title: Relationship between discharge practices and intensive care unit in-hospital mortality performance: evidence of a discharge bias.
abstract: BACKGROUND
  Current intensive care unit performance measures include in-hospital mortality after intensive care unit admission. This measure does not account for deaths occurring after transfer to another hospital or soon after discharge and therefore, may be biased.
  
  
  OBJECTIVE
  Determine how transfer rates to other acute care hospitals and early post-discharge mortality rates impact hospital performance assessments using an in-hospital mortality model.
  
  
  METHODS
  Data were retrospectively collected on 10,502 eligible intensive care unit patients across 35 California hospitals between 2001 and 2004.
  
  
  METHODS
  We calculated the rates of acute care hospital transfers and early post-discharge mortality (30-day overall mortality-30-day in-hospital mortality) for each hospital. We assessed hospital performance with standardized mortality ratios (SMRs) using the Mortality Probability Model III. Using regression models, we explored the relationship between in-hospital SMRs and the rates of hospital transfers or early post-discharge mortality. We explored the same relationship using a 30-day SMR.
  
  
  RESULTS
  In multivariable models, for each 1% increase in patients transferred to another acute care hospital, there was an in-hospital SMR reduction of -0.021 (-0.040-0.001). Additionally, a 1% increase in early post-discharge mortality was associated with an in-hospital SMR reduction of -0.049 (-0.142-0.045). Assessing hospital performance based upon 30-day mortality end point resulted in SMRs closer to 1.0 for hospitals at high and low ends of in-hospital mortality performance.
  
  
  CONCLUSIONS
  Variations in transfer rates and potentially discharge timing appear to bias in-hospital SMR calculations. A 30-day mortality model is a potential alternative that may limit this bias.
authors.affiliation: Philip R. Lee Institute for Health Policy Studies and Division of General Internal Medicine, University of California-San Francisco, CA, USA. eduard.vasilevskis@vanderbilt.edu
authors.forename: Eduard E
authors.initials: EE
authors.lastname: Vasilevskis
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kuzniewicz
authors.forename: Mitzi L
authors.initials: ML
authors.lastname: Dean
authors.forename: Ted
authors.initials: T
authors.lastname: Clay
authors.forename: Eric
authors.initials: E
authors.lastname: Vittinghoff
authors.forename: Deborah J
authors.initials: DJ
authors.lastname: Rennie
authors.forename: R Adams
authors.initials: RA
authors.lastname: Dudley
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D002140:California; D003422:Critical Care / Q000706:statistics & numerical data*; D005260:Female; D019538:Health Care Surveys; D006276:Health Facility Size; D054625:Healthcare Disparities / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000592:standards; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010360:Patient Transfer / Q000706:statistics & numerical data; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D012044:Regression Analysis; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods / Q000592:standards; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric; D013997:Time Factors; D055815:Young Adult