5 research outputs found
Time to minimal disease activity in relation to quality of life, productivity, and radiographic damage 1year after diagnosis in psoriatic arthritis
Background: In a cohort of patients with newly diagnosed psoriatic arthritis (PsA) who received usual care, we
investigated the impact of time elapsed to minimal disease activity (MDA) on health-related quality of life (HRQoL),
work productivity, and radiographic damage throughout the first year after diagnosis.
Methods: Data collected in the Dutch southwest early PsA cohort (DEPAR) study were analyzed. These threemonthly data encompassed disease activity, HRQOL was measured with the Short Form 36 (SF36) Physical
Component Scale (SF36-PCS) and Mental Component Scale, and productivity was measured with the Productivity
Cost Questionnaire. Radiographic damage was scored at baseline and at 12 months with the PsA-modified Sharp/
van der Heijde score. Patients were classified by time to MDA as in early (within 3 months), late (at 6–12 months),
and never MDA in the first year.
Results: We included 296 patients who had had their 1-year outpatient visit (mean age 51 years, 53% male). Ninetysix (32%) were classified as early MDA, 78 (26%) as late MDA, and 98 (33%) as never MDA. Data of 24 patients (8%)
were missing. SF36-PCS and productivity scores improved after gaining MDA, but remained low in never MDA
patients. At 1 year, SF36-PCS and productivity scores were similar in early and late MDA patients. Radiographic
progression rate was low and similar in all groups.
Conclusion: Gaining MDA was associated with considerable improvement in HRQoL and functioning, irrespective
of time to first MDA. In the one third of patients not in MDA in the first year, the disease had a substantial health
impact
Influence of Disease Manifestations on Health-related Quality of Life in Early Psoriatic Arthritis
Burden of Psoriatic Arthritis According to Different Definitions of Disease Activity: Comparing Minimal Disease Activity and the Disease Activity Index for Psoriatic Arthritis
Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions
BACKGROUND: Biological disease-modifying antirheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis. However, as bDMARDs may also lead to adverse events and are expensive, tapering them is of great clinical interest. Tapering according to disease activity-guided dose optimization (DGDO) does not seem to affect long term remission rates, but flares are frequent during this process. Our objective was to develop a model for the prediction of flares during bDMARD tapering using data from routine care and to evaluate its potential clinical impact. METHODS: We used a joint latent class model to repeatedly predict the probability of a flare occurring within the next 3 months. The model was developed using longitudinal data on disease activity (DAS28) and other routine care data from two clinics. Predictive accuracy was assessed in cross-validation and external validation was performed with data from the DRESS (Dose REduction Strategy of Subcutaneous tumor necrosis factor inhibitors) trial. Additionally, we simulated the reduction in number of flares and bDMARD dose when implementing the model as a decision aid during bDMARD tapering in the DRESS trial. RESULTS: Data from 279 bDMARD courses were used for model development. The final model included two latent DAS28-trajectories, bDMARD type and dose, disease duration, and seropositivity. The area under the curve of the final model was 0.76 (0.69-0.83) in cross-validation and 0.68 (0.62-0.73) in external validation. In simulation of prediction-aided decisions, the mean number of flares over 18 months decreased from 1.21 (0.99-1.43) to 0.75 (0.54-0.96). The reduction in he bDMARD dose was mostly maintained, increasing from 54 to 64% of full dose. CONCLUSIONS: We developed a dynamic flare prediction model, exclusively based on data typically available in routine care. Our results show that using this model to aid decisions during bDMARD tapering may significantly reduce the number of flares while maintaining most of the bDMARD dose reduction. TRIAL REGISTRATION: The clinical impact of the prediction model is currently under investigation in the PATIO randomized controlled trial (Dutch Trial Register number NL9798)