Prediction of thirty-day morbidity and mortality after laparoscopic sleeve gastrectomy: data from an artificial
Eric S Wise1, Stuart K Amateau2, Sayeed Ikramuddin3
1Department of Surgery, Division of Gastrointestinal/Bariatric Surgery, University Of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA. wise0147@umn.edu.
Background:
Multiple patient factors may convey increased risk of 30-day morbidity and mortality after laparoscopic vertical sleeve gastrectomy (LVSG). Assessing the likelihood of short-term morbidity is useful for both the bariatric surgeon and patient. Artificial neural networks (ANN) are computational algorithms that use pattern recognition to predict outcomes, providing a potentially more accurate and dynamic model relative to traditional multiple regression. Using a comprehensive national database, this study aims to use an ANN to optimize the prediction of the composite endpoint of 30-day readmission, reoperation, reintervention, or mortality, after LVSG.
Methods:
A cohort of 101,721 LVSG patients was considered for analysis from the 2016 Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program national dataset. Select patient factors were chosen a priori as simple, pertinent and easily obtainable, and their association with the 30-day endpoint was assessed. Those factors with a significant association on both bivariate and multivariate nominal logistic regression analysis were incorporated into a back-propagation ANN with three nodes each assigned a training value of 0.333, with k-fold internal validation. Logistic regression and ANN models were compared using area under receiver-operating characteristic curves (AUROC).
Results:
Upon bivariate analysis, factors associated with 30-day complications were older age (P = 0.03), non-white race, higher initial body mass index, severe hypertension, diabetes mellitus, non-independent functional status, and previous foregut/bariatric surgery (all P < 0.001). These factors remained significant upon nominal logistic regression analysis (n = 100,791, P < 0.001, r2= 0.008, AUROC = 0.572). Upon ANN analysis, the training set (80% of patients) was more accurate than logistic regression (n = 80,633, r2= 0.011, AUROC = 0.581), and it was confirmed by the validation set (n = 20,158, r2= 0.012, AUROC = 0.585).
Conclusions:
This study identifies a panel of simple and easily obtainable preoperative patient factors that may portend increased morbidity after LSG. Using an ANN model, prediction of these events can be optimized relative to standard logistic regression modeling.
