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Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic
BACKGROUND: During the coronavirus disease 2019 (COVID‐19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hyp...
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/PMC9579891/ https://www.ncbi.nlm.nih.gov/pubmed/36267429 http://dx.doi.org/10.1002/clt2.12201 |
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author | Gawlewicz‐Mroczka, Agnieszka Pytlewski, Adam Celejewska‐Wójcik, Natalia Ćmiel, Adam Gielicz, Anna Sanak, Marek Mastalerz, Lucyna |
author_facet | Gawlewicz‐Mroczka, Agnieszka Pytlewski, Adam Celejewska‐Wójcik, Natalia Ćmiel, Adam Gielicz, Anna Sanak, Marek Mastalerz, Lucyna |
author_sort | Gawlewicz‐Mroczka, Agnieszka |
collection | PubMed |
description | BACKGROUND: During the coronavirus disease 2019 (COVID‐19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin‐tolerant asthma. METHODS: We used a prospective database of 135 patients with non‐steroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (NERD) and 81 NSAID‐tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques. RESULTS: The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy‐to‐obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%. CONCLUSIONS: ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID‐19 pandemic. |
format | Online Article Text |
id | pubmed-9579891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95798912022-10-19 Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic Gawlewicz‐Mroczka, Agnieszka Pytlewski, Adam Celejewska‐Wójcik, Natalia Ćmiel, Adam Gielicz, Anna Sanak, Marek Mastalerz, Lucyna Clin Transl Allergy Original Article BACKGROUND: During the coronavirus disease 2019 (COVID‐19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin‐tolerant asthma. METHODS: We used a prospective database of 135 patients with non‐steroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (NERD) and 81 NSAID‐tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques. RESULTS: The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy‐to‐obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%. CONCLUSIONS: ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID‐19 pandemic. John Wiley and Sons Inc. 2022-10-19 /pmc/articles/PMC9579891/ /pubmed/36267429 http://dx.doi.org/10.1002/clt2.12201 Text en © 2022 The Authors. Clinical and Translational Allergy published by John Wiley & Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Gawlewicz‐Mroczka, Agnieszka Pytlewski, Adam Celejewska‐Wójcik, Natalia Ćmiel, Adam Gielicz, Anna Sanak, Marek Mastalerz, Lucyna Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic |
title | Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic |
title_full | Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic |
title_fullStr | Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic |
title_full_unstemmed | Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic |
title_short | Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic |
title_sort | machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579891/ https://www.ncbi.nlm.nih.gov/pubmed/36267429 http://dx.doi.org/10.1002/clt2.12201 |
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