title: The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.
abstract: OBJECTIVE
  To develop and internally validate a surgical site infection (SSI) prediction model for Japan.
  
  
  METHODS
  Retrospective observational cohort study.
  
  
  METHODS
  We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables.
  
  
  RESULTS
  The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected.
  
  
  CONCLUSIONS
  Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.
authors.affiliation: 1Kyushu University Graduate School of Medical Sciences,Fukuoka,Japan.
authors.forename: Haruhisa
authors.initials: H
authors.lastname: Fukuda
authors.affiliation: 2Institute of Statistical Mathematics,Tachikawa,Tokyo,Japan.
authors.forename: Manabu
authors.initials: M
authors.lastname: Kuroki
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003428:Cross Infection / Q000453:epidemiology*; D005260:Female; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D017053:Infection Control / Q000592:standards*; D007564:Japan / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012189:Retrospective Studies; D020379:Risk Adjustment; D012307:Risk Factors; D013530:Surgical Wound Infection / Q000453:epidemiology*