Actionable Risk Model for the Development of Surgical Site Infection after Emergency Surgery

Abstract

Background:

Surgical site infections (SSIs) increase mortality and the economic burden associated with emergency surgery (ES). A reliable and sensitive scoring system to predict SSIs can help guide clinician assessment and patient counseling of post-operative SSI risk. We hypothesized that after quantifying the ES post-operative SSI incidence, readily abstractable parameters can be used to develop an actionable risk stratification scheme.

Patients and Methods:

We reviewed retrospectively all patients who underwent ES operations at an urban academic hospital system (2005–2013). Comorbidities and operative characteristics were abstracted from the electronic health record (EHR) with a primary outcome of post-operative SSIs. Risk of SSI was calculated using logistic regression modeling and validated using bootstrapping techniques. Beta-coefficients were calculated to correlate risk. A simplified clinical risk assessment tool was derived by assigning point values to the rounded β-coefficients.

Results:

A total of 4,783 patients with a 13.2% incidence of post-operative SSIs were identified. The strongest risk factors associated with SSIs included acute intestinal ischemia, weight loss, intestinal perforation, trauma-related laparotomy, radiation exposure, previous gastrointestinal surgery, and peritonitis. The assessment tool defined three patient groups based on SSI risk. Post-operative SSI incidence in high-risk patients (34%; score = 6–10) exceeded that of medium- (11.1%; score = 3–5) and low-risk patients (1.5%; score = 1–2) (C statistic = 0.802). Patients with a risk score ≥10 points evidenced the highest post-operative SSI risk (71.9%).

Conclusion:

Pre-operative identification of ES patient risk for post-operative SSI may inform pre-operative patient counseling and operative planning if the proposed procedure includes medical device implantation. A clinically relevant seven-factor risk stratification model such as this empirically derived one may be suitable to incorporate into the EHR as a decision-support tool.