ArticleCritical care medicine2014
Hospital factors associated with discharge bias in ICU performance measurement.
Article in Critical care medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care.Critical care (London, England) · 2014Pooled it
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- An administrative model for benchmarking hospitals on their 30-day sepsis mortality.BMC health services research · 2019Article
- Impact of Public Reporting of 30-day Mortality on Timing of Death after Coronary Artery Bypass Graft Surgery.Anesthesiology · 2017Article
- External validation of SAPS 3 and MPMAnnals of intensive care · 2017Article
- Understanding intensive care unit benchmarking.Intensive care medicine · 2017Article
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- Intensive Care Unit (ICU) - Managed Elderly Hospitalizations with Dementia in Texas, 2001-2010: A Population-Level Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
objectivePerformance 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.
designRetrospective cohort study.
settingNonfederal Pennsylvania hospital discharges in 2008. PATIENTS: Eligible patients were 18 years old or older and admitted to an ICU.
interventionsNone. MEASUREMENTS AND MAIN
resultsWe 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.
conclusionsDischarge 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.
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