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Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram

BACKGROUND: We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission. METHODS: For emergency medical admissions (96,305 episodes in 50,612 patie...

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Autores principales: Cournane, Seán, Conway, Richard, Byrne, Declan, O'Riordan, Deirdre, Silke, Bernard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5705890/
https://www.ncbi.nlm.nih.gov/pubmed/29270210
http://dx.doi.org/10.1155/2017/5267864
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author Cournane, Seán
Conway, Richard
Byrne, Declan
O'Riordan, Deirdre
Silke, Bernard
author_facet Cournane, Seán
Conway, Richard
Byrne, Declan
O'Riordan, Deirdre
Silke, Bernard
author_sort Cournane, Seán
collection PubMed
description BACKGROUND: We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission. METHODS: For emergency medical admissions (96,305 episodes in 50,612 patients) between 2002 and 2016, the relationship between 30-day in-hospital mortality and admission laboratory data was determined using logistic regression. The previously validated Acute Illness Severity model was then transposed to a Kattan-style nomogram with a Stata user-written program. RESULTS: The Acute Illness Severity was based on the admission Manchester triage category and biochemical laboratory score; these latter were based on the serum albumin, sodium, potassium, urea, red cell distribution width, and troponin status. The laboratory admission data was predictive with an AUROC of 0.85 (95% CI: 0.85, 0.86). The sensitivity was 94.4%, with a specificity of 62.7%. The positive predictive value was 21.2%, with a negative predictive value of 99.1%. For the Kattan-style nomogram, the regression coefficients are converted to a 100-point scale with the predictor parameters mapped to a probability axis. The nomogram would be an easy-to-use tool at the bedside and for educational purposes, illustrating the relative importance of the contribution of each predictor to the overall score. CONCLUSION: A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described.
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spelling pubmed-57058902017-12-21 Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram Cournane, Seán Conway, Richard Byrne, Declan O'Riordan, Deirdre Silke, Bernard Comput Math Methods Med Research Article BACKGROUND: We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission. METHODS: For emergency medical admissions (96,305 episodes in 50,612 patients) between 2002 and 2016, the relationship between 30-day in-hospital mortality and admission laboratory data was determined using logistic regression. The previously validated Acute Illness Severity model was then transposed to a Kattan-style nomogram with a Stata user-written program. RESULTS: The Acute Illness Severity was based on the admission Manchester triage category and biochemical laboratory score; these latter were based on the serum albumin, sodium, potassium, urea, red cell distribution width, and troponin status. The laboratory admission data was predictive with an AUROC of 0.85 (95% CI: 0.85, 0.86). The sensitivity was 94.4%, with a specificity of 62.7%. The positive predictive value was 21.2%, with a negative predictive value of 99.1%. For the Kattan-style nomogram, the regression coefficients are converted to a 100-point scale with the predictor parameters mapped to a probability axis. The nomogram would be an easy-to-use tool at the bedside and for educational purposes, illustrating the relative importance of the contribution of each predictor to the overall score. CONCLUSION: A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described. Hindawi 2017 2017-11-14 /pmc/articles/PMC5705890/ /pubmed/29270210 http://dx.doi.org/10.1155/2017/5267864 Text en Copyright © 2017 Seán Cournane et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cournane, Seán
Conway, Richard
Byrne, Declan
O'Riordan, Deirdre
Silke, Bernard
Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram
title Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram
title_full Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram
title_fullStr Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram
title_full_unstemmed Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram
title_short Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram
title_sort predicting outcomes in emergency medical admissions using a laboratory only nomogram
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5705890/
https://www.ncbi.nlm.nih.gov/pubmed/29270210
http://dx.doi.org/10.1155/2017/5267864
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