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Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team()
AIM: The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk m...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier/north-Holland Biomedical Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4111919/ https://www.ncbi.nlm.nih.gov/pubmed/24830872 http://dx.doi.org/10.1016/j.resuscitation.2014.05.004 |
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author | Harrison, David A. Patel, Krishna Nixon, Edel Soar, Jasmeet Smith, Gary B. Gwinnutt, Carl Nolan, Jerry P. Rowan, Kathryn M. |
author_facet | Harrison, David A. Patel, Krishna Nixon, Edel Soar, Jasmeet Smith, Gary B. Gwinnutt, Carl Nolan, Jerry P. Rowan, Kathryn M. |
author_sort | Harrison, David A. |
collection | PubMed |
description | AIM: The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. METHODS: Risk models for two outcomes—return of spontaneous circulation (ROSC) for greater than 20 min and survival to hospital discharge—were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. RESULTS: 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC > 20 min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC > 20 min (c index 0.81 versus 0.72). CONCLUSIONS: Validated risk models for ROSC > 20 min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. |
format | Online Article Text |
id | pubmed-4111919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Elsevier/north-Holland Biomedical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-41119192014-08-01 Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() Harrison, David A. Patel, Krishna Nixon, Edel Soar, Jasmeet Smith, Gary B. Gwinnutt, Carl Nolan, Jerry P. Rowan, Kathryn M. Resuscitation Clinical Paper AIM: The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. METHODS: Risk models for two outcomes—return of spontaneous circulation (ROSC) for greater than 20 min and survival to hospital discharge—were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. RESULTS: 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC > 20 min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC > 20 min (c index 0.81 versus 0.72). CONCLUSIONS: Validated risk models for ROSC > 20 min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. Elsevier/north-Holland Biomedical Press 2014-08 /pmc/articles/PMC4111919/ /pubmed/24830872 http://dx.doi.org/10.1016/j.resuscitation.2014.05.004 Text en © 2014 The Authors http://creativecommons.org/licenses/by/3.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Clinical Paper Harrison, David A. Patel, Krishna Nixon, Edel Soar, Jasmeet Smith, Gary B. Gwinnutt, Carl Nolan, Jerry P. Rowan, Kathryn M. Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() |
title | Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() |
title_full | Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() |
title_fullStr | Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() |
title_full_unstemmed | Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() |
title_short | Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() |
title_sort | development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team() |
topic | Clinical Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4111919/ https://www.ncbi.nlm.nih.gov/pubmed/24830872 http://dx.doi.org/10.1016/j.resuscitation.2014.05.004 |
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