title: The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections.
abstract: Surgical site infections (SSI) are an important cause of peri-surgical morbidity with risks that vary extensively between patients and surgeries. Quantifying SSI risk would help identify candidates most likely to benefit from interventions to decrease the risk of SSI.
  
  
  
  We randomly divided all surgeries recorded in the National Surgical Quality Improvement Program from 2010 into a derivation and validation population. We used multivariate logistic regression to determine the independent association of patient and surgical covariates with the risk of any SSI (including superficial, deep, and organ space SSI) within 30 days of surgery. To capture factors particular to specific surgeries, we developed a surgical risk score specific to all surgeries having a common first 3 numbers of their CPT code.
  
  
  
  Derivation (n = 181 894) and validation (n = 181 146) patients were similar for all demographics, past medical history, and surgical factors. Overall SSI risk was 3.9%. The SSI Risk Score (SSIRS) found that risk increased with patient factors (smoking, increased body mass index), certain comorbidities (peripheral vascular disease, metastatic cancer, chronic steroid use, recent sepsis), and operative characteristics (surgical urgency; increased ASA class; longer operation duration; infected wounds; general anaesthesia; performance of more than one procedure; and CPT score). In the validation population, the SSIRS had good discrimination (c-statistic 0.800, 95% CI 0.795-0.805) and calibration.
  
  
  
  SSIRS can be calculated using patient and surgery information to estimate individual risk of SSI for a broad range of surgery types.
authors.affiliation: Department of Medicine, University of Ottawa, Ottawa, Canada ; Ottawa Hospital Research Institute, Ottawa, Canada ; Institute for Clinical Evaluative Sciences, Toronto, Canada.
authors.forename: Carl
authors.initials: C
authors.lastname: van Walraven
authors.forename: Reilly
authors.initials: R
authors.lastname: Musselman
mesh_terms: D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D058996:Quality Improvement; D011897:Random Allocation; D018570:Risk Assessment; D013530:Surgical Wound Infection / Q000175:diagnosis*; D013997:Time Factors; D014481:United States