Development of a predictive model for postoperative body mass index and diabetes outcomes after metabolic bariatric surgery: retrospective cohort study

BACKGROUND: Predicting postoperative body mass index (BMI) trajectories and long-term type 2 diabetes (T2D) remission after bariatric surgery remains challenging. Existing models often rely on baseline variables only and fail to incorporate dynamic postoperative changes. This study aimed to develop and validate a multicentre machine-learning framework that predicts individualized BMI trajectories and T2D remission using routinely available preoperative data and time-dependent weight evolution. METHODS: This multicentre retrospective cohort study included adult patients who underwent Roux-en-Y gastric bypass or sleeve gastrectomy across 11 European centres (2012-2023). Variables with > 30% missing data were excluded; remaining missing values were imputed iteratively. A two-stage approach was used: a regression model predicting postoperative BMI at 3-60 months using an autoregressive design; and a classification model predicting T2D remission using baseline features and predicted BMI trajectories. Internal performance was evaluated with ten-fold and leave-one-clinic-out cross-validation; external validation used an independent cohort from Linköping, Sweden. RESULTS: Of the 11 457 patients initially identified, 9652 patients with complete baseline and follow-up information were used for the analysis. The best BMI model (HistGradientBoosting) achieved a root mean square error (RMSE) of 1.11 kg/m2 (95% confidence interval 1.07 to 1.14) and a mean absolute error (MAE) of 0.62 kg/m2 across clinics; external testing showed an RMSE of 1.12 kg/m2 (95% confidence interval 1.11 to 1.12) and an MAE of 0.63 kg/m2. The T2D remission classifier (XGBoost) obtained a Macro F1 score of 0.88 (precision 0.87, recall 0.88), with an external F1 score of 0.89. Incorporating predicted BMI trajectories improved discrimination compared with baseline-only models (C-index 0.95 versus 0.93). CONCLUSION: A two-stage machine-learning framework has high predictive performance for postoperative BMI and T2D remission up to 5 years after bariatric surgery. Dynamic incorporation of predicted weight trajectories enhances metabolic risk prediction and supports individualized counselling and postoperative management.