title: Risk-adjusted rates for potentially avoidable reoperations were computed from routine hospital data.
abstract: OBJECTIVE
  Reoperations may reflect a suboptimal initial surgical treatment. The study aimed to develop a screening algorithm for those potentially avoidable, using only routinely collected hospital data and a prediction model to adjust rates for case-mix.
  
  
  METHODS
  Data of a 3-year random sample of 7,370 therapeutic operations on inpatients, among which 833 were followed-up by a reoperation during the same stay. A review of medical records identified clearly avoidable and other potentially avoidable reoperations to develop and test the screening algorithm. A logistic prediction model of potentially avoidable reoperations was developed on one randomly chosen half of the data (about 9,000 interventions) and tested on the other half (cross-validation).
  
  
  RESULTS
  Two hundred thirty-seven interventions (3%) were followed by a potentially avoidable reoperation, among which 144 were clearly avoidable. The screening algorithm had a sensitivity of 75% and a specificity of 72%. Predictors of potentially avoidable reoperations were surgery categories, diagnosis related conditions, and experiencing prior surgery. The risk score, based on these variables, showed at once a satisfactory discriminative performance (C-statistic=0.76) and goodness-of-fit measure on the validation set.
  
  
  CONCLUSIONS
  The adjusted rate of potentially avoidable reoperations should be included in internal reporting of hospital quality indicators, but further validated in various settings.
authors.affiliation: Institut Universitaire de Médecine Sociale et Préventive, University of Lausanne, 17 Rue du Bugnon, 1005 Lausanne, Switzerland. patricia.halfon@chuv.ch
authors.forename: Patricia
authors.initials: P
authors.lastname: Halfon
authors.forename: Yves
authors.initials: Y
authors.lastname: Eggli
authors.forename: Maurice
authors.initials: M
authors.lastname: Matter
authors.forename: Christine
authors.initials: C
authors.lastname: Kallay
authors.forename: Guy
authors.initials: G
authors.lastname: van Melle
authors.forename: Bernard
authors.initials: B
authors.lastname: Burnand
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006302:Health Services Research / Q000379:methods; D006761:Hospitals / Q000592:standards*; D006801:Humans; D008297:Male; D019300:Medical Errors / Q000706:statistics & numerical data*; D008499:Medical Records; D008875:Middle Aged; D011183:Postoperative Complications; D011379:Prognosis; D019984:Quality Indicators, Health Care; D012086:Reoperation / Q000706:statistics & numerical data*; D012307:Risk Factors; D013514:Surgical Procedures, Operative / Q000592:standards; D013557:Switzerland