A Clinical Decision Support System for Predicting the Early Complications of One-Anastomosis Gastric Bypass Surgery

• Published in: Obesity surgery Vol. 29; no. 7; pp. 2276 - 2286
• Main Authors: Sheikhtaheri, Abbas, Orooji, Azam, Pazouki, Abdolreza, Beitollahi, Maryam
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2019; Springer Nature B.V
• Subjects: Comorbidity; Decision support systems; Decision Support Systems, Clinical; Gastric Bypass - statistics & numerical data; Gastrointestinal surgery; Humans; Intraoperative Complications - epidemiology; Iran; Medicine; Medicine & Public Health; Obesity; Obesity, Morbid - epidemiology; Obesity, Morbid - physiopathology; Obesity, Morbid - surgery; Original Contributions; Postoperative Complications - epidemiology; Risk Factors; Surgery; Surgical outcomes; Iran; Machine learning; Clinical decision support system; Early complications; Obesity surgery; Artificial neural network; Data mining; One-anastomosis gastric bypass
• ISSN: 0960-8923; 1708-0428; 1708-0428
• DOI: 10.1007/s11695-019-03849-w

Background/Objective One of the most effective treatments for patients with obesity, albeit with some complications, is obesity surgery. The aim of this study was to develop a clinical decision support system (CDSS) to predict the early complications of one-anastomosis gastric bypass (OAGB) surgery. Subjects/Methods This study was conducted in Tehran, Iran on patients who underwent OAGB surgery in 2011–2014 in five hospitals. Initially, variables affecting the OAGB early complications were identified using the literature review. Patients’ data were extracted from an existing database of obesity surgery. Then, different artificial neural networks (ANNs) (multilayer perceptron (MLP) network) were developed and evaluated for prediction of 10-day, 1-month, and 3-month complications. Results Factors including age, BMI, smoking status, intra-operative complications, comorbidities, laboratory tests, sonography results, and endoscopy results were considered important factors for predicting early complications of OAGB. A CDSS was developed with these variables. The accuracy, specificity, and sensitivity of the 10-day prediction system in the test data were 98.4%, 98.6%, and 98.3%, respectively. These figures for 1-month system were 96%, 93%, and 98.4% and for the 3-month system were 89.3%, 86.6%, and 91.5%, respectively. Conclusions Using the CDSS designed, we could accurately predict the early complications of OAGB surgery.