivariable logistic regression model. The area under the curve of the ESG-RS was .74 (95% confidence interval, .70-.78). For the ESG-RS, the optimal cutpoint was 67.9 (high risk vs low risk), with a sensitivity of .76 and specificity of .71. The ESG-RS aids clinicians in preoperative risk stratification of patients undergoing ESG while clarifying factors contributing to a higher risk of reintervention.