Multivariate Logistic Regression Analysis Of Postoperative Complications And Risk Model Establishment Of Gastrectomy For Gastric Cancer:a Single Center Cohort Report

Objective:To evaluate the risk factors of postoperative complications and establish logistic regression model in a real life cohort of patients with gastric cancer following gastrectomy. Background:Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity.Methods:We retrospectively analyzed data on2795patients with gastric cancer who underwent surgery at the Affiliated Hospital of Qingdao University between2007and2012, established multivariate logistic regression model to predictive risk factors related to complications of postgatrectomy for gastric cancer.Results:24out of86variables were identified statistically significant in univariate logistic regression analysis,11significant variables entered multivariate analysis were employed to produce the risk model. Hepatic cirrhosis, diabetes mellitus, Child-Pugh score (B+C), invasion of neighboring organs, combined resection, introperative transfusion, Billroth Ⅱ anastomosis of re-establishment, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. The model was highly accurate86.7%(2423/2795), sensitive76.2%(327/429) and specificity88.6%(2096/2366) in predicting postoperative complications after gastric operation.Conclusions:The risk model targeting specific-procedure can predict postoperative complications specific to an individual patient’s risk factors. To have truly informed consent and shared decision making with a patient, we accredit the model has the ability to provide customized, personal risk estimates for patients undergoing gastric operation.