OBJECTIVE: To analyze the risk factors for surgical site infection (SSI) complicating coronary artery bypass graft (CABG) surgery and to create an alternative SSI risk score based on the results of multivariate analysis. METHODS: A prospective cohort study involving inpatient and laboratory-based surveillance of patients who underwent CABG surgery over a 27-month period from January 1, 2003 through March 31, 2005. Data were obtained from 6 acute care hospitals in Victoria, Australia, that contributed surveillance data for SSI complicating CABG surgery to the Victorian Hospital Acquired Infection Surveillance System Coordinating Centre and the Australasian Society of Cardiac and Thoracic Surgeons, also in Victoria. RESULTS: A total of 4,633 (93%) of the 4,987 patients who underwent CABG surgery during this period were matched in the 2 systems databases. There were 286 SSIs and 62 deep or organ space sternal SSIs (deep or organ space sternal SSI rate, 1.33%). Univariate analysis revealed that diabetes mellitus, body mass index (BMI) greater than 35, and receipt of blood transfusion were risk factors for all types of SSI complicating CABG surgery. Six multivariate analysis models were created to examine either preoperative factors alone or preoperative factors combined with operative factors. All models revealed diabetes and BMI of 30 or greater as risk factors for SSI complicating CABG surgery. A new preoperative scoring system was devised to predict sternal SSI, which assigned 1 point for diabetes, 1 point for BMI of 30 or greater but less than 35, and 2 points for BMI of 35 or greater. Each point in the scoring system represented approximately a doubling of risk of SSI. The new scoring system performed better than the National Nosocomial Infections Surveillance System (NNIS) risk index at predicting SSI. CONCLUSION: A new weighted scoring system based on preoperative risk factors was created to predict sternal SSI risk following CABG surgery. The new scoring system outperformed the NNIS risk index. Future studies are needed to validate this scoring system.
OBJECTIVE: To analyze the risk factors for surgical site infection (SSI) complicating coronary artery bypass graft (CABG) surgery and to create an alternative SSI risk score based on the results of multivariate analysis. METHODS: A prospective cohort study involving inpatient and laboratory-based surveillance of patients who underwent CABG surgery over a 27-month period from January 1, 2003 through March 31, 2005. Data were obtained from 6 acute care hospitals in Victoria, Australia, that contributed surveillance data for SSI complicating CABG surgery to the Victorian Hospital Acquired Infection Surveillance System Coordinating Centre and the Australasian Society of Cardiac and Thoracic Surgeons, also in Victoria. RESULTS: A total of 4,633 (93%) of the 4,987 patients who underwent CABG surgery during this period were matched in the 2 systems databases. There were 286 SSIs and 62 deep or organ space sternal SSIs (deep or organ space sternal SSI rate, 1.33%). Univariate analysis revealed that diabetes mellitus, body mass index (BMI) greater than 35, and receipt of blood transfusion were risk factors for all types of SSI complicating CABG surgery. Six multivariate analysis models were created to examine either preoperative factors alone or preoperative factors combined with operative factors. All models revealed diabetes and BMI of 30 or greater as risk factors for SSI complicating CABG surgery. A new preoperative scoring system was devised to predict sternal SSI, which assigned 1 point for diabetes, 1 point for BMI of 30 or greater but less than 35, and 2 points for BMI of 35 or greater. Each point in the scoring system represented approximately a doubling of risk of SSI. The new scoring system performed better than the National Nosocomial Infections Surveillance System (NNIS) risk index at predicting SSI. CONCLUSION: A new weighted scoring system based on preoperative risk factors was created to predict sternal SSI risk following CABG surgery. The new scoring system outperformed the NNIS risk index. Future studies are needed to validate this scoring system.
Authors: Luke F Chen; Jean Marie Arduino; Shubin Sheng; Lawrence H Muhlbaier; Zeina A Kanafani; Anthony D Harris; Thomas G Fraser; Keith Allen; G Ralph Corey; Vance G Fowler Journal: Am J Infect Control Date: 2012-05-19 Impact factor: 2.918
Authors: A Figuerola-Tejerina; E Bustamante; E Tamayo; C A Mestres; J Bustamante-Munguira Journal: Eur J Clin Microbiol Infect Dis Date: 2017-01-19 Impact factor: 3.267