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

Abstract

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 (R2). 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/m2 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 R2=0.24). Majority of models overestimated outcome with SE 5.03 to 5.13kg/m2. 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. Graphical abstract

Development and Internal/External Validation of a Prediction Model for Weight Loss Following Gastric Cancer Surgery: A Multicenter Retrospective Study

“…3B). Postoperative weight loss prediction models have been developed for bariatric surgeries and have been able to predict weight loss with an accuracy of R 2 ranging from 0.24 to 0.75 [20][21][22][23]. Even though the R 2 values in these studies were higher than ours, R 2 in these studies was still low.…”

“…The area in which research on predictive models of weight loss has been most active is bariatric surgery [20][21][22][23]. Predicting postoperative weight loss is also an important subject in other fields of gastrointestinal surgery, but only a few relevant studies have been conducted [24,25].…”

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“…Another potential implication of ingestive behavioral analyses may be the optimization of the preoperative counseling process. Existing models of bariatric weight loss prediction tend to overestimate the outcome ( 56 ). However, a recent study published by Perez-Leighton et al showed the utility of preoperative classification of patients according to their sucrose-wanting rating in the selection of BS type ( 57 ).…”

Current and Potential Applications of Artificial Intelligence in Metabolic Bariatric Surgery

“…As such, they can serve as an invaluable source of prediction, provided they undergo appropriate analysis with AI tools. Nevertheless, despite the accumulated experience and evidence with bariatric surgery and its effectiveness, to date there is no accurate tool for weight loss prediction, and most clinical models tend to overestimate the bariatric outcome of the most commonly performed procedures [54].…”