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.

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 ≥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 follow-up 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. FINDINGS: 10 231 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 (75·3%) were female, 2530 (24·7%) 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 2·8 kg/m2 (95% CI 2·6-3·0) and mean RMSE BMI was 4·7 kg/m2 (4·4-5·0), and the mean difference between predicted and observed BMI was -0·3 kg/m2 (SD 4·7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. INTERPRETATION: We developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions. FUNDING: SOPHIA Innovative Medicines Initiative 2 Joint Undertaking, supported by the EU's Horizon 2020 research and innovation programme, the European Federation of Pharmaceutical Industries and Associations, Type 1 Diabetes Exchange, and the Juvenile Diabetes Research Foundation and Obesity Action Coalition; Métropole Européenne de Lille; Agence Nationale de la Recherche; Institut national de recherche en sciences et technologies du numérique through the Artificial Intelligence chair Apprenf; Université de Lille Nord Europe's I-SITE EXPAND as part of the Bandits For Health project; Laboratoire d'excellence European Genomic Institute for Diabetes; Soutien aux Travaux Interdisciplinaires, Multi-établissements et Exploratoires programme by Conseil Régional Hauts-de-France (volet partenarial phase 2, project PERSO-SURG).

Impact of Robotic Assistance on Complications in Bariatric Surgery at Expert Laparoscopic Surgery Centers: A Retrospective Comparative Study With Propensity Score.

OBJECTIVE: To investigate the way robotic assistance affected rate of complications in bariatric surgery at expert robotic and laparoscopic surgery facilities. BACKGROUND: While the benefits of robotic assistance were established at the beginning of surgical training, there is limited data on the robot's influence on experienced bariatric laparoscopic surgeons. METHODS: We conducted a retrospective study using the BRO clinical database (2008-2022) collecting data of patients operated on in expert centers. We compared the serious complication rate (defined as a Clavien score≥3) in patients undergoing metabolic bariatric surgery with or without robotic assistance. We used a directed acyclic graph to identify the variables adjustment set used in a multivariable linear regression, and a propensity score matching to calculate the average treatment effect (ATE) of robotic assistance. RESULTS: The study included 35,043 patients [24,428 sleeve gastrectomy (SG); 10,452 Roux-en-Y gastric bypass (RYGB); 163 single anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S)], with 938 operated on with robotic assistance (801 SG; 134 RYGB; 3 SADI-S), among 142 centers. Overall, we found no benefit of robotic assistance regarding the risk of complications (average treatment effect=-0.05, P =0.794), with no difference in the RYGB+SADI group ( P =0.322) but a negative trend in the SG group (more complications, P =0.060). Length of hospital stay was decreased in the robot group (3.7±11.1 vs 4.0±9.0 days, P <0.001). CONCLUSIONS: Robotic assistance reduced the length of stay but did not statistically significantly reduce postoperative complications (Clavien score≥3) following either GBP or SG. A tendency toward an elevated risk of complications following SG requires more supporting studies.