title: Performance of risk-adjusted control charts to monitor in-hospital mortality of intensive care unit patients: a simulation study.
abstract: OBJECTIVE
  Increases in case-mix adjusted mortality may be indications of decreasing quality of care. Risk-adjusted control charts can be used for in-hospital mortality monitoring in intensive care units by issuing a warning signal when there are more deaths than expected. The aim of this study was to systematically assess and compare, by computer simulation, expected delay before a warning signal was given for an upward shift in mortality rate in intensive care mortality data by different risk-adjusted control charts.
  
  
  METHODS
  We compared the efficiency of the risk-adjusted P-chart, risk-adjusted Additive P-chart, risk-adjusted Multiplicative P-chart, monthly Standardized Mortality Ratio, risk-adjusted Cumulative Sum, risk-adjusted Resetting Sequential Probability Ratio Test, and risk-adjusted Exponentially Weighted Moving Average control chart to detect an upward shift in mortality rate in eight different scenarios that varied by mortality increase factor and monthly patient volume.
  
  
  METHODS
  Adult intensive care units in The Netherlands.
  
  
  METHODS
  Patients admitted to 73 intensive care units from the Dutch National Intensive Care Evaluation quality registry from the year 2009.
  
  
  METHODS
  None.
  
  
  METHODS
  We compared the performance of the different risk-adjusted control charts by the median time-to-signal and the 6-month detection rate.
  
  
  RESULTS
  In all eight scenarios, the risk-adjusted Exponentially Weighted Moving Average control chart had the shortest median time-to-signal, and in four, the highest 6-month detection rate. The median time-to-signal for an average volume intensive care unit (i.e., 50 admissions per month) with an increase in mortality rate of R = 1.50 on the odds scale was 9 months for the risk-adjusted Exponentially Weighted Moving Average control chart.
  
  
  CONCLUSIONS
  The risk-adjusted Exponentially Weighted Moving Average control chart signaled the fastest in most of the simulated scenarios and is therefore superior in detecting increases in in-hospital mortality of intensive care patients compared to the other types of risk-adjusted control charts.
authors.affiliation: Department of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands. a.koetsier@amc.uva.nl
authors.forename: Antonie
authors.initials: A
authors.lastname: Koetsier
authors.forename: Nicolette F
authors.initials: NF
authors.lastname: de Keizer
authors.forename: Evert
authors.initials: E
authors.lastname: de Jonge
authors.forename: David A
authors.initials: DA
authors.lastname: Cook
authors.forename: Niels
authors.initials: N
authors.lastname: Peek
mesh_terms: D018806:APACHE; D000328:Adult; D019985:Benchmarking / Q000379:methods*; D003198:Computer Simulation; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D008499:Medical Records / Q000706:statistics & numerical data*; D009010:Monte Carlo Method; D009426:Netherlands / Q000453:epidemiology; D012042:Registries; D020379:Risk Adjustment