Logo do repositório
 
Publicação

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

2026-08Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorOchs, Vincent
dc.contributor.authorKollmann, Lars
dc.contributor.authorRosenblum, Ilan
dc.contributor.authorPoljo, Adisa
dc.contributor.authorHeule, Andreas
dc.contributor.authorEnodien, Bassey
dc.contributor.authorChumakova-Orin, Maryna
dc.contributor.authorDeMaria, Eric J.
dc.contributor.authorBurri, Emanuel
dc.contributor.authorStoll, Reinhard
dc.contributor.authorKollmar, Otto
dc.contributor.authorRosenberg, Robert
dc.contributor.authorProbst, Pascal
dc.contributor.authorMuller, Markus K.
dc.contributor.authorTaha-Mehlitz, Stephanie
dc.contributor.authorMüller, Beat P.
dc.contributor.authorFrey, Daniel M.
dc.contributor.authorKalinowski, Piotr
dc.contributor.authorPrzybysz, Marta
dc.contributor.authorBartkowiak, Mateusz
dc.contributor.authorDalkilic, Muhammed Said
dc.contributor.authorSisik, Abdullah
dc.contributor.authorOliveira, Rodrigo Otavio Carvalho de
dc.contributor.authorMartins, Fatima
dc.contributor.authorStenberg, Erik
dc.contributor.authorAndersson, Ellen
dc.contributor.authorOlbers, Torsten
dc.contributor.authorFlemming, Sven
dc.contributor.authorSeyfried, Florian
dc.contributor.authorPonholzer, Florian
dc.contributor.authorWeissenbacher, Annemarie
dc.contributor.authorÖfner, Dietmar
dc.contributor.authorBetzler, Johanna
dc.contributor.authorOtto, Mirko
dc.contributor.authorPeterli, Ralph
dc.contributor.authorCattin, Philippe C.
dc.contributor.authorTaha, Anas
dc.contributor.institutioniNOVA4Health - pólo NMS
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.pblOxford University Press
dc.date.accessioned2026-07-15T10:28:01Z
dc.date.available2026-07-15T10:28:01Z
dc.date.issued2026-08
dc.descriptionPublisher Copyright: © The Author(s) 2026. Published by Oxford University Press on behalf of BJS Foundation Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.description.abstractBackground: 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent367615
dc.identifier.doi10.1093/bjsopen/zrag054
dc.identifier.issn2474-9842
dc.identifier.otherPURE: 168313980
dc.identifier.otherPURE UUID: 374f5579-7681-4d42-aac7-f874ab25a0ec
dc.identifier.otherScopus: 105043811167
dc.identifier.urihttp://hdl.handle.net/10362/204532
dc.identifier.urlhttps://www.scopus.com/pages/publications/105043811167
dc.language.isoeng
dc.peerreviewedyes
dc.subjectdiabetes remission
dc.subjectmachine learning
dc.subjectpostoperative weight loss
dc.subjectrisk prediction
dc.subjectSurgery
dc.subjectSDG 3 - Good Health and Well-being
dc.titleDevelopment of a predictive model for postoperative body mass index and diabetes outcomes after metabolic bariatric surgeryen
dc.title.subtitleretrospective cohort studyen
dc.typejournal article
degois.publication.issue4
degois.publication.titleBJS Open
degois.publication.volume10
dspace.entity.typePublication
rcaap.rightsopenAccess

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
zrag054.pdf
Tamanho:
359 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NMS: iNOVA4Health - Artigos em revista internacional com arbitragem científica