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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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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. |
format | Online Article Text |
id | pubmed-5705890 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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