title: Hospital factors associated with discharge bias in ICU performance measurement.
abstract: OBJECTIVE
  Performance assessments based on in-hospital mortality for ICU patients can be affected by discharge practices such that differences in mortality may reflect variation in discharge patterns rather than quality of care. Time-specific mortality rates, such as 30-day mortality, are preferred but are harder to measure. The degree to which the difference between 30-day and in-hospital ICU mortality rates-or "discharge bias"-varies by hospital type is unknown. The aim of this study was to quantify variation in discharge bias across hospitals and determine the hospital characteristics associated with greater discharge bias.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  Nonfederal Pennsylvania hospital discharges in 2008.
  
  
  METHODS
  Eligible patients were 18 years old or older and admitted to an ICU.
  
  
  METHODS
  None.
  
  
  RESULTS
  We used logistic regression with hospital-level random effects to calculate hospital-specific risk-adjusted 30-day and in-hospital mortality rates. We then calculated discharge bias, defined as the difference between 30-day and in-hospital mortality rates, and used multivariable linear regression to compare discharge bias across hospital types. A total of 43,830 patients and 134 hospitals were included in the analysis. Mean (SD) risk-adjusted hospital-specific in-hospital and 30-day ICU mortality rates were 9.6% (1.3) and 12.7% (1.5), respectively. Hospital-specific discharge biases ranged from -1.3% to 6.6%. Discharge bias was smaller in large hospitals compared with small hospitals, making large hospitals appear comparatively worse from a benchmarking standpoint when using in-hospital mortality instead of 30-day mortality.
  
  
  CONCLUSIONS
  Discharge practices bias in-hospital ICU mortality measures in a way that disadvantages large hospitals. Accounting for discharge bias will prevent these hospitals from being unfairly disadvantaged in public reporting and pay-for-performance.
authors.affiliation: 1Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. 2CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. 3Department of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, PA. 4Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI. 5Center for Clinical Management Research, Ann Arbor VA, Ann Arbor, MI.
authors.forename: Lora A
authors.initials: LA
authors.lastname: Reineck
authors.forename: Francis
authors.initials: F
authors.lastname: Pike
authors.forename: Tri Q
authors.initials: TQ
authors.lastname: Le
authors.forename: Brandon D
authors.initials: BD
authors.lastname: Cicero
authors.forename: Theodore J
authors.initials: TJ
authors.lastname: Iwashyna
authors.forename: Jeremy M
authors.initials: JM
authors.lastname: Kahn
mesh_terms: D015982:Bias; D015331:Cohort Studies; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D016015:Logistic Models; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010414:Pennsylvania; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D012052:Reimbursement, Incentive; D012121:Respiration, Artificial / Q000401:mortality; D012189:Retrospective Studies; D012306:Risk; D012307:Risk Factors; D018805:Sepsis / Q000401:mortality