Biomarkers for predicting response to aspirin therapy in aspirin-exacerbated respiratory disease

BACKGROUND: Aspirin desensitization followed by daily aspirin use is an effective treatment for aspirin‐exacerbated respiratory disease (AERD). OBJECTIVE: To assess clinical features as well as genetic, immune, cytological and biochemical biomarkers that might predict a positive response to high‐dose aspirin therapy in AERD. METHODS: We enrolled 34 AERD patients with severe asthma who underwent aspirin desensitization followed by 52‐week aspirin treatment (650 mg/d). At baseline and at 52 weeks, clinical assessment was performed; phenotypes based on induced sputum cells were identified; eicosanoid, cytokine and chemokine levels in induced sputum supernatant were determined; and induced sputum expression of 94 genes was assessed. Responders to high‐dose aspirin were defined as patients with improvement in 5‐item Asthma Control Questionnaire score, 22‐item Sino‐Nasal Outcome Test (SNOT‐22) score and forced expiratory volume in 1 second at 52 weeks. RESULTS: There were 28 responders (82%). Positive baseline predictors of response included female sex (p = .002), higher SNOT‐22 score (p = .03), higher blood eosinophil count (p = .01), lower neutrophil percentage in induced sputum (p = .003), higher expression of the hydroxyprostaglandin dehydrogenase gene, HPGD (p = .004) and lower expression of the proteoglycan 2 gene, PRG2 (p = .01). The best prediction model included Asthma Control Test and SNOT‐22 scores, blood eosinophils and total serum immunoglobulin E. Responders showed a marked decrease in sputum eosinophils but no changes in eicosanoid levels. CONCLUSIONS AND CLINICAL RELEVANCE: Female sex, high blood eosinophil count, low sputum neutrophil percentage, severe nasal symptoms, high HPGD expression and low PRG2 expression may predict a positive response to long‐term high‐dose aspirin therapy in patients with AERD

Artificial neural network identifies nonsteroidal anti‐inflammatory drugs exacerbated respiratory disease (N‐ERD) cohort

Background: To 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. Methods: This 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. Results: The 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). Conclusions: The 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

Artificial neural network identifies nonsteroidal anti‐inflammatory drugs exacerbated respiratory disease (N‐ERD) cohort

Background: To 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. Methods: This 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. Results: The 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). Conclusions: The 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