INTRODUCTION: Despite being the most common surgery in the United States, little is known about predicting weight loss success and failure with sleeve gastrectomy (SG). Papers that have been published are inconclusive. We decided to use multivariate analysis from 2 practices to design a model to predict weight loss outcomes using data widely available to any surgical practice at 3 months to determine weight loss outcomes at 1 year. SETTING: Two private practices in the United States. METHODS: A retrospective review of 613 patients from 2 bariatric institutions were included in this study. Co-morbidities and other preoperative characteristics were gathered, and %EWL was calculated for 1, 3, and 12 months. Excess weight loss (%EWL)<55% at 1 year was defined as weight loss failure. Multiple variate analysis was used to find factors that affect %EWL at 12 months. RESULTS: Preoperative sleep apnea, preoperative diabetes, %EWL at 1 month, and %EWL at 3 months all affect %EWL at 1 year. The positive predictive value and negative predictive value of our model was 72% and 91%, respectively. Sensitivity and specificity were 71% and 91%, respectively. CONCLUSION: One-year results of the SG can be predicted by diabetes, sleep apnea, and weight loss velocity at 3 months postoperatively. This can help surgeons direct surgical or medical interventions for patients at 3 months rather than at 1 year or beyond.
INTRODUCTION: Despite being the most common surgery in the United States, little is known about predicting weight loss success and failure with sleeve gastrectomy (SG). Papers that have been published are inconclusive. We decided to use multivariate analysis from 2 practices to design a model to predict weight loss outcomes using data widely available to any surgical practice at 3 months to determine weight loss outcomes at 1 year. SETTING: Two private practices in the United States. METHODS: A retrospective review of 613 patients from 2 bariatric institutions were included in this study. Co-morbidities and other preoperative characteristics were gathered, and %EWL was calculated for 1, 3, and 12 months. Excess weight loss (%EWL)<55% at 1 year was defined as weight loss failure. Multiple variate analysis was used to find factors that affect %EWL at 12 months. RESULTS:Preoperative sleep apnea, preoperative diabetes, %EWL at 1 month, and %EWL at 3 months all affect %EWL at 1 year. The positive predictive value and negative predictive value of our model was 72% and 91%, respectively. Sensitivity and specificity were 71% and 91%, respectively. CONCLUSION: One-year results of the SG can be predicted by diabetes, sleep apnea, and weight loss velocity at 3 months postoperatively. This can help surgeons direct surgical or medical interventions for patients at 3 months rather than at 1 year or beyond.
Authors: Austin Cottam; Daniel Cottam; Hinali Zaveri; Samuel Cottam; Amit Surve; Walter Medlin; Christina Richards Journal: Obes Surg Date: 2018-09 Impact factor: 4.129
Authors: Onno M Tettero; Valerie M Monpellier; Ignace M C Janssen; Ingrid H M Steenhuis; Maartje M van Stralen Journal: Obes Surg Date: 2022-07-16 Impact factor: 3.479