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Prevalence and characterization of severe asthma in Hungary

Background: Severe asthma (SA) database was established in Hungary to estimate the prevalence of SA and to define and analyze clinical phenotypes of the patients. Methods: SA questionnaires were sent out to 143 public pulmonary dispensaries specialized for diagnosing and caring pulmonary patients. D...

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Detalles Bibliográficos
Autores principales: Csoma, Zsuzsanna, Gál, Zsófia, Gézsi, András, Herjavecz, Irén, Szalai, Csaba
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7283249/
https://www.ncbi.nlm.nih.gov/pubmed/32518278
http://dx.doi.org/10.1038/s41598-020-66445-4
Descripción
Sumario:Background: Severe asthma (SA) database was established in Hungary to estimate the prevalence of SA and to define and analyze clinical phenotypes of the patients. Methods: SA questionnaires were sent out to 143 public pulmonary dispensaries specialized for diagnosing and caring pulmonary patients. Data of 520 SA patients were evaluated. Results: The prevalence of SA within the asthmatic population in Hungary was 0.89%. The mean age of patients were 56.4 ± 13.4 years, SA were more frequent in females (64%), the prevalence of allergy was 56.6%, 72.1% of patients had persistent airflow limitation (FEV1 < 80%), 37.9% severe airway obstruction (FEV1 ≤ 60%), 33.6% required systemic corticosteroid maintenance therapy, 21.5% had salicylate intolerance and 43.2% rhinosinusitis. A Bayesian dependency network was calculated which revealed several interdependencies between the characteristics. E.g. there was a strong association between salicylate intolerance and rhinosinusitis, more patients received regular systemic corticosteroid treatment who had salicylate intolerance and the proportion of salicylate intolerance was significantly higher in females. Conclusion: The prevalence of SA was determined in Hungary which was lower than in other studies. Using a Bayesian-based network analysis several interdependencies were revealed between patient characteristics.