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Background Weight loss trajectories after bariatric surgery vary widelybetween individuals, and predicting weight loss before the operation remainschallenging. We aimed to develop a model using machine learning to provideindividual preoperative prediction of 5-year weight loss trajectories aftersurgery. Methods In this multinational retrospective observational study weenrolled adult participants (aged $\ge$18 years) from ten prospective cohorts(including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish ObeseSubjects study, and a large cohort from the Dutch Obesity Clinic [NederlandseObesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] andSM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 yearfollowup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band.Patients with a previous history of bariatric surgery or large delays betweenscheduled and actual visits were excluded. The training cohort comprisedpatients from two centres in France (ABOS and BAREVAL). The primary outcome wasBMI at 5 years. A model was developed using least absolute shrinkage andselection operator to select variables and the classification and regressiontrees algorithm to build interpretable regression trees. The performances ofthe model were assessed through the median absolute deviation (MAD) and rootmean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres inten countries were included in the analysis, corresponding to 30 602patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) werefemale, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes availablein the training cohort, seven variables were selected: height, weight,intervention type, age, diabetes status, diabetes duration, and smoking status.At 5 years, across external testing cohorts the overall mean MAD BMI was2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean differencebetween predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7).This model is incorporated in an easy to use and interpretable web-basedprediction tool to help inform clinical decision before surgery.InterpretationWe developed a machine learning-based model, which isinternationally validated, for predicting individual 5-year weight losstrajectories after three common bariatric interventions.

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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