ArticleAllergy2020
Artificial neural network identifies nonsteroidal anti-inflammatory drugs exacerbated respiratory disease (N-ERD) cohort.
Article in Allergy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Paucigranulocytic Asthma in Aspirin-Hypersensitive Patients: Pleiotropic Regulation of Type 2 Biomarkers.Clinical and translational allergy · 2026Article
- Gut microbiota mediates the anti-obesity effects ofFrontiers in nutrition · 2026Article
- Aspirin hypersensitivity diagnostic index (AHDI): In vitro test for diagnosing of N-ERD based on urinary 15-oxo-ETE and LTEAllergy · 2025Article
- An overview of immunotoxicity in drug discovery and development.Toxicology letters · 2025Review
- Non-eosinophilic asthma in nonsteroidal anti-inflammatory drug exacerbated respiratory disease.Clinical and translational allergy · 2023Article
- Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic.Clinical and translational allergy · 2022Article
- Local and Systemic Production of Pro-Inflammatory Eicosanoids Is Inversely Related to Sensitization to Aeroallergens in Patients with Aspirin-Exacerbated Respiratory Disease.Journal of personalized medicine · 2022Article
- Artificial neural network identifies nonsteroidal anti-inflammatory drugs exacerbated respiratory disease (N-ERD) cohort.Allergy · 2020Article
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Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo date, there has been no reliable in vitro test to either diagnose or differentiate nonsteroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (N-ERD). The aim of the present study was to develop and validate an artificial neural network (ANN) for the prediction of N-ERD in patients with asthma.
methodsThis study used a prospective database of patients with N-ERD (n = 121) and aspirin-tolerant (n = 82) who underwent aspirin challenge from May 2014 to May 2018. Eighteen parameters, including clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant (ISS) and urine were extracted for the ANN.
resultsThe validation sensitivity of ANN was 94.12% (80.32%-99.28%), specificity was 73.08% (52.21%-88.43%), and accuracy was 85.00% (77.43%-92.90%) for the prediction of N-ERD. The area under the receiver operating curve was 0.83 (0.71-0.90).
conclusionsThe designed ANN model seems to have powerful prediction capabilities to provide diagnosis of N-ERD. Although it cannot replace the gold-standard aspirin challenge test, the implementation of the ANN might provide an added value for identification of patients with N-ERD. External validation in a large cohort is needed to confirm our results.
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