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

Ari Wes; Joseph Fernandez-Moure; Lewis Kaplan; John Fischer

Surgical site infections (SSI) increase mortality and the economic burden associated with emergency surgery (ES).  A reliable and sensitive scoring system to predict SSI can help guide clinician assessment and patient counseling of post-operative SSI risk. 

We hypothesized that after quantifying the ES post-op SSI incidence, readily abstractable variables can be used to develop an actionable risk stratification scheme. 

We retrospectively reviewed 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-op SSI. SSI risk was calculated using logistic regression modeling and validated using bootstrapping techniques. Beta (β) coefficients were calculated to correlate risk. A simplified clinical risk assessment tool, the emergency surgery infection risk score (ESIRS) was derived by assigning point values to the rounded β-coefficients.

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

Preoperative identification of ES patient risk for post-op SSI may inform pre-operative patient counseling and operative planning if the proposed procedure includes medical device implantation.  A clinically relevant 7-factor risk stratification model such as this empirically derived ESIRS may be suitable to incorporate into the EHR as a decision-support tool.