title: Utilization of the NSQIP-Pediatric Database in Development and Validation of a New Predictive Model of Pediatric Postoperative Wound Complications.
abstract: BACKGROUND
  Surgical wound classification, introduced in 1964, stratifies the risk of surgical site infection (SSI) based on a clinical estimate of the inoculum of bacteria encountered during the procedure. Recent literature has questioned the accuracy of predicting SSI risk based on wound classification. We hypothesized that a more specific model founded on specific patient and perioperative factors would more accurately predict the risk of SSI.
  
  
  METHODS
  Using all observations from the 2012 to 2014 pediatric National Surgical Quality Improvement Program-Pediatric (NSQIP-P) Participant Use File, patients were randomized into model creation and model validation datasets. Potential perioperative predictive factors were assessed with univariate analysis for each of 4 outcomes: wound dehiscence, superficial wound infection, deep wound infection, and organ space infection. A multiple logistic regression model with a step-wise backwards elimination was performed. A receiver operating characteristic curve with c-statistic was generated to assess the model discrimination for each outcome.
  
  
  RESULTS
  A total of 183,233 patients were included. All perioperative NSQIP factors were evaluated for clinical pertinence. Of the original 43 perioperative predictive factors selected, 6 to 9 predictors for each outcome were significantly associated with postoperative SSI. The predictive accuracy level of our model compared favorably with the traditional wound classification in each outcome of interest.
  
  
  CONCLUSIONS
  The proposed model from NSQIP-P demonstrated a significantly improved predictive ability for postoperative SSIs than the current wound classification system. This model will allow providers to more effectively counsel families and patients of these risks, and more accurately reflect true risks for individual surgical patients to hospitals and payers.
authors.affiliation: Division of Pediatric Surgery, Children's Hospital of Alabama, University of Alabama at Birmingham, Birmingham, AL.
authors.forename: Ilan I
authors.initials: II
authors.lastname: Maizlin
authors.affiliation: Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL.
authors.forename: David T
authors.initials: DT
authors.lastname: Redden
authors.affiliation: Division of Pediatric Surgery, Children's Hospital of Alabama, University of Alabama at Birmingham, Birmingham, AL.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Beierle
authors.affiliation: Division of Pediatric Surgery, Children's Hospital of Alabama, University of Alabama at Birmingham, Birmingham, AL.
authors.forename: Mike K
authors.initials: MK
authors.lastname: Chen
authors.affiliation: Division of Pediatric Surgery, Children's Hospital of Alabama, University of Alabama at Birmingham, Birmingham, AL. Electronic address: robert.russell@childrensal.org.
authors.forename: Robert T
authors.initials: RT
authors.lastname: Russell
mesh_terms: D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016015:Logistic Models; D008297:Male; D015999:Multivariate Analysis; D010372:Pediatrics; D058996:Quality Improvement; D012372:ROC Curve; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013530:Surgical Wound Infection / Q000175:diagnosis* / Q000209:etiology