Identifying demographic, social and clinical predictors of biologic therapy effectiveness in psoriasis: a multicentre longitudinal cohort study. (1st May 2019) - Warren, R.B.; Marsden, A.; Tomenson, B.; Mason, K.J.; Soliman, M.M.; Burden, A.D.; Reynolds, N.J.; Stocken, D.; Emsley, R.; Griffiths, C.E.M.; Smith, C.; ,
Summary: Background: Biologic therapies have revolutionized the treatment of moderate‐to‐severe psoriasis. However, for reasons largely unknown, many patients do not respond or lose response to these drugs. Objectives: To evaluate demographic, social and clinical factors that could be used to predict effectiveness