Preoperative Prediction of Body Mass Index of Patients with Type 2 Diabetes at 1 Year After Laparoscopic Sleeve Gastrectomy: Cross-Sectional Study.

Abstract

Background: Very few models predict weight loss among type 2 diabetes mellitus (T2D) patients after laparoscopic sleeve gastrectomy (LSG). This retrospective study undertook such a task. Materials and Methods: We identified all patients >18 years old with T2D who underwent primary LSG at our institution and had complete data. The training set comprised 107 patients operated upon during the period April 2011 to June 2014; the validation set comprised 134 patients operated upon during the successive chronological period, July 2014 to December 2015. Sex, age, presurgery BMI, T2D duration, number of T2D medications, insulin use, hypertension, and dyslipidemia were utilized as independent predictors of 1-year BMI. We employed regression analysis, and assessed the goodness of fit and "Residuals versus Fits" plot. Paired sample t-tests compared the observed and predicted BMI at 1 year. Results: The model comprised preoperative BMI (β = 0.757, P = 0.026) + age (β = 0.142, P < 0.0001) with adjusted R2 of 0.581 (P < 0.0001), and goodness of fit showed an unbiased model with accurate prediction. The equation was: BMI value 1 year after LSG = 1.777 + 0.614 × presurgery BMI (kg/m2) +0.106 × age (years). For validation, the equation exhibited an adjusted R2 0.550 (P < 0.0001), and the goodness of fit indicated an unbiased model. The BMI predicted by the model fell within -3.78 BMI points to +2.42 points of the observed 1-year BMI. Pairwise difference between the mean 1-year observed and predicted BMI was not significant (-0.41 kg/m2, P = 0.225). Conclusions: This predictive model estimates the BMI 1 year after LSG. The model comprises preoperative BMI and age. It allows the forecast of patients' BMI after surgery, hence setting realistic expectations which are critical for patient satisfaction after bariatric surgery. An attainable target motivates the patient to achieve it.