Rheumatoid Arthritis Clinical Trial
Official title:
Development of a Prediction Model for the Risk of Hospitalized Infection in Patients With Chronic Inflammatory Arthritis Treated With Biological Drugs
Background The risk for hospitalized infection (i.e. infection leading to hospitalization) in
patients with inflammatory arthritis (rheumatoid arthritis (RA), psoriatic arthritis (PsA) or
axial spondyloarthritis (axSpA) treated with biological drugs is known to be increased
compared to the background population. In daily clinical practice, there is a need for a
simple way to assess the absolute risk for hospitalized infection in individual patients
based on easily available information such as age, diagnosis, functional status,
comorbidities and medication. This risk estimate will be useful in clinical decision making
e.g. when advising patients on whether or not to initiate biologic therapy or when advising
patients on influenza or pneumococcal vaccination.
Objectives The objectives are 1) to assess the risk for hospitalized infection (infection
leading to hospitalization) in patients with inflammatory arthritis during 12 months of
follow-up after initiating treatment with their first biological drug (bDMARD) with the risk
in the general population, and 2) to develop a simple, clinically useful algorithm that
allows prediction of the risk of hospitalized infection in individual patients.
Methods Observational cohort study based on existing data in: The Danish Rheumatology
Register (DANBIO), The Danish National Patient Register, The Danish National Prescription
Register and The Danish Register of Causes of Death. All patients registered in DANBIO with
RA, PsA or axSpA who initiated treatment with their first biological drug between January 1,
2006 and December 31, 2016 will be identified. Baseline predictors and outcomes (hospitalized
infection or death) during 12 months of follow-up are obtained. Logistic regression analysis
and 10-fold cross-validation will be used to develop and internally validate the prediction
model.
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