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Prognostic factors in chronic hypersensitivity pneumonitis
Hypersensitivity pneumonitis (HP) is an immunologically mediated lung disease resulting from exposure to inhaled environmental antigens. Prognosis is variable, with a subset of patients developing progressive fibrosis leading to respiratory failure and death. Therefore, there is an urgent need to id...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
European Respiratory Society
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9489027/ https://www.ncbi.nlm.nih.gov/pubmed/32414744 http://dx.doi.org/10.1183/16000617.0167-2019 |
Sumario: | Hypersensitivity pneumonitis (HP) is an immunologically mediated lung disease resulting from exposure to inhaled environmental antigens. Prognosis is variable, with a subset of patients developing progressive fibrosis leading to respiratory failure and death. Therefore, there is an urgent need to identify factors which predict prognosis and survival in patients with HP. We undertook a narrative review of existing evidence to identify prognostic factors in patients with chronic HP. Patient demographics, smoking history, extent of antigen exposure and comorbidities all have reported associations with disease outcome, and physiological, radiological and laboratory markers have been shown to predict overall survival. While no single marker has been demonstrated to accurately and reliably predict prognosis, older age, more severe impairment of pulmonary function at baseline and established fibrosis on either biopsy or high-resolution computed tomography are consistently associated with worse survival. The vast majority of existing studies are retrospective, and this review identifies a need for prospective longitudinal studies with serial assessment of respiratory health to ascertain factors associated with nonfatal deterioration. Future developments, including the development of HP-specific composite scores may help further improve our ability to predict outcomes for individual patients. |
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