Bariatric Times

DEC 2017

A peer-reviewed, evidence-based journal that promotes clinical development and metabolic insights in total bariatric patient care for the healthcare professional

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17 Original Research Bariatric Times • December 2017 capacity to predict, despite our efforts to maintain database integrity, might arise from continuously revising and p airing data input and output variables. Another limitation involved size and scope of the database. Data was collected at one clinic in Utah, and despite long-term collection, more data i n each surgery type stands to improve neural networks trained for application over diverse populations and surgeries. Additionally, more clinical measures, such as the presence of diabetes and e ven pre-diabetes measured through hemoglobin A1c, might improve predictions. Unfortunately, most models to date rely on single or a couple types of surgeries, 34,36,37 and it is r are for a study to consider several options at once. It is also rare to track many different clinical measurements as done in the research based funded LABS study. 34 As data are shared and combined over time, 38 models can rely on larger and broader databases for model development. Finally, moving away from simpler regression models also can impede application to clinical practice. As models improve, user-friendly apps should be designed to deliver model predictions to surgeons and patients quickly, effectively, and efficiently. CONCLUSION Neural networks and other sophisticated machine-learning algorithms can amplify long-term predictive accuracy from pre-operative and short-term observations beyond standard methods like regression. These models can provide personalized information to guide patient-surgeon decisions. REFERENCES 1. Adams TD, Davidson LE, Litwin SE, et al. Health benefits of gastric bypass surgery after 6 years. JAMA. 2012;308(11):1122–1131. 2. Courcoulas AP, Christian NJ, Belle SH, et al. Weight change and health outcomes at 3 years after bariatric surgery among individuals with severe obesity. JAMA. 2013;310(22):2416–2425. 3. Sjoholm K, Pajunen P, Jacobson P, et al. Incidence and remission of type 2 diabetes in relation to degree of obesity at baseline and 2 year weight change: the Swedish Obese Subjects (SOS) study. Diabetologia. 2015;58(7):1448–1453. 4. Sjostrom L, Peltonen M, Jacobson P, et al. Association of bariatric surgery with long-term remission of type 2 diabetes and with microvascular and macrovascular complications. JAMA. 2014;311(22):2297–2304. 5. Hatoum IJ, Blackstone R, Hunter TD, et al. Clinical factors associated with remission of obesity-related comorbidities after bariatric surgery. JAMA Surg. 2015:1–8. 6. Dicker D, Yahalom R, Comaneshter DS, et al. Long-term outcomes of three types of bariatric surgery on obesity and type 2 diabetes control and remission. Obes Surg. 2016;26(8):1814–1820. 7. Burza MA, Romeo S, Kotronen A, et al. Long-term effect of bariatric surgery on liver enzymes in the Swedish Obese Subjects (SOS) study. PLoS One. 2013;8(3):e60495. Epub 2013 Mar 26. 8. Sjostrom L. Review of the key results from the Swedish Obese Subjects (SOS) trial—a prospective controlled intervention study of bariatric surgery. J Intern Med. 2013;273(3):219–234. 9. Sjostrom L, Peltonen M, Jacobson P, et al. Bariatric surgery and long-term cardiovascular events. JAMA. 2012;307(1):56–65. 10. Sjostrom L. Bariatric surgery and reduction in morbidity and mortality: experiences from the SOS study. Int J Obes (Lond). 2008;32 Suppl 7:S93–S97. 11. Belle S, Consortium L. The NIDDK Bariatric Surgery clinical Research Consortium (LABS). Surg Obes Relat Dis. 2005;1(2):145–147. 12. Moon RC, Teixeira AF, Jawad MA. Treatment of weight regain following Roux-en-Y gastric bypass: revision of pouch, creation of new gastrojejunostomy and placement of proximal pericardial patch ring. Obes Surg. 2014;24(6):829–834. 