Pain rating variability and response to treatment in osteoarthritis clinical trials

Despite recognition that osteoarthritis (OA) pain is dynamic, most clinical trials rely on static, single-point assessments. This study examined whether daily pre-treatment pain variability predicts treatment response in OA. We retrospectively analyzed data from 345 knee OA patients enrolled in two randomized, double-blind trials comparing naproxen (500 mg BID; n=190) to placebo (n=155). Participants reported daily pain (0-10 NRS) over 117 days (5-day baseline + 112-day treatment). Pain variability before treatment was quantified using: (1) standard deviation (pre-treatment pain variability, PTPV); (2) autocorrelation (AR); and (3) probability of acute change (PAC; ≥2-point shift). Logistic regression assessed responder status (≥30% pain reduction), and linear mixed-effects models evaluated longitudinal pain changes. Participants were stratified by treatment arm and high/low PTPV (median split). Correlations between variability metrics were also analyzed. Higher PTPV significantly predicted treatment response at Weeks 4, 8, 12, and 16 (p<0.05). Though both groups improved, participants receiving naproxen with high PTPV showed significantly greater pain reduction over time (p<0.001) compared to those with low variability and placebo subgroups. PTPV was not correlated with baseline pain, indicating independence from pain severity. Variability metrics captured distinct aspects of the pain experience: PTPV and PAC were strongly correlated (r=0.67), AR was negatively correlated with PAC (r=-0.25), and weakly with PTPV (r=0.12). Importantly none was associated with mean pain levels. Our study shows pre-treatment pain variability is a robust, independent predictor of response to both active drug and placebo. We suggest incorporating dynamic pain metrics may improve outcome prediction and trial design in OA. PERSPECTIVE: This study demonstrates that pre-treatment pain variability reflects unique aspects of the pain experience, independent of average pain intensity. Its consistent association with treatment response-regardless of intervention-supports its value as a predictive marker. Incorporating variability into clinical trial design may enhance outcome sensitivity and improve patient stratification.