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Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score
BACKGROUND: The past decade has seen tremendous growth in patients with ambulatory ventricular assist devices. We sought to identify patients that present to the emergency department (ED) at the highest risk of death. METHODS AND RESULTS: This retrospective analysis of ED encounters from the Nationw...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238533/ https://www.ncbi.nlm.nih.gov/pubmed/35023355 http://dx.doi.org/10.1161/JAHA.121.020942 |
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author | Edelson, Jonathan B. Edwards, Jonathan J. Katcoff, Hannah Mondal, Antara Chen, Feiyan Reza, Nosheen Hanff, Thomas C. Griffis, Heather Mazurek, Jeremy A. Wald, Joyce Burstein, Danielle S. Atluri, Pavan O’Connor, Matthew J. Goldberg, Lee R. Zamani, Payman Groeneveld, Peter W. Rossano, Joseph W. Lin, Kimberly Y. Birati, Edo Y. |
author_facet | Edelson, Jonathan B. Edwards, Jonathan J. Katcoff, Hannah Mondal, Antara Chen, Feiyan Reza, Nosheen Hanff, Thomas C. Griffis, Heather Mazurek, Jeremy A. Wald, Joyce Burstein, Danielle S. Atluri, Pavan O’Connor, Matthew J. Goldberg, Lee R. Zamani, Payman Groeneveld, Peter W. Rossano, Joseph W. Lin, Kimberly Y. Birati, Edo Y. |
author_sort | Edelson, Jonathan B. |
collection | PubMed |
description | BACKGROUND: The past decade has seen tremendous growth in patients with ambulatory ventricular assist devices. We sought to identify patients that present to the emergency department (ED) at the highest risk of death. METHODS AND RESULTS: This retrospective analysis of ED encounters from the Nationwide Emergency Department Sample includes 2010 to 2017. Using a random sampling of patient encounters, 80% were assigned to development and 20% to validation cohorts. A risk model was derived from independent predictors of mortality. Each patient encounter was assigned to 1 of 3 groups based on risk score. A total of 44 042 ED ventricular assist device patient encounters were included. The majority of patients were male (73.6%), <65 years old (60.1%), and 29% presented with bleeding, stroke, or device complication. Independent predictors of mortality during the ED visit or subsequent admission included age ≥65 years (odds ratio [OR], 1.8; 95% CI, 1.3–4.6), primary diagnoses (stroke [OR, 19.4; 95% CI, 13.1–28.8], device complication [OR, 10.1; 95% CI, 6.5–16.7], cardiac [OR, 4.0; 95% CI, 2.7–6.1], infection [OR, 5.8; 95% CI, 3.5–8.9]), and blood transfusion (OR, 2.6; 95% CI, 1.8–4.0), whereas history of hypertension was protective (OR, 0.69; 95% CI, 0.5–0.9). The risk score predicted mortality areas under the curve of 0.78 and 0.71 for development and validation. Encounters in the highest risk score strata had a 16‐fold higher mortality compared with the lowest risk group (15.8% versus 1.0%). CONCLUSIONS: We present a novel risk score and its validation for predicting mortality of patients with ED ventricular assist devices, a high‐risk, and growing, population. |
format | Online Article Text |
id | pubmed-9238533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92385332022-06-30 Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score Edelson, Jonathan B. Edwards, Jonathan J. Katcoff, Hannah Mondal, Antara Chen, Feiyan Reza, Nosheen Hanff, Thomas C. Griffis, Heather Mazurek, Jeremy A. Wald, Joyce Burstein, Danielle S. Atluri, Pavan O’Connor, Matthew J. Goldberg, Lee R. Zamani, Payman Groeneveld, Peter W. Rossano, Joseph W. Lin, Kimberly Y. Birati, Edo Y. J Am Heart Assoc Original Research BACKGROUND: The past decade has seen tremendous growth in patients with ambulatory ventricular assist devices. We sought to identify patients that present to the emergency department (ED) at the highest risk of death. METHODS AND RESULTS: This retrospective analysis of ED encounters from the Nationwide Emergency Department Sample includes 2010 to 2017. Using a random sampling of patient encounters, 80% were assigned to development and 20% to validation cohorts. A risk model was derived from independent predictors of mortality. Each patient encounter was assigned to 1 of 3 groups based on risk score. A total of 44 042 ED ventricular assist device patient encounters were included. The majority of patients were male (73.6%), <65 years old (60.1%), and 29% presented with bleeding, stroke, or device complication. Independent predictors of mortality during the ED visit or subsequent admission included age ≥65 years (odds ratio [OR], 1.8; 95% CI, 1.3–4.6), primary diagnoses (stroke [OR, 19.4; 95% CI, 13.1–28.8], device complication [OR, 10.1; 95% CI, 6.5–16.7], cardiac [OR, 4.0; 95% CI, 2.7–6.1], infection [OR, 5.8; 95% CI, 3.5–8.9]), and blood transfusion (OR, 2.6; 95% CI, 1.8–4.0), whereas history of hypertension was protective (OR, 0.69; 95% CI, 0.5–0.9). The risk score predicted mortality areas under the curve of 0.78 and 0.71 for development and validation. Encounters in the highest risk score strata had a 16‐fold higher mortality compared with the lowest risk group (15.8% versus 1.0%). CONCLUSIONS: We present a novel risk score and its validation for predicting mortality of patients with ED ventricular assist devices, a high‐risk, and growing, population. John Wiley and Sons Inc. 2022-01-13 /pmc/articles/PMC9238533/ /pubmed/35023355 http://dx.doi.org/10.1161/JAHA.121.020942 Text en © 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Original Research Edelson, Jonathan B. Edwards, Jonathan J. Katcoff, Hannah Mondal, Antara Chen, Feiyan Reza, Nosheen Hanff, Thomas C. Griffis, Heather Mazurek, Jeremy A. Wald, Joyce Burstein, Danielle S. Atluri, Pavan O’Connor, Matthew J. Goldberg, Lee R. Zamani, Payman Groeneveld, Peter W. Rossano, Joseph W. Lin, Kimberly Y. Birati, Edo Y. Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score |
title | Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score |
title_full | Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score |
title_fullStr | Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score |
title_full_unstemmed | Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score |
title_short | Novel Risk Model to Predict Emergency Department Associated Mortality for Patients Supported With a Ventricular Assist Device: The Emergency Department–Ventricular Assist Device Risk Score |
title_sort | novel risk model to predict emergency department associated mortality for patients supported with a ventricular assist device: the emergency department–ventricular assist device risk score |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238533/ https://www.ncbi.nlm.nih.gov/pubmed/35023355 http://dx.doi.org/10.1161/JAHA.121.020942 |
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