13. 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Revised sleeve gastrectomy: another option for weight loss failure after sleeve gastrectomy. Surg Endosc. 2014;28(4):1096–1102. 24. Sarela AI, Dexter SP, McMahon MJ. Use of the obesity surgery mortality risk score to predict complications of laparoscopic bariatric surgery. Obes Surg. 2011;21(11):1698–1703. 25. Sarkhosh K, Switzer NJ, El-Hadi M, et al. The impact of bariatric surgery on obstructive sleep apnea: a systematic review. Obes Surg. 2013;23(3):414–423. 26. Thomas H, Agrawal S. Systematic review of obesity surgery mortality risk score-preoperative risk stratification in bariatric surgery. Obes Surg. 2012;22(7):1135–1140. 27. McVay MA, Friedman KE, Applegate KL, Portenier DD. Patient predictors of follow-up care attendance in Roux- en-Y gastric bypass patients. Surg Obes Relat Dis. 2013;9(6):956–962. 28. Livhits M, Mercado C, Yermilov I, Parikh JA, et al. Preoperative predictors of weight loss following bariatric surgery: systematic review. Obes Surg. 2012;22(1):70–89. 29. Kinzl JF, Schrattenecker M, Traweger C, et al. Psychosocial predictors of weight loss after bariatric surgery. Obes Surg. 2006;16(12):1609–1614. 30. Ortega E, Morinigo R, Flores L, et al. Predictive factors of excess body weight loss 1 year after laparoscopic bariatric surgery. Surg Endosc. 2012;26(6):1744–1750. 31. Cottam A, Billing J, Cottam D, et al. Long-term success and failure with SG is predictable by 3 months: a multivariate model using simple office markers. Surg Obes Relat Dis. 2017;13(8):1266–1270. 32. Hatoum IJ, Greenawalt DM, Cotsapas C, et al. Weight loss after gastric bypass is associated with a variant at 15q26.1. Am J Hum Genet. 2013;92(5):827–834. 33. Pedersen HK, Gudmundsdottir V, Pedersen MK, et al. Ranking factors involved in diabetes remission after bariatric surgery using machine- learning integrating clinical and genomic biomarkers. Genomic Medicine. 2016;1:16035. 34. Courcoulas AP, Christian NJ, O'Rourke RW, et al. Preoperative factors and 3-year weight change in the Longitudinal Assessment of Bariatric Surgery (LABS) consortium. Surg Obes Relat Dis. 2015;11(5):1109–1118. 35. Compher CW, Hanlon A, Kang Y, et al. Attendance at clinical visits predicts weight loss after gastric bypass surgery. Obes Surg. 2012;22(6):927–934. 36. Abu Dayyeh BK, Lautz DB, Thompson CC. Gastrojejunal stoma diameter predicts weight regain after Roux-en-Y gastric bypass. Clin Gastroenterol Hepatol. 2011;9(3):228–233. 37. Yanos BR, Saules KK, Schuh LM, Sogg S. Predictors of lowest weight and long-term weight regain among Roux-en-Y gastric bypass patients. Obes Surg. 2015;25(8):1364–1370. 38. Benoit SC, Hunter TD, Francis DM, De La Cruz-Munoz N. Use of bariatric outcomes longitudinal database (BOLD) to study variability in patient success after bariatric surgery. Obes Surg. 2014;24(6):936–943. FUNDING: No funding was provided. DISCLOSURES: The authors report no conflicts of interest relevant to the content of this article. AUTHOR AFFILIATION: Diana M. Thomas, PhD, and Patrick Kuiper, MS, are with the Department of Mathematical Sciences, United States Military Academy in West Point, New York. Hinali Zaveri, MD; Amit Surve, MD; and Daniel R. Cottam, MD, are with the Bariatric Medicine Institute in Salt Lake City, Utah. ADDRESS FOR CORRESPONDENCE: Diana M. Thomas, MD, Department of Mathematical Sciences, United States Military Academy, West Point, NY 10996; Phone: 973-224-2562; E-mail: Neural networks and other sophisticated machine-learning algorithms can amplify long-term predictive accuracy from pre-operative and short-term observations beyond standard methods like regression.

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