title: A revised method to assess intensive care unit clinical performance and resource utilization.
abstract: OBJECTIVE
  In 1994, Rapoport et al. published a two-dimensional graphical tool for benchmarking intensive care units (ICUs) using a Mortality Probability Model (MPM0-II) to assess clinical performance and a Weighted Hospital Days scale (WHD-94) to assess resource utilization. MPM0-II and WHD-94 do not calibrate on contemporary data, giving users of the graph an inflated assessment of their ICU's performance. MPM0-II was recently updated (MPM0-III) but not the model for predicting resource utilization. The objective was to develop a new WHD model and revised Rapoport-Teres graph.
  
  
  METHODS
  Multicenter cohort study.
  
  
  METHODS
  One hundred thirty-five ICUs in 98 hospitals participating in Project IMPACT.
  
  
  METHODS
  Patients were 124,855 MPM0-II eligible Project IMPACT patients treated between March 2001 and June 2004.
  
  
  METHODS
  None.
  
  
  RESULTS
  WHD was redefined as 4 units for the first day of each ICU stay, 2.5 units for each additional ICU day, and 1 unit for each non-ICU day after the first ICU discharge. Stepwise linear regression was used to construct a model to predict ICU-specific log average WHD from 39 candidate variables available in Project IMPACT. The updated WHD model has four independent variables: percent of patients dying in the hospital, percent of unscheduled surgical patients, percent of patients on mechanical ventilation within 1 hr of ICU admission, and percent discharged from the ICU to an external post-acute care facility. The first three variables increase average WHD and the last decreases it. The new model has good performance (R = 0.47) and, when combined with MPM0-II, provides a well-calibrated Rapoport-Teres graph.
  
  
  CONCLUSIONS
  A new WHD model has been derived from a large, contemporary critical care database and, when used with MPM0-III, updates a popular method for benchmarking ICUs. Project IMPACT participants will likely perceive a decline in their ICU performance coordinates due to the recalibrated graph and should instead focus on their unit's performance relative to their peers.
authors.affiliation: OptiStatim LLC, Longmeadow, MA, USA.
authors.forename: Brian H
authors.initials: BH
authors.lastname: Nathanson
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Higgins
authors.forename: Daniel
authors.initials: D
authors.lastname: Teres
authors.forename: Wayne S
authors.initials: WS
authors.lastname: Copes
authors.forename: Andrew
authors.initials: A
authors.lastname: Kramer
authors.forename: Maureen
authors.initials: M
authors.lastname: Stark
mesh_terms: D019985:Benchmarking / Q000379:methods* / Q000706:statistics & numerical data; D015331:Cohort Studies; D006295:Health Resources / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000191:economics / Q000592:standards*; D007902:Length of Stay; D016014:Linear Models; D008875:Middle Aged; D015233:Models, Statistical; D011336:Probability; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D015996:Survival Rate; D014481:United States