Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study
Creators: name: Saux, Patrick
name: Bauvin, Pierre
name: Raverdy, Violeta
name: Teigny, Julien
name: Verkindt, Hélène
name: Soumphonphakdy, Tomy
name: Debert, Maxence
name: Jacobs, Anne
name: Jacobs, Daan
name: Monpellier, Valerie
name: Lee, Phong Ching
name: Lim, Chin Hong
name: Andersson-Assarsson, Johanna C
name: Carlsson, Lena
name: Svensson, Per-Arne
name: Galtier, Florence
name: Dezfoulian, Guelareh
name: Moldovanu, Mihaela
name: Andrieux, Severine
name: Couster, Julien
name: Lepage, Marie
name: Lembo, Erminia
name: Verrastro, Ornella
name: Robert, Maud
name: Salminen, Paulina
name: Mingrone, Geltrude
name: Peterli, Ralph
name: Cohen, Ricardo V
name: Zerrweck, Carlos
name: Nocca, David
name: Roux, Carel W Le
name: Caiazzo, Robert
name: Preux, Philippe
name: Pattou, François
Publisher: name: arXiv
Year: 2023
Subjects: subject: Machine Learning (cs.LG)
subject: Applications (stat.AP)
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. InterpretationWe developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.
description: The Lancet Digital Health, 2023