title: Development and validation of a novel stratification tool for identifying cancer patients at increased risk of surgical site infection.
abstract: OBJECTIVE
  To identify cancer-specific predictors of postoperative surgical site infection (SSI), and to develop a risk-stratification prognostic tool and compare its performance with traditional measures.
  
  
  BACKGROUND
  The incidence and risk factors for SSI in cancer patients are unknown; current risk-stratification tools are not cancer-specific.
  
  
  METHODS
  A prospective cohort study of patients undergoing elective operations (n = 503) at a tertiary cancer center was conducted. SSI was assessed using postdischarge active surveillance. Multivariate logistic regression analyses were performed to identify predictors of SSI, and β-coefficients were used to create a scoring system. The sum of these was used to create a Risk of Surgical Site Infection in Cancer (RSSIC) score. The RSSIC was validated using bootstrapping techniques, and its discrimination was compared with the National Nosocomial Infection Surveillance (NNIS) risk index.
  
  
  RESULTS
  The 30-day SSI incidence was 24%. Significant predictors of SSI included preoperative chemotherapy (OR = 1.94 [95% CI, 1.16-3.25]), clean-contaminated wounds (OR = 2.1 [95% CI, 1.24-3.55]), operative time ≥2 hours (OR = 1.75 [95% CI, 1.01-3.04]) and ≥4 hours (OR = 2.24 [95% CI, 1.22-4.1]), and surgical site: groin (OR = 4.65 [95% CI, 1.69-12.83]), and head/neck (OR = 0.12 [95% CI, 0.02-0.89]). The RSSIC score stratified patients into 4 risk strata for SSI. The performance of this score exceeded that of the NNIS score (AUC = 0.70 vs. 0.63, respectively; P = 0.01).
  
  
  CONCLUSIONS
  SSIs are common following cancer surgery. Preoperative chemotherapy, in addition to other common risk factors, was identified as a significant predictor for SSI in cancer patients. The RSSIC improves risk-stratification of cancer patients and identifies those that may benefit from more aggressive or novel preventive strategies.
authors.affiliation: Michael E. DeBakey Department of Surgery-Surgical Oncology, Houston, TX, USA. danaya@bcm.edu
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Anaya
authors.forename: Janice N
authors.initials: JN
authors.lastname: Cormier
authors.forename: Yan
authors.initials: Y
authors.lastname: Xing
authors.forename: Paul
authors.initials: P
authors.lastname: Koller
authors.forename: Lindsay
authors.initials: L
authors.lastname: Gaido
authors.forename: Donna
authors.initials: D
authors.lastname: Hadfield
authors.forename: Roy F
authors.initials: RF
authors.lastname: Chemaly
authors.forename: Barry W
authors.initials: BW
authors.lastname: Feig
mesh_terms: D000368:Aged; D015984:Causality; D015331:Cohort Studies; D003428:Cross Infection / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology*; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000601:surgery*; D016017:Odds Ratio; D011159:Population Surveillance; D011379:Prognosis; D011446:Prospective Studies; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data*; D013530:Surgical Wound Infection / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology*