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. DESIGN: Multicenter cohort study. SETTING: One hundred thirty-five ICUs in 98 hospitals participating in Project IMPACT. PATIENTS: Patients were 124,855 MPM0-II eligible Project IMPACT patients treated between March 2001 and June 2004. INTERVENTIONS: None. MEASUREMENTS AND MAIN 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.
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. DESIGN: Multicenter cohort study. SETTING: One hundred thirty-five ICUs in 98 hospitals participating in Project IMPACT. PATIENTS: Patients were 124,855 MPM0-II eligible Project IMPACT patients treated between March 2001 and June 2004. INTERVENTIONS: None. MEASUREMENTS AND MAIN 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: Anthony D Slonim; Sachin Khandelwal; Jianping He; Matthew Hall; David C Stockwell; Wendy M Turenne; Samir S Shah Journal: Pediatrics Date: 2010-05-10 Impact factor: 7.124
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Authors: Kay H Stricker; Oliver Kimberger; Kurt Schmidlin; Marcel Zwahlen; Ulrike Mohr; Hans U Rothen Journal: Intensive Care Med Date: 2009-12 Impact factor: 17.440