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.

Implementation of the new ILAE classification of epilepsies into clinical practice - A cohort study.

PURPOSE: Appropriate management of patients with epilepsy requires precise classification of their disease. Implementation of the recent International League Against Epilepsy (ILAE) classification of seizures and epilepsies may affect data on the relative proportions of specific types of seizures or epilepsies and should be tested in everyday practice. The aim of the study was to determine the prevalence of specific epilepsy types, syndromes, and etiologies, as defined by the new ILAE classification, in a large cohort of adult patients with epilepsy. MATERIAL AND METHODS: The single-center cohort study involved consecutive adult patients with epilepsy seen at the university epilepsy clinic. Information about medical history, neurological examination, neuroimaging, electroencephalography (EEG), genetic tests, epilepsy treatment, and other investigations was collected from medical records and prospectively updated if necessary. Epilepsy types and etiology, as well as epileptic syndromes, were classified according to the new ILAE classifications. RESULTS: We studied 653 patients (mean age: 37.2 years, 59.9% were women). Epilepsy was classified as focal in 458 cases (70.2%), generalized in 155 subjects (23.7%), or as combined focal and generalized in 11 patients (1.7%). The epilepsy type was labeled as unknown in 29 (4.4%) patients. A definite cause of epilepsy was identified in 59.4% of the cases, with a structural etiology (n = 179, 27.4%) and genetic or presumed genetic etiology (n = 169, 25.9%) being the most common. In 167 (25.5%) patients, specific epilepsy syndromes, mostly genetic generalized epilepsy syndromes, were diagnosed. CONCLUSION: The use of the recent ILAE classification of seizures and epilepsies in the cohort of patients with epilepsy seen in single epilepsy center enabled unequivocal characterization of epilepsy type in >95% of patients. A definite etiology of epilepsy could be established in about 60% of patients.