Prediction Model of Diabetes Remission at 1-Year after Sleeve Gastrectomy and Comparison with other Models

Although numerous prediction models are available for diabetes remission following metabolic bariatric surgery, few are based on sleeve gastrectomy (SG). This study aimed to establish a predictive model for type 2 diabetes mellitus (T2DM) remission following SG and evaluate the efficacy of existing predictive models.

Patient data were gathered from a cohort study titled “Longitudinal Study of Bariatric Surgery in Western China.” The synthetic minority oversampling technique was implemented, with 70% randomly selected as the training set and the remaining 30% as the testing set. Univariate logistic regression was used to identify factors associated with T2DM remission. These were included in subsequent stepwise multivariate analyses. A nomogram was then constructed. It was evaluated using a receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis. Finally, eight pre-existing predictive models were validated.

Initially, 166 patients were enrolled with a T2DM remission rate of 89.2%. Univariate logistic regression indicated that male patients, T2DM duration exceeding 1 year, elevated fasting blood glucose levels, and higher HbA1c levels were less likely to achieve remission 1 year following SG. A nomogram was constructed using variables, including sex, T2DM duration, and HbA1c levels. The ROC curve indicated that the nomogram had higher accuracy (AUC = 0.826, 95%CI: 0.768–0.884). Moreover, the AUCs were 0.790 (95%CI: 0.692–0.887), 0.865 (95%CI: 0.774–0.956) and 0.813 (95%CI: 0.733–0.893) for the testing, externally validated, and raw datasets, respectively.

The nomogram exhibited high efficacy in predicting T2DM remission in Chinese patients who underwent SG.