Different factors, such as age, gender, preoperative weight but also the patient's motivation, are known to impact outcomes after Roux-en-Y gastric bypass (RYGBP). Weight loss prediction is helpful to define realistic expectations and maintain motivation during follow-up, but also to select good candidates for surgery and limit failures. Therefore, developing a realistic predictive tool appears interesting.
A Swiss cohort (n = 444), who underwent RYGBP, was used, with multiple linear regression models, to predict weight loss up to 60 months after surgery considering age, height, gender and weight at baseline. We then applied our model on two French cohorts and compared predicted weight to the one finally reached. Accuracy of our model was controlled using root mean square error (RMSE).
Mean weight loss was 43.6 ± 13.0 and 40.8 ± 15.4 kg at 12 and 60 months respectively. The model was reliable to predict weight loss (0.37 < R ²  < 0.48) and RMSE between 5.0 and 12.2 kg. High preoperative weight and young age were positively correlated to weight loss, as well as male gender. Correlations between predicted weight and real weight were highly significant in both validation cohorts (R ≥ 0.7 and P < 0.01) and RMSE increased throughout follow-up between 6.2 and 15.4 kg.
Our statistical model to predict weight loss outcomes after RYGBP seems accurate. It could be a valuable tool to define realistic weight loss expectations and to improve patient selection and outcomes during follow-up. Further research is needed to demonstrate the interest of this model in improving patients' motivation and results and limit the failures.