title: Individual and Clustered Rankability of ICUs According to Case-Mix-Adjusted Mortality.
abstract: OBJECTIVE
  The performance of ICUs can be compared by ranking them into a league table according to their risk-adjusted mortality rate. The statistical quality of a league table can be expressed as its rankability, the percentage of variation between ICUs attributable to unexplained differences. We examine whether we can improve the rankability of our league table by using data from a longer period or by grouping ICUs with similar performance constructing a league table of clusters rather than individual ICUs.
  
  
  METHODS
  We developed a league table for risk-adjusted mortality rate with its rankability. The effect of assessment period was determined using a resampling procedure. Hierarchical clustering was used to obtain clusters of similar ICUs.
  
  
  METHODS
  We used data from ICUs participating in the Dutch National Intensive Care Evaluation registry between 2011 and 2013.
  
  
  RESULTS
  We constructed league tables using 157,394 admissions from 78 ICUs with risk-adjusted mortality rate between 5.9% and 13.9% per ICU over the inclusion period. The rankability was 73% for 2013 and 89% for the whole period 2011-2013. Rankability over the year 2013 increased till 98% when clustering ICUs, reaching an optimum at a league table of seven clusters.
  
  
  CONCLUSIONS
  We conclude that, when using data from a single year, the rankability of a league table of Dutch ICUs based on risk-adjusted mortality rate was unacceptably low. We could improve the rankability of this league table by increasing the period of data collection or by grouping similar ICUs into clusters and constructing a league table of clusters of ICUs rather than individual ICUs. Ranking clusters of ICUs could be useful for identifying possible differences in performance between clusters of ICUs.
authors.affiliation: 1Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands. 2Health eResearch Centre, Institute of Population Health, University of Manchester, Manchester, United Kingdom. 3Department of Intensive Care, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands. 4Department of Intensive Care, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Ilona W M
authors.initials: IW
authors.lastname: Verburg
authors.forename: Nicolette F
authors.initials: NF
authors.lastname: de Keizer
authors.forename: Rebecca
authors.initials: R
authors.lastname: Holman
authors.forename: Dave
authors.initials: D
authors.lastname: Dongelmans
authors.forename: Evert
authors.initials: E
authors.lastname: de Jonge
authors.forename: Niels
authors.initials: N
authors.lastname: Peek
mesh_terms: D019985:Benchmarking / Q000379:methods*; D003953:Diagnosis-Related Groups; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D009426:Netherlands; D019984:Quality Indicators, Health Care; D020379:Risk Adjustment / Q000379:methods*