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Cardiac arrest risk standardization using administrative data compared to registry data

BACKGROUND: Methods for comparing hospitals regarding cardiac arrest (CA) outcomes, vital for improving resuscitation performance, rely on data collected by cardiac arrest registries. However, most CA patients are treated at hospitals that do not participate in such registries. This study aimed to d...

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Autores principales: Grossestreuer, Anne V., Gaieski, David F., Donnino, Michael W., Nelson, Joshua I. M., Mutter, Eric L., Carr, Brendan G., Abella, Benjamin S., Wiebe, Douglas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5544239/
https://www.ncbi.nlm.nih.gov/pubmed/28783754
http://dx.doi.org/10.1371/journal.pone.0182864
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author Grossestreuer, Anne V.
Gaieski, David F.
Donnino, Michael W.
Nelson, Joshua I. M.
Mutter, Eric L.
Carr, Brendan G.
Abella, Benjamin S.
Wiebe, Douglas J.
author_facet Grossestreuer, Anne V.
Gaieski, David F.
Donnino, Michael W.
Nelson, Joshua I. M.
Mutter, Eric L.
Carr, Brendan G.
Abella, Benjamin S.
Wiebe, Douglas J.
author_sort Grossestreuer, Anne V.
collection PubMed
description BACKGROUND: Methods for comparing hospitals regarding cardiac arrest (CA) outcomes, vital for improving resuscitation performance, rely on data collected by cardiac arrest registries. However, most CA patients are treated at hospitals that do not participate in such registries. This study aimed to determine whether CA risk standardization modeling based on administrative data could perform as well as that based on registry data. METHODS AND RESULTS: Two risk standardization logistic regression models were developed using 2453 patients treated from 2000–2015 at three hospitals in an academic health system. Registry and administrative data were accessed for all patients. The outcome was death at hospital discharge. The registry model was considered the “gold standard” with which to compare the administrative model, using metrics including comparing areas under the curve, calibration curves, and Bland-Altman plots. The administrative risk standardization model had a c-statistic of 0.891 (95% CI: 0.876–0.905) compared to a registry c-statistic of 0.907 (95% CI: 0.895–0.919). When limited to only non-modifiable factors, the administrative model had a c-statistic of 0.818 (95% CI: 0.799–0.838) compared to a registry c-statistic of 0.810 (95% CI: 0.788–0.831). All models were well-calibrated. There was no significant difference between c-statistics of the models, providing evidence that valid risk standardization can be performed using administrative data. CONCLUSIONS: Risk standardization using administrative data performs comparably to standardization using registry data. This methodology represents a new tool that can enable opportunities to compare hospital performance in specific hospital systems or across the entire US in terms of survival after CA.
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spelling pubmed-55442392017-08-12 Cardiac arrest risk standardization using administrative data compared to registry data Grossestreuer, Anne V. Gaieski, David F. Donnino, Michael W. Nelson, Joshua I. M. Mutter, Eric L. Carr, Brendan G. Abella, Benjamin S. Wiebe, Douglas J. PLoS One Research Article BACKGROUND: Methods for comparing hospitals regarding cardiac arrest (CA) outcomes, vital for improving resuscitation performance, rely on data collected by cardiac arrest registries. However, most CA patients are treated at hospitals that do not participate in such registries. This study aimed to determine whether CA risk standardization modeling based on administrative data could perform as well as that based on registry data. METHODS AND RESULTS: Two risk standardization logistic regression models were developed using 2453 patients treated from 2000–2015 at three hospitals in an academic health system. Registry and administrative data were accessed for all patients. The outcome was death at hospital discharge. The registry model was considered the “gold standard” with which to compare the administrative model, using metrics including comparing areas under the curve, calibration curves, and Bland-Altman plots. The administrative risk standardization model had a c-statistic of 0.891 (95% CI: 0.876–0.905) compared to a registry c-statistic of 0.907 (95% CI: 0.895–0.919). When limited to only non-modifiable factors, the administrative model had a c-statistic of 0.818 (95% CI: 0.799–0.838) compared to a registry c-statistic of 0.810 (95% CI: 0.788–0.831). All models were well-calibrated. There was no significant difference between c-statistics of the models, providing evidence that valid risk standardization can be performed using administrative data. CONCLUSIONS: Risk standardization using administrative data performs comparably to standardization using registry data. This methodology represents a new tool that can enable opportunities to compare hospital performance in specific hospital systems or across the entire US in terms of survival after CA. Public Library of Science 2017-08-04 /pmc/articles/PMC5544239/ /pubmed/28783754 http://dx.doi.org/10.1371/journal.pone.0182864 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Grossestreuer, Anne V.
Gaieski, David F.
Donnino, Michael W.
Nelson, Joshua I. M.
Mutter, Eric L.
Carr, Brendan G.
Abella, Benjamin S.
Wiebe, Douglas J.
Cardiac arrest risk standardization using administrative data compared to registry data
title Cardiac arrest risk standardization using administrative data compared to registry data
title_full Cardiac arrest risk standardization using administrative data compared to registry data
title_fullStr Cardiac arrest risk standardization using administrative data compared to registry data
title_full_unstemmed Cardiac arrest risk standardization using administrative data compared to registry data
title_short Cardiac arrest risk standardization using administrative data compared to registry data
title_sort cardiac arrest risk standardization using administrative data compared to registry data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5544239/
https://www.ncbi.nlm.nih.gov/pubmed/28783754
http://dx.doi.org/10.1371/journal.pone.0182864
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