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SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care

BACKGROUND: Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool fo...

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Autores principales: Altieri Dunn, Stefanie C., Bellon, Johanna E., Bilderback, Andrew, Borrebach, Jeffrey D., Hodges, Jacob C., Wisniewski, Mary Kay, Harinstein, Matthew E., Minnier, Tamra E., Nelson, Joel B., Hall, Daniel E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870086/
https://www.ncbi.nlm.nih.gov/pubmed/33556123
http://dx.doi.org/10.1371/journal.pone.0246669
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author Altieri Dunn, Stefanie C.
Bellon, Johanna E.
Bilderback, Andrew
Borrebach, Jeffrey D.
Hodges, Jacob C.
Wisniewski, Mary Kay
Harinstein, Matthew E.
Minnier, Tamra E.
Nelson, Joel B.
Hall, Daniel E.
author_facet Altieri Dunn, Stefanie C.
Bellon, Johanna E.
Bilderback, Andrew
Borrebach, Jeffrey D.
Hodges, Jacob C.
Wisniewski, Mary Kay
Harinstein, Matthew E.
Minnier, Tamra E.
Nelson, Joel B.
Hall, Daniel E.
author_sort Altieri Dunn, Stefanie C.
collection PubMed
description BACKGROUND: Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. METHODS AND FINDINGS: All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively). CONCLUSIONS: The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.
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spelling pubmed-78700862021-02-11 SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care Altieri Dunn, Stefanie C. Bellon, Johanna E. Bilderback, Andrew Borrebach, Jeffrey D. Hodges, Jacob C. Wisniewski, Mary Kay Harinstein, Matthew E. Minnier, Tamra E. Nelson, Joel B. Hall, Daniel E. PLoS One Research Article BACKGROUND: Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. METHODS AND FINDINGS: All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively). CONCLUSIONS: The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes. Public Library of Science 2021-02-08 /pmc/articles/PMC7870086/ /pubmed/33556123 http://dx.doi.org/10.1371/journal.pone.0246669 Text en © 2021 Altieri Dunn et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Altieri Dunn, Stefanie C.
Bellon, Johanna E.
Bilderback, Andrew
Borrebach, Jeffrey D.
Hodges, Jacob C.
Wisniewski, Mary Kay
Harinstein, Matthew E.
Minnier, Tamra E.
Nelson, Joel B.
Hall, Daniel E.
SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care
title SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care
title_full SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care
title_fullStr SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care
title_full_unstemmed SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care
title_short SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care
title_sort safenet: initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870086/
https://www.ncbi.nlm.nih.gov/pubmed/33556123
http://dx.doi.org/10.1371/journal.pone.0246669
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