Interstitial Pneumonia With Autoimmune Features (IPAF) - Fernandes, Ligia; Nasser, Mouhamad; Ahmad, Kais; Cottin, Vincent (Frontiers in Medicine, 2019)
A significant proportion of patients with interstitial lung disease (ILD) manifest autoimmune features, but do not fulfil the diagnostic criteria for a definite connective tissue disease (CTD). In 2015, the European Respiratory Society (ERS) and American Thoracic Society (ATS) “Task Force on undifferentiated Forms of connective tissue disease-associated interstitial lung disease” proposed classification criteria for a so-called research category of Interstitial Pneumonia with Autoimmune Features (IPAF). These classification criteria were based on a combination of features from three domains: a clinical domain consisting of extra-thoracic features; a serologic domain with specific autoantibodies; and a morphologic domain with imaging patterns, histopathological findings or multi-compartment involvement. Patients meeting IPAF criteria tend to have a history of smoking similar to patients with idiopathic pulmonary fibrosis. The main clinical and serological markers of autoimmune features are Raynaud' phenomenon and positive antinuclear antibodies, respectively. Nonspecific interstitial pneumonia is the main radiologic and histopathologic pattern. However, the radiologic or histologic pattern may be that of usual interstitial pneumonia, with IPAF criteria belonging to the clinical and the serological domains. Management should be carefully individualized on a case-by-case basis in keeping with the wide heterogeneity of IPAF and lack of evidence in this particular subgroup of patients. Prognosis is generally intermediate between that of idiopathic pulmonary fibrosis and connective tissue disease-associated interstitial lung disease, but substantially variable according to the predominant histologic and radiologic patterns. As acknowledged by the Task Force, the proposed classification scheme of IPAF is a research concept that will need revision and refinement based on data to better inform prognostication and patient care.