Is It Possible to Predict Weight Loss After Bariatric Surgery?-External Validation of Predictive Models

Background Bariatric surgery is the most effective obesity treatment. Weight loss varies among patients, and not everyone achieves desired outcome. Identification of predictive factors for weight loss after bariatric surgery resulted in several prediction tools proposed. We aimed to validate the performance of available prediction models for weight reduction 1 year after surgical treatment. Materials and Methods The retrospective analysis included patients after Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) who completed 1-year follow-up. Postoperative body mass index (BMI) predicted by 12 models was calculated for each patient. The correlation between predicted and observed BMI was assessed using linear regression. Accuracy was evaluated by squared Pearson's correlation coefficient (R-2). Goodness-of-fit was assessed by standard error of estimate (SE) and paired sample t test between estimated and observed BMI. Results Out of 760 patients enrolled, 509 (67.00%) were women with median age 42 years. Of patients, 65.92% underwent SG and 34.08% had RYGB. Median BMI decreased from 45.19 to 32.53kg/m(2) after 1 year. EWL amounted to 62.97%. All models presented significant relationship between predicted and observed BMI in linear regression (correlation coefficient between 0.29 and 1.22). The best predictive model explained 24% variation of weight reduction (adjusted R-2=0.24). Majority of models overestimated outcome with SE 5.03 to 5.13kg/m(2). Conclusion Although predicted BMI had reasonable correlation with observed values, none of evaluated models presented acceptable accuracy. All models tend to overestimate the outcome. Accurate tool for weight loss prediction should be developed to enhance patient's assessment.

Automated summary

This study aimed to validate various prediction models for weight reduction after bariatric surgery. While there was reasonable correlation between predicted and observed BMI, the models tended to overestimate the outcome, highlighting the need for accurate tools for weight loss prediction.