{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T06:04:50Z","timestamp":1791007490942,"version":"4.1.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008494","name":"Vontobel foundation","doi-asserted-by":"publisher","award":["0132\/2024"],"award-info":[{"award-number":["0132\/2024"]}],"id":[{"id":"10.13039\/501100008494","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This multicentre retrospective cohort study included adult patients who underwent Roux-en-Y gastric bypass or sleeve gastrectomy across 11 European centres (2012\u20132023). Variables with &amp;gt; 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\u201360 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 Link\u00f6ping, Sweden.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2005kg\/m2 (95% confidence interval 1.07 to 1.14) and a mean absolute error (MAE) of 0.62\u2005kg\/m2 across clinics; external testing showed an RMSE of 1.12\u2005kg\/m2 (95% confidence interval 1.11 to 1.12) and an MAE of 0.63\u2005kg\/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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjsopen\/zrag054","type":"journal-article","created":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T11:41:45Z","timestamp":1776598905000},"source":"Crossref","is-referenced-by-count":3,"title":["Development of a predictive model for postoperative body mass index and diabetes outcomes after metabolic bariatric surgery: retrospective cohort study"],"prefix":"10.1093","volume":"10","author":[{"given":"Vincent","family":"Ochs","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Medicine, University of Basel , Basel ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Kollmann","sequence":"additional","affiliation":[{"name":"Department of General, Visceral, Transplantation, Vascular, and Pediatric Surgery, University Hospital Wuerzburg , Wuerzburg ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilan","family":"Rosenblum","sequence":"additional","affiliation":[{"name":"Department of Visceral Surgery, Cantonal Hospital Baselland , Liestal ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adisa","family":"Poljo","sequence":"additional","affiliation":[{"name":"Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St.Clara Hospital and University Hospital Basel , Basel ,","place":["Switzerland"]},{"name":"Johannes Kepler University Linz, Medical Faculty , Linz ,","place":["Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Heule","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Medicine, University of Basel , Basel ,","place":["Switzerland"]},{"name":"Institute for Medical Informatics, University of Applied Sciences of Northwestern Switzerland , Muttenz ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bassey","family":"Enodien","sequence":"additional","affiliation":[{"name":"Department of Surgery, Cantonal Hospital Glarus , Glarus ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryna","family":"Chumakova-Orin","sequence":"additional","affiliation":[{"name":"Department of Surgery, East Carolina University, Brody School of Medicine , Greenville, North Carolina ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric J","family":"DeMaria","sequence":"additional","affiliation":[{"name":"Department of Surgery, East Carolina University, Brody School of Medicine , Greenville, North Carolina 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