Prediction of 30-Day Morbidity and Mortality After Conversion of Sleeve Gastrectomy to Roux-en-Y Gastric Bypass: Use of an Artificial Neural Network

Background: Conversion of sleeve gastrectomy to Roux-en-Y gastric bypass is indicated primarily for unsatisfactory weight loss or gastroesophageal reflux disease (GERD). This study aimed to use a comprehensive database to define predictors of 30-day reoperation, readmission, reintervention, or mortality. An artificial neural network (ANN) was employed to optimize prediction of the composite endpoint (occurrence of 1+ morbid event). Methods: Areview of 8895 patients who underwent conversion for weight-related or GERD-related indications was performed using the 2021 MBSAQIP national dataset. Demographics, comorbidities, laboratory values, and other factors were assessed for bivariate and subsequent multivariable associations with the composite endpoint (P ≤.05). Factors considered in the multivariable model were imputed into a three-node ANN with 20% randomly withheld for internal validation, to optimize predictive accuracy. Models were compared using receiver operating characteristic (ROC) curve analysis. Results: 39% underwent conversion for weight considerations and 61% for GERD. Rates of 30-day reoperation, readmission, reintervention, mortality, and the composite endpoint were 3.0%, 7.1%, 2.1%,.1%, and 9.1%, respectively. Of the nine factors associated with the composite endpoint on bivariate analysis, only non-white race (P <.001; odds ratio 1.4), lower body-mass index (P <.001; odds ratio.22), and therapeutic anticoagulation (P =.001; odds ratio 2.0) remained significant upon multivariable analysis. Areas under ROC curves for the multivariable regression, ANN training, and validation sets were.587,.601, and.604, respectively. Discussion: Identification of risk factors for morbidity after conversion offers critical information to improve patient selection and manage postoperative expectations. ANN models, with appropriate clinical integration, may optimize prediction of morbidity.