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.
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.
Authors: Chip M Lynch; Behnaz Abdollahi; Joshua D Fuqua; Alexandra R de Carlo; James A Bartholomai; Rayeanne N Balgemann; Victor H van Berkel; Hermann B Frieboes Journal: Int J Med Inform Date: 2017-09-25 Impact factor: 4.046
Authors: Lucyna Mastalerz; Katarzyna E Tyrak; Maria Ignacak; Ewa Konduracka; Filip Mejza; Adam Ćmiel; Michał Buczek; Adrianna Kot; Krzysztof Oleś; Marek Sanak Journal: Allergy Date: 2018-12-05 Impact factor: 13.146
Authors: Katherine N Cahill; Christina B Johns; Jing Cui; Paige Wickner; David W Bates; Tanya M Laidlaw; Patrick E Beeler Journal: J Allergy Clin Immunol Date: 2016-07-25 Impact factor: 10.793
Authors: Alan Kaplan; Hui Cao; J Mark FitzGerald; Nick Iannotti; Eric Yang; Janwillem W H Kocks; Konstantinos Kostikas; David Price; Helen K Reddel; Ioanna Tsiligianni; Claus F Vogelmeier; Pascal Pfister; Paul Mastoridis Journal: J Allergy Clin Immunol Pract Date: 2021-02-19