Association for Academic Surgery
An Evaluation of Surgical Site Infections by Wound Classification System Using the ACS-NSQIP

https://doi.org/10.1016/j.jss.2011.05.056Get rights and content

Background

Surgical wound classification has been the foundation for infectious risk assessment, perioperative protocol development, and surgical decision-making. The wound classification system categorizes all surgeries into: clean, clean/contaminated, contaminated, and dirty, with estimated postoperative rates of surgical site infection (SSI) being 1%–5%, 3%–11%, 10%–17%, and over 27%, respectively. The present study evaluates the associated rates of the SSI by wound classification using a large risk adjusted surgical patient database.

Methods

A cross-sectional study was performed using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) dataset between 2005 and 2008. All surgical cases that specified a wound class were included in our analysis. Patient demographics, hospital length of stay, preoperative risk factors, co-morbidities, and complication rates were compared across the different wound class categories. Surgical site infection rates for superficial, deep incisional, and organ/space infections were analyzed among the four wound classifications using multivariate logistic regression.

Results

A total of 634,426 cases were analyzed. From this sample, 49.7% were classified as clean, 35.0% clean/contaminated, 8.56% contaminated, and 6.7% dirty. When stratifying by wound classification, the clean, clean/contaminated, contaminated, and dirty wound classifications had superficial SSI rates of 1.76%, 3.94%, 4.75%, and 5.16%, respectively. The rates of deep incisional infections were 0.54%, 0.86%, 1.31%, and 2.1%. The rates for organ/space infection were 0.28%, 1.87%, 2.55%, and 4.54%.

Conclusion

Using ACS-NSQIP data, the present study demonstrates substantially lower rates of surgical site infections in the contaminated and dirty wound classifications than previously reported in the literature.

Introduction

Surgical wound classification, introduced by the National Academy of Sciences in 1964, has been the foundation for infectious risk assessment, perioperative protocol development, and surgical decision-making. The classification system classifies all surgeries into four categories: clean, clean/contaminated, contaminated, and dirty based on the bacterial load of the surgical wound 1, 2. In 1970, the National Nosocomial Infectious Surveillance Survey was conducted to identify factors that played a role in postoperative wound infections [3], which led to the 1985 Center for Disease Control’s (CDC) guidelines for preventions of postoperative wound infection [4]. The guidelines provided updated estimated postoperative rates of surgical site infections to 1%–5% for clean, 3%–11% for clean/contaminated, 10%–17% for contaminated, and over 27% for dirty 5, 6.

The wound classification system is an important predictor of postoperative outcomes. Recent studies have focused on elements such as preoperative risk factors and co-morbidities, operative time, prophylactic antibiotic use, and the American Society of Anesthesiology (ASA) physical status score, along with wound classification to predict postoperative surgical outcomes [5]. Currently, national efforts such as the Medicare guidelines on antibiotic prophylaxis that specify when the antibiotic infusion should begin and end have contributed to decreased rates of SSIs 7, 8. Appropriate use of antibiotic prophylaxis in surgical patients was one of 11 practices rated by the Agency for Healthcare Research and Quality (AHRQ) to be strongly supported by evidence-based studies [9]. Initiatives by the AHRQ and programs like the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) have elevated quality of care to the forefront of national healthcare concerns and have spurred the need to evaluate current systems to consider the changes in healthcare. The ACS-NSQIP database provides a tool to assess surgical outcomes drawing from the records of hundreds of hospitals. The present study evaluates the rates of postoperative surgical site infection (SSI) by wound classification using the ACS-NSQIP, a large risk-adjusted surgical patient database.

Section snippets

Methods

A retrospective analysis was performed using the ACS-NSQIP dataset between 2005 and 2008. All surgical cases that specified a wound class were included in our analysis. Patients who died on the same day of operation were excluded. Patients were stratified by wound classification (clean, clean/contaminated, contaminated, and dirty) as defined by ACS-NSQIP (Table 1). Patient demographics, hospital length of stay, preoperative risk factors, co-morbidities, and complication rates were compared

Results

A total of 634,426 cases were analyzed. From this sample, 49.7% of wounds were classified as clean, 35.0% as clean/contaminated, 8.6% as contaminated, and 6.7% as dirty. Most patients were female (57.4%) and over the age of 60 (40.8%) y. The mean age for all patients was 54.7 y. Regarding ethnicity, White patients were the most common (71.7%), followed by Black (9.8%), and Hispanic (6.0%). The most common co-morbidity examined was obesity (defined as a body mass index ≥ 35, 27.8%), followed by

Discussion

The present study is the first to present an analysis of postoperative SSIs based upon wound classification categories using a risk-adjusted national database. A trend toward higher rates of postoperative SSIs was observed when progressing from clean to dirty wound procedures. However, the rates of infection observed for each wound classification, specifically in the contaminated and dirty wound class, were notably lower from those previously reported in the literature over the past sixty years

Acknowledgments

The authors thank the Robert Garrett Fund for Treatment of Children, which helped support this study.

References (16)

There are more references available in the full text version of this article.

Cited by (175)

View all citing articles on Scopus
View full text