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Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit
OBJECTIVE: To compare the predictive performance of Epic Systems Corporation’s proprietary intensive care unit (ICU) mortality risk model (IMRM) with that of the Acute Physiology and Chronic Health Evaluation (APACHE) IV score. METHODS: This is a retrospective cohort study of patients treated from J...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560567/ https://www.ncbi.nlm.nih.gov/pubmed/33083706 http://dx.doi.org/10.1016/j.mayocpiqo.2020.09.001 |
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author | Tan, Eugene M. Kashyap, Rahul Olson, Ian C. O’Horo, John C. |
author_facet | Tan, Eugene M. Kashyap, Rahul Olson, Ian C. O’Horo, John C. |
author_sort | Tan, Eugene M. |
collection | PubMed |
description | OBJECTIVE: To compare the predictive performance of Epic Systems Corporation’s proprietary intensive care unit (ICU) mortality risk model (IMRM) with that of the Acute Physiology and Chronic Health Evaluation (APACHE) IV score. METHODS: This is a retrospective cohort study of patients treated from January 1, 2008, through January 1, 2018. This single-center study was performed at Mayo Clinic (Rochester, MN), a tertiary care teaching and referral center. The primary outcome was death in the ICU. Discrimination of each risk model for hospital mortality was assessed by comparing area under the receiver operating characteristic curve (AUROC). RESULTS: The cohort mostly comprised older patients (median age, 64 years) and men (56.7%). The mortality rate of the cohort was 3.5% (2251 of 63,775 patients). The AUROC for mortality prediction was 89.7% (95% CI, 89.5% to 89.9%) for the IMRM, which was significantly greater than the AUROC of 88.2% (95% CI, 87.9% to 88.4%) for APACHE IV (P<.001). CONCLUSION: The IMRM was superior to the commonly used APACHE IV score and may be easily integrated into electronic health records at any hospital using Epic software. |
format | Online Article Text |
id | pubmed-7560567 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-75605672020-10-19 Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit Tan, Eugene M. Kashyap, Rahul Olson, Ian C. O’Horo, John C. Mayo Clin Proc Innov Qual Outcomes Original Article OBJECTIVE: To compare the predictive performance of Epic Systems Corporation’s proprietary intensive care unit (ICU) mortality risk model (IMRM) with that of the Acute Physiology and Chronic Health Evaluation (APACHE) IV score. METHODS: This is a retrospective cohort study of patients treated from January 1, 2008, through January 1, 2018. This single-center study was performed at Mayo Clinic (Rochester, MN), a tertiary care teaching and referral center. The primary outcome was death in the ICU. Discrimination of each risk model for hospital mortality was assessed by comparing area under the receiver operating characteristic curve (AUROC). RESULTS: The cohort mostly comprised older patients (median age, 64 years) and men (56.7%). The mortality rate of the cohort was 3.5% (2251 of 63,775 patients). The AUROC for mortality prediction was 89.7% (95% CI, 89.5% to 89.9%) for the IMRM, which was significantly greater than the AUROC of 88.2% (95% CI, 87.9% to 88.4%) for APACHE IV (P<.001). CONCLUSION: The IMRM was superior to the commonly used APACHE IV score and may be easily integrated into electronic health records at any hospital using Epic software. Elsevier 2020-10-06 /pmc/articles/PMC7560567/ /pubmed/33083706 http://dx.doi.org/10.1016/j.mayocpiqo.2020.09.001 Text en © 2020 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Article Tan, Eugene M. Kashyap, Rahul Olson, Ian C. O’Horo, John C. Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit |
title | Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit |
title_full | Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit |
title_fullStr | Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit |
title_full_unstemmed | Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit |
title_short | Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit |
title_sort | validation of a retrospective computing model for mortality risk in the intensive care unit |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560567/ https://www.ncbi.nlm.nih.gov/pubmed/33083706 http://dx.doi.org/10.1016/j.mayocpiqo.2020.09.001 |
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