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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Fernandez-Moure, JS; Wes, A; Kaplan, LJ; Fischer, JP

Published Date

  • May 12, 2020

Published In

PubMed ID

  • 32397903

Pubmed Central ID

  • 32397903

Electronic International Standard Serial Number (EISSN)

  • 1557-8674

International Standard Serial Number (ISSN)

  • 1096-2964

Digital Object Identifier (DOI)

  • 10.1089/sur.2019.282

Language

  • eng