Predictors of Pain Response in Knee OA

Osteoarthritis (OA) is the most common form of arthritis worldwide, one of the most prevalent sources of chronic musculoskeletal pain, and a leading cause of disability. It affects 32.5 million of United States (US) adults, with over half of this population, 18.7 million, in the working age range (18-64 years) (1) OA is a heterogeneous clinical syndrome, and it is now widely accepted that different phenotypes of the disease can identify trajectories of disease progression and further guide clinical management of OA patients (2,3). However, presently, the amount of information needed to phenotype and classify OA patients is large and time-consuming. The establishment of meaningful clinical profiles, with tangible collected information in the clinical set, that can further predict therapeutic responses is much needed in designing and implementing clinical trials as well as in clinical practice. It is known that substantial fluctuations in pain intensity appear to be part of the natural history for some patients with OA. It was shown in the past that this individual variability in pain ratings is related to better response to treatment in OA clinical trials. Moreover, recent evidence shows individual pain variability is a better predictor of patients who respond preferentially to drug over placebo (4). Whether other baseline factors may also be important is not known and the impact of different means of assessing pain, e.g., WOMAC vs NRS, have not been evaluated,

In this project we aim to study pain variability and other clinical parameters from several clinical trials of OA pain. In brief, we aim to evaluate how individual pain variability (measured as the standard deviation from the mean for each subject) before treatment predicts pain recovery (>30% pain change) in placebo and active groups. We also will investigate the impact of other clinical data and means of assessing pain that have been collected on this outcome.

Our results extend previous findings to include a correlation between experimental pain variability and the placebo response and suggest that experimental pain variability is a better predictor of patients who respond preferentially to drug over placebo. These data will also inform the optimal use of different pain measures in clinical trials of musculoskeletal pain.