Is tightly controlled disease activity possible with online patient-reported outcomes?

To evaluate the performance of patient-reported outcomes (PRO) as primary indices for identification and prediction of a 28-joint Disease Activity Score (DAS28)>3.2 among patients with rheumatoid arthritis (RA).

Patients with RA completed monthly online PRO [Health Assessment Questionnaire (HAQ), Rheumatoid Arthritis Disease Activity Index (RADAI), visual analog scale (VAS) fatigue] and were clinically assessed every 3 months using the DAS28. Simple descriptive statistics, logistic regression, and the Bayesian joint modeling approach were used to analyze the data. The Bayesian joint model combines the scores and changes in the scores of 3 PRO to predict a DAS28>3.2 at the subsequent timepoint.

A group of 159 patients with RA participated. Stratified summaries of the PRO by DAS28 categories at baseline provided incremental values of the PRO for more active disease. However, on an individual level, the DAS28 and the PRO fluctuated over time. The prediction of subsequent DAS score by a single instrument at single timepoints resulted in moderate sensitivity and specificity. Using the intercept and slope of the combined PRO of the first 3 measurements to predict the DAS28 state at 3 months resulted in a sensitivity of 0.81 and a specificity of 0.92. After 10-fold cross validation, the model had a sensitivity of 0.61 and specificity of 0.75 to identify patients with a DAS28>3.2.

PRO showed fluctuating levels of disease activity over time, while on a group level disease activity stayed the same. Using the changes in RADAI, HAQ, and VAS fatigue over time to predict future DAS28>3.2 resulted in moderate performance after the internal cross-validation of the model (sensitivity 0.61, specificity 0.75).