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You are here: Home / Abstracts / Sleeve Weight Loss Outcomes are Easily Predictable Using Common Pre-Operative Office Based Markers

Sleeve Weight Loss Outcomes are Easily Predictable Using Common Pre-Operative Office Based Markers

Samuel Cottam, Daniel Cottam, MD, Austin Cottam, BS, Hinali Zaveri, MD, Amit Surve, MD, Christina Richards, MD, FACS, Walter Medlin, MD. Bariatric Medicine Institute of Utah

Introduction: Average percent excess weight loss data is commonly discussed preoperatively to guide patient expectations following surgery.  However, there is a wide range and variation in weight loss following Vertical Sleeve Gastrectomy. Unfortunately, most surgeons and even fewer patients have heard of using predictive models to help guide their decisions on procedure choice.  We have developed a predictive model for Vertical Sleeve Gastrectomy to help patient choice prior to this major life changing decision. 

Method: 371 SG patients met the criteria for our study. These patients underwent surgery between October 2008 and June 2016. Non-linear regressions were performed to interpolate individual patient weights at one year. Multivariate analysis was used to find factors that effected weight loss.  A model was constructed to predict weight loss performance.

Results: Variables that affect weight loss were found to be preoperative BMI, age, Hypertension, and Diabetes. Diabetes and Hypertension together were found to significantly effect weight loss.

Effect of variables on weight loss at 1 year
Variable Effect on EWL at 1-year
BMI -1.22% per point BMI
HTN -1.53%
DM -.731%
HTN and DM -12.5%
Age -.304% per year

Conclusion: Patients weight loss can be accurately predicted by simple preoperative factors. These findings should be used to help patients and surgeons decide if the VSG is an appropriate surgery for each patient.   Using this model most patients can avoid failure by choosing an appropriate surgical approach for their personal circumstances.  


Presented at the SAGES 2017 Annual Meeting in Houston, TX.

Abstract ID: 84986

Program Number: P588

Presentation Session: iPoster Session (Non CME)

Presentation Type: Poster

38

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