BACKGROUND: Surgical site infection is one of the most common and significant morbidities following colon and rectal surgery, representing a marker of institutional quality. Various measures have been implemented to lower its incidence. However, the level of incidence remains unacceptable in many reports. OBJECTIVE: This study addresses whether surgical site infections can be accurately predicted in an outpatient clinical setting among patients undergoing elective colon and rectal surgery. DESIGN: This investigation was designed as a retrospective cohort study with the use of logistic regression modeling. SETTINGS: Data for this study were extracted from the American College of Surgeons National Surgical Quality Improvement Program Participant user data file. PATIENTS: Patients undergoing elective intraabdominal colorectal surgery during 2009 were included. MAIN OUTCOME MEASURES: The primary outcome measured was the probability of 30-day surgical site infection (superficial and deep incisional). RESULTS: A total of 18,403 records for patients with colorectal surgery were identified. Superficial incisional surgical site infections were identified in 1447 records (7.86%). Deep incisional surgical site infections were identified in 278 records (1.51%). Body mass index, preoperative hematocrit, open approach, ASA classification level, smoking, alcohol use, functional status before surgery, and age more than 75 years were identified as likely independent predictors of deep and superficial surgical site infections. Multivariable logistic regression analysis was used to develop a series of predictive models. Reduced versions of the models were then developed that included only highly statistically significant predictors of infection in the corresponding full models (age, alcohol abuse, ASA classification, stoma closure, open approach, BMI, and hematocrit). Nomograms representing the final reduced model equations are presented. LIMITATIONS: This study was limited by the use of an administrative database and its retrospective design. CONCLUSIONS: Surgical site infection is common morbidity following colon and rectal surgery. Nomograms using key patient characteristics can be used to accurately calculate a patients' risk of surgical site infection. This tool could be applied in the clinical setting to prospectively identify patients at highest risk of surgical site infection.
BACKGROUND: Surgical site infection is one of the most common and significant morbidities following colon and rectal surgery, representing a marker of institutional quality. Various measures have been implemented to lower its incidence. However, the level of incidence remains unacceptable in many reports. OBJECTIVE: This study addresses whether surgical site infections can be accurately predicted in an outpatient clinical setting among patients undergoing elective colon and rectal surgery. DESIGN: This investigation was designed as a retrospective cohort study with the use of logistic regression modeling. SETTINGS: Data for this study were extracted from the American College of Surgeons National Surgical Quality Improvement Program Participant user data file. PATIENTS: Patients undergoing elective intraabdominal colorectal surgery during 2009 were included. MAIN OUTCOME MEASURES: The primary outcome measured was the probability of 30-day surgical site infection (superficial and deep incisional). RESULTS: A total of 18,403 records for patients with colorectal surgery were identified. Superficial incisional surgical site infections were identified in 1447 records (7.86%). Deep incisional surgical site infections were identified in 278 records (1.51%). Body mass index, preoperative hematocrit, open approach, ASA classification level, smoking, alcohol use, functional status before surgery, and age more than 75 years were identified as likely independent predictors of deep and superficial surgical site infections. Multivariable logistic regression analysis was used to develop a series of predictive models. Reduced versions of the models were then developed that included only highly statistically significant predictors of infection in the corresponding full models (age, alcohol abuse, ASA classification, stoma closure, open approach, BMI, and hematocrit). Nomograms representing the final reduced model equations are presented. LIMITATIONS: This study was limited by the use of an administrative database and its retrospective design. CONCLUSIONS: Surgical site infection is common morbidity following colon and rectal surgery. Nomograms using key patient characteristics can be used to accurately calculate a patients' risk of surgical site infection. This tool could be applied in the clinical setting to prospectively identify patients at highest risk of surgical site infection.
Authors: Puja M Shah; Heather L Evans; Amy Harrigan; Robert G Sawyer; Charles M Friel; Traci L Hedrick Journal: Surg Infect (Larchmt) Date: 2017-05-09 Impact factor: 2.150