title: Finding a method for optimizing risk adjustment when comparing surgical-site infection rates.
abstract: OBJECTIVE
  To investigate whether stratification of the risk of developing a surgical-site infection (SSI) is improved when a logistic regression model is used to weight the risk factors for each procedure category individually instead of the modified NNIS System risk index.
  
  
  METHODS
  The German Nosocomial Infection Surveillance System, based on NNIS System methodology, has 273 acute care surgical departments participating voluntarily. Data on 9 procedure categories were included (214,271 operations).
  
  
  METHODS
  For each of the procedure categories, the significant risk factors from the available data (NNIS System risk index variables of ASA score, wound class, duration of operation, and endoscope use, as well as gender and age) were identified by multiple logistic regression analyses with stepwise variable selection. The area under the receiver operating characteristic (ROC) curve resulting from these analyses was used to evaluate the predictive power of logistic regression models.
  
  
  RESULTS
  For most procedures, at least two of the three variables contributing to the NNIS System risk index were shown to be independent risk factors (appendectomy, knee arthroscopy, cholecystectomy, colon surgery, herniorrhaphy, hip prosthesis, knee prosthesis, and vascular surgery). The predictive power of logistic regression models (including age and gender, when appropriate) was low (between 0.55 and 0.71) and for most procedures only slightly better than that of the NNIS System risk index.
  
  
  CONCLUSIONS
  Without the inclusion of additional procedure-specific variables, logistic regression models do not improve the comparison of SSI rates from various hospitals.
authors.affiliation: Institut für Hygiene und Umweltmedizin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Brandt
authors.forename: Sonja
authors.initials: S
authors.lastname: Hansen
authors.forename: Dorit
authors.initials: D
authors.lastname: Sohr
authors.forename: Franz
authors.initials: F
authors.lastname: Daschner
authors.forename: Henning
authors.initials: H
authors.lastname: Rüden
authors.forename: Petra
authors.initials: P
authors.lastname: Gastmeier
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D005858:Germany; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance / Q000379:methods*; D011237:Predictive Value of Tests; D012372:ROC Curve; D020379:Risk Adjustment / Q000379:methods*; D013530:Surgical Wound Infection / Q000209:etiology / Q000517:prevention & control*