Relationship Between Discharge Practices and Intensive Care Unit In-Hospital Mortality Performance
Evidence of a Discharge Bias
Datos Bibliográficos
| ID | 9102361 |
|---|---|
| Autores | Eduard E Vasilevskis (0000-0001-8165-5321, Vanderbilt University, autor de correspondencia), Michael W Kuzniewicz (0000-0002-3271-2999, University of California, San Francisco, autor de correspondencia), Mitzi L Dean (Ashland (United States), autor de correspondencia), Ted Clay (University of California, San Francisco, autor de correspondencia), Eric Vittinghoff (0000-0001-8535-0920), Deborah J Rennie (University of California, San Francisco, autor de correspondencia), R Adams Dudley (0000-0002-8532-8552, Ashland (United States), autor de correspondencia) |
| Año | 2009 |
| Volumen | 47 |
| Número | 7 |
| Páginas | 803-812 |
| Fecha de publicación | 2009-07-01 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Medical Care (JOURNAL) |
| Identificadores de la revista | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Editorial | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0b013e3181a39454 |
| PMID | 19536006 |
| OpenAlex | W2078968344 |
| Idioma | EN |
| Citas recibidas | 2 |
| Referencias citadas | 40 |
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
| Obras citantes distintas | 2 |
|---|---|
| Citas por año | 0,18 |
| Intervalo de citas | 2015 - 2016 (2) |
| Velocidad de citación | historical |
| Altamente citado | No |
| Tipos de cita | Neutras: 2 |