Predicting treatment outcomes following an exacerbation of airways disease.

<h4>Background</h4>COPD and asthma exacerbations result in many emergency department admissions. Not all treatments are successful, often leading to hospital readmissions.<h4>Aims</h4>We sought to develop predictive models for exacerbation treatment outcome in a cohort of exa...

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Autores principales:	Andreas Halner, Sally Beer, Richard Pullinger, Mona Bafadhel, Richard E K Russell
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1d82f80a5f2c4e26a9d87215740c54ed
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spelling	oai:doaj.org-article:1d82f80a5f2c4e26a9d87215740c54ed2021-12-02T20:14:57ZPredicting treatment outcomes following an exacerbation of airways disease.1932-620310.1371/journal.pone.0254425https://doaj.org/article/1d82f80a5f2c4e26a9d87215740c54ed2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254425https://doaj.org/toc/1932-6203<h4>Background</h4>COPD and asthma exacerbations result in many emergency department admissions. Not all treatments are successful, often leading to hospital readmissions.<h4>Aims</h4>We sought to develop predictive models for exacerbation treatment outcome in a cohort of exacerbating asthma and COPD patients presenting to the emergency department.<h4>Methods</h4>Treatment failure was defined as the need for additional systemic corticosteroids (SCS) and/or antibiotics, hospital readmissison or death within 30 days of initial emergency department visit. We performed univariate analysis comparing characteristics of patients either given or not given SCS at exacerbation and of patients who succeeded versus failed treatment. Patient demographics, medications and exacerbation symptoms, physiology and biology were available. We developed multivariate random forest models to identify predictors of SCS prescription and for predicting treatment failure.<h4>Results</h4>Data were available for 81 patients, 43 (53%) of whom failed treatment. 64 (79%) of patients were given SCS. A random forest model using presence of wheeze at exacerbation and blood eosinophil percentage predicted SCS prescription with area under receiver operating characteristic curve (AUC) 0.69. An 11 variable random forest model (which included medication, previous exacerbations, symptoms and quality of life scores) could predict treatment failure with AUC 0.81. A random forest model using just the two best predictors of treatment failure, namely, visual analogue scale for breathlessness and sputum purulence, predicted treatment failure with AUC 0.68.<h4>Conclusion</h4>Prediction of exacerbation treatment outcome can be achieved via supervised machine learning combining different predictors at exacerbation. Validation of our predictive models in separate, larger patient cohorts is required.Andreas HalnerSally BeerRichard PullingerMona BafadhelRichard E K RussellPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0254425 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andreas Halner Sally Beer Richard Pullinger Mona Bafadhel Richard E K Russell Predicting treatment outcomes following an exacerbation of airways disease.
description	<h4>Background</h4>COPD and asthma exacerbations result in many emergency department admissions. Not all treatments are successful, often leading to hospital readmissions.<h4>Aims</h4>We sought to develop predictive models for exacerbation treatment outcome in a cohort of exacerbating asthma and COPD patients presenting to the emergency department.<h4>Methods</h4>Treatment failure was defined as the need for additional systemic corticosteroids (SCS) and/or antibiotics, hospital readmissison or death within 30 days of initial emergency department visit. We performed univariate analysis comparing characteristics of patients either given or not given SCS at exacerbation and of patients who succeeded versus failed treatment. Patient demographics, medications and exacerbation symptoms, physiology and biology were available. We developed multivariate random forest models to identify predictors of SCS prescription and for predicting treatment failure.<h4>Results</h4>Data were available for 81 patients, 43 (53%) of whom failed treatment. 64 (79%) of patients were given SCS. A random forest model using presence of wheeze at exacerbation and blood eosinophil percentage predicted SCS prescription with area under receiver operating characteristic curve (AUC) 0.69. An 11 variable random forest model (which included medication, previous exacerbations, symptoms and quality of life scores) could predict treatment failure with AUC 0.81. A random forest model using just the two best predictors of treatment failure, namely, visual analogue scale for breathlessness and sputum purulence, predicted treatment failure with AUC 0.68.<h4>Conclusion</h4>Prediction of exacerbation treatment outcome can be achieved via supervised machine learning combining different predictors at exacerbation. Validation of our predictive models in separate, larger patient cohorts is required.
format	article
author	Andreas Halner Sally Beer Richard Pullinger Mona Bafadhel Richard E K Russell
author_facet	Andreas Halner Sally Beer Richard Pullinger Mona Bafadhel Richard E K Russell
author_sort	Andreas Halner
title	Predicting treatment outcomes following an exacerbation of airways disease.
title_short	Predicting treatment outcomes following an exacerbation of airways disease.
title_full	Predicting treatment outcomes following an exacerbation of airways disease.
title_fullStr	Predicting treatment outcomes following an exacerbation of airways disease.
title_full_unstemmed	Predicting treatment outcomes following an exacerbation of airways disease.
title_sort	predicting treatment outcomes following an exacerbation of airways disease.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1d82f80a5f2c4e26a9d87215740c54ed
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Predicting treatment outcomes following an exacerbation of airways disease.

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