Related Experiment Video
Updated: Jul 5, 2026
Sleeve Gastrectomy in Mice using Surgical Clips
Published on: November 14, 2020
Development of a predictive model for postoperative body mass index and diabetes outcomes after metabolic bariatric
Vincent Ochs1, Lars Kollmann2, Ilan Rosenblum3
1Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Basel, Switzerland.
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.
