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Relationship Between Discharge Practices and Intensive Care Unit In-Hospital Mortality Performance

Evidence of a Discharge Bias

Dados Bibliográficos

ID9102361
AutoresEduard E Vasilevskis (0000-0001-8165-5321, Vanderbilt University, autor correspondente), Michael W Kuzniewicz (0000-0002-3271-2999, University of California, San Francisco, autor correspondente), Mitzi L Dean (Ashland (United States), autor correspondente), Ted Clay (University of California, San Francisco, autor correspondente), Eric Vittinghoff (0000-0001-8535-0920), Deborah J Rennie (University of California, San Francisco, autor correspondente), R Adams Dudley (0000-0002-8532-8552, Ashland (United States), autor correspondente)
Ano2009
Volume47
Fascículo7
Páginas803-812
Data de publicação2009-07-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e3181a39454
PMID19536006
OpenAlexW2078968344
IdiomaEN
Citações recebidas2
Referências citadas40

CONTEXT: 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. DESIGN, SETTING, AND PARTICIPANTS: Data were retrospectively collected on 10,502 eligible intensive care unit patients across 35 California hospitals between 2001 and 2004. MEASURES: 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

Acute care · Context (archaeology) · Health care · Hospital discharge · Intensive care · Intensive care medicine · Intensive care unit · Mortality rate · Standardized mortality ratio · Emergency Medicine · Heart Failure Treatment and Management · Internal Medicine · Medicine · Sepsis Diagnosis and Treatment · Trauma and Emergency Care Studies

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Obras citantes distintas2
Citações por ano0,18
Intervalo de citações2015 - 2016 (2)
Velocidade de citaçãohistorical
Altamente citadoNão
Tipos de citaçãoNeutras: 2
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