Eric S Wise1,2, Kyle M Hocking3,4, Stephen M Kavic5. 1. Department of Surgery, Vanderbilt University Medical Center, 1161 21st Ave S, MCN T2121, Nashville, TN, 37232-2730, USA. eric.s.wise@vanderbilt.edu. 2. Department of General Surgery, University of Maryland Medical Center, Baltimore, MD, USA. eric.s.wise@vanderbilt.edu. 3. Department of Surgery, Vanderbilt University Medical Center, 1161 21st Ave S, MCN T2121, Nashville, TN, 37232-2730, USA. 4. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. 5. Department of General Surgery, University of Maryland Medical Center, Baltimore, MD, USA.
Abstract
INTRODUCTION: Laparoscopic Roux-en-Y gastric bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body mass index loss (%EBMIL) over 25 kg/m(2), which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50% EBMIL at 1 year postoperatively. METHODS: Six hundred and forty-seven LRYGB included patients were retrospectively reviewed for preoperative factors independently associated with EBMIL at 180 and 365 days postoperatively (EBMIL180 and EBMIL365, respectively). Previously validated factors were selectively analyzed, including age; race; gender; preoperative BMI (BMI0); hemoglobin; and diagnoses of hypertension (HTN), diabetes mellitus (DM), and depression or anxiety disorder. Variables significant upon multivariate analysis (P < .05) were modeled by "traditional" multiple linear regression and an ANN, to predict %EBMIL180 and %EBMIL365. RESULTS: The mean EBMIL180 and EBMIL365 were 56.4 ± 16.5 % and 73.5 ± 21.5%, corresponding to total body weight losses of 25.7 ± 5.9% and 33.6 ± 8.0%, respectively. Upon multivariate analysis, independent factors associated with EBMIL180 included black race (B = -6.3%, P < .001), BMI0 (B = -1.1%/unit BMI, P < .001), and DM (B = -3.2%, P < .004). For EBMIL365, independently associated factors were female gender (B = 6.4%, P < .001), black race (B = -6.7%, P < .001), BMI0 (B = -1.2%/unit BMI, P < .001), HTN (B = -3.7%, P = .03), and DM (B = -6.0%, P < .001). Pearson r(2) values for the multiple linear regression and ANN models were 0.38 (EBMIL180) and 0.35 (EBMIL365), and 0.42 (EBMIL180) and 0.38 (EBMIL365), respectively. ANN prediction of benchmark 50% EBMIL at 365 days generated an area under the curve of 0.78 ± 0.03 in the training set (n = 518) and 0.83 ± 0.04 (n = 129) in the validation set. CONCLUSIONS: Available at https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR, this or other ANN models may be used to provide an optimized estimate of postoperative EBMIL following LRYGB.
INTRODUCTION: Laparoscopic Roux-en-Y gastric bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body mass index loss (%EBMIL) over 25 kg/m(2), which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50% EBMIL at 1 year postoperatively. METHODS: Six hundred and forty-seven LRYGB included patients were retrospectively reviewed for preoperative factors independently associated with EBMIL at 180 and 365 days postoperatively (EBMIL180 and EBMIL365, respectively). Previously validated factors were selectively analyzed, including age; race; gender; preoperative BMI (BMI0); hemoglobin; and diagnoses of hypertension (HTN), diabetes mellitus (DM), and depression or anxiety disorder. Variables significant upon multivariate analysis (P < .05) were modeled by "traditional" multiple linear regression and an ANN, to predict %EBMIL180 and %EBMIL365. RESULTS: The mean EBMIL180 and EBMIL365 were 56.4 ± 16.5 % and 73.5 ± 21.5%, corresponding to total body weight losses of 25.7 ± 5.9% and 33.6 ± 8.0%, respectively. Upon multivariate analysis, independent factors associated with EBMIL180 included black race (B = -6.3%, P < .001), BMI0 (B = -1.1%/unit BMI, P < .001), and DM (B = -3.2%, P < .004). For EBMIL365, independently associated factors were female gender (B = 6.4%, P < .001), black race (B = -6.7%, P < .001), BMI0 (B = -1.2%/unit BMI, P < .001), HTN (B = -3.7%, P = .03), and DM (B = -6.0%, P < .001). Pearson r(2) values for the multiple linear regression and ANN models were 0.38 (EBMIL180) and 0.35 (EBMIL365), and 0.42 (EBMIL180) and 0.38 (EBMIL365), respectively. ANN prediction of benchmark 50% EBMIL at 365 days generated an area under the curve of 0.78 ± 0.03 in the training set (n = 518) and 0.83 ± 0.04 (n = 129) in the validation set. CONCLUSIONS: Available at https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR, this or other ANN models may be used to provide an optimized estimate of postoperative EBMIL following LRYGB.
Authors: Christopher D Still; G Craig Wood; Xin Chu; Christina Manney; William Strodel; Anthony Petrick; Jon Gabrielsen; Tooraj Mirshahi; George Argyropoulos; Jamie Seiler; Marco Yung; Peter Benotti; Glenn S Gerhard Journal: Obesity (Silver Spring) Date: 2014-02-06 Impact factor: 5.002
Authors: G Craig Wood; Peter Benotti; Glenn S Gerhard; Elaina K Miller; Yushan Zhang; Richard J Zaccone; George A Argyropoulos; Anthony T Petrick; Christopher D Still Journal: J Obes Date: 2014-03-20
Authors: Austin Cottam; Daniel Cottam; Walter Medlin; Christina Richards; Samuel Cottam; Hinali Zaveri; Amit Surve Journal: Surg Endosc Date: 2015-12-22 Impact factor: 4.584
Authors: Eric S Wise; Kyle M Hocking; Adam Weltz; Anna Uebele; Jose J Diaz; Stephen M Kavic; Mark D Kligman Journal: Surg Endosc Date: 2016-02-22 Impact factor: 4.584
Authors: Austin Cottam; Daniel Cottam; Dana Portenier; Hinali Zaveri; Amit Surve; Samuel Cottam; Legrand Belnap; Walter Medlin; Christina Richards Journal: Obes Surg Date: 2017-02 Impact factor: 4.129
Authors: Eric S Wise; Kyle M Hocking; Monica E Polcz; Gregory J Beilman; Colleen M Brophy; Jenna H Sobey; Philip J Leisy; Roy K Kiberenge; Bret D Alvis Journal: Anesthesiology Date: 2021-04-01 Impact factor: 7.892
Authors: Izabela A Karpińska; Jan Kulawik; Magdalena Pisarska-Adamczyk; Michał Wysocki; Michał Pędziwiatr; Piotr Major Journal: Obes Surg Date: 2021-03-13 Impact factor: 4.129