Objective: Surgical site infection (SSI) following coronary artery bypass grafting (CABG) is a serious complication associated with significant morbidity and mortality. Despite the substantial impact of SSI there is lack of a specific risk stratification tool to predict this complication after CABG. This study was undertaken to develop a specific prognostic scoring system for the development of SSI that could risk-stratify patients undergoing CABG.
Methods: Between January 2009 and June 2012, continuous prospective surveillance data on SSI and a set of 41 variables were collected. Using binary logistic regression analysis we identified independent predictors of SSI. Initially we developed a predictive model in a subset of 769 patients. Dataset was expanded to 4087 cases and a final model and risk score were derived. Calibration of the scores was performed using the Hosmer-Lemeshow test.
Results: The model had area under Receiver Operating Characteristic curve of 0.727 (0.827 for preliminary dataset). Baseline risk score incorporated independent predictors of SSI: female gender = 2 (p < 0.0001; RR 2.1), diabetes = 1 (p = 0.0098, RR 1.4) or HbA1c >7.5% = 3 (p < 0.0001; RR 3.4), body mass index ≥35 = 2 (p < 0.0001; RR 2.4), left ventricular ejection fraction < 45% = 1 (p = 0.0255; RR 1.4), and emergency surgery = 2 (p = 0.012; RR 2.4). A risk stratification system, the Brompton & Harefield Infection Score (BHIS) was developed.
Conclusion: BHIS effectively predicts SSI risk and may help with risk stratification in relation to public reporting and reimbursement as well as targeted prevention strategies in patients undergoing CABG.
Keywords: Cardiac surgery; Coronary artery bypass grafting; Predictive model; Prevention; Surgical site infection; Surveillance.
Copyright © 2015 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.