GISEA: an Italian biological agents registry in rheumatology

Abstract

The GISEA registry is an independent database that was established by the Italian Group for the Study of Early Arthritis (GISEA) in 2008, funded by the Italian Association of Rheumatic Patients (ANMAR - ONLUS). In line with the network's epidemiological strategy, the initial protocol was designed to collect long-term follow-up data concerning patients with rheumatic diseases treated with biological agents in order to investigate the realworld characteristics in terms of disease activity, comorbidities and survival on treatment. We here describe the design and methodology used to collect patient data. Information concerning demographics, disease activity, treatment changes (including the reasons for changing and the duration of each therapy), concomitant therapies and adverse events is available to all the members of the study groups by means of a web-based interface that allows queries and the presentation of numerical data, as well as graphics to illustrate trends. Fourteen Italian rheumatology centres have contributed patients to the database which, at the time writing, includes 5145 patients (72% women) with a mean age of 53 years (range 16-88). The initial diagnoses were rheumatoid arthritis (3494 patients, 67.9%), psoriatic arthritis (833, 16.2%), ankylosing spondylitis (493, 9.6%), undifferentiated spondylo-arthritides (307, 5.9%), enteropathic arthritis (14, 0.3%) and spondylitis following reactive arthritis (4, 0.1%). These patients have been followed for up to 10 years, and 1927 (35.8%) have been treated for at least three years. The biological treatments received include etanercept, infliximab, anakinra, adalimumab, abatacept, rituximab and tocilizumab. A total of 2926 adverse events have been observed, with 1171 patients (22%) reporting at least one. Analysis of the accumulated data will provide insights into the critical early phase of the studied arthritides, and enable us to identify the clinical and laboratory profiles that may predict responsiveness to a specific therapy.