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.

翻译：摘要 背景 减重手术后个体间的体重下降轨迹差异显著，术前预测减重效果仍面临挑战。本研究旨在通过机器学习模型对术后5年个体体重下降轨迹进行术前预测。方法 这项跨国回顾性观察研究纳入来自欧洲、美洲及亚洲10个前瞻性队列（包括ABOS [NCT01129297]、BAREVAL [NCT02310178]、瑞典肥胖受试者研究及荷兰肥胖诊所大规模队列）和两项随机试验（SleevePass [NCT00793143]与SM-BOSS [NCT00356213]）的成年参与者（年龄≥18岁），术后随访5年，涉及Roux-en-Y胃旁路术、袖状胃切除术或胃束带术。排除有减重手术史或计划就诊与实际就诊时间间隔过长的患者。训练队列包含来自法国两个中心（ABOS和BAREVAL）的患者。主要结局指标为5年时身体质量指数（BMI）。采用最小绝对收缩与选择算子筛选变量，并通过分类与回归树算法构建可解释的回归树模型。通过BMI的中位绝对偏差和均方根误差评估模型性能。结果 共纳入来自10个国家12个中心的10231例患者，对应30602患者-年。在全部12个队列参与者中，女性7701例（75.3%），男性2530例（24.7%）。从训练队列可用的434项基线特征中筛选出7个变量：身高、体重、手术类型、年龄、糖尿病状态、糖尿病病程及吸烟状态。在5年时间点，外部测试队列的整体平均MAD BMI为2.8 kg/m²（95%CI 2.6-3.0），平均RMSE BMI为4.7 kg/m²（95%CI 4.4-5.0），预测BMI与实测BMI的平均差值为-0.3 kg/m²（SD 4.7）。该模型已整合至易于使用且具备可解释性的网络预测工具中，以辅助术前临床决策。解释 我们开发了经过国际验证的机器学习模型，用于预测三种常见减重干预措施术后5年的个体化体重下降轨迹。