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Hospital Factors Associated With Discharge Bias in ICU Performance Measurement*

Reineck, Lora A. MD1,2; Pike, Francis PhD2; Le, Tri Q. MPH2,3; Cicero, Brandon D. MPH2; Iwashyna, Theodore J. MD, PhD4,5; Kahn, Jeremy M. MD, MS2,3

doi: 10.1097/CCM.0000000000000132
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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.

Design: Retrospective cohort study.

Setting: Nonfederal Pennsylvania hospital discharges in 2008.

Patients: Eligible patients were 18 years old or older and admitted to an ICU.

Interventions: None.

Measurements and Main 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.

Supplemental Digital Content is available in the text.

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.

* See also p. 1285.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (

Supported, in part, by grant R01HL096651 from National Institutes of Health.

Dr. Reineck received support for article research from National Institutes of Health (NIH, 5T32HL7563). Her institution received grant support from NIH (NIH 5T32HL7563—institutional training grant). Dr. Kahn is employed by the U.S. Veterans Affairs Health Service and has lectured for the National Association of Long-term Care Hospitals (travel reimbursement and speaking honorarium), American Lung Association of Washington (travel reimbursement and speaking honorarium), Barlow Respiratory Hospital (travel reimbursement and speaking honorarium), Canadian Critical Care Society (travel reimbursement), and the American Thoracic Society (travel reimbursement and speaking honorarium). His institution received grant support from the National Institutes of Health and the U.S. Health Resources and Services Administration. The remaining authors have disclosed that they do not have any potential conflicts of interest.

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© 2014 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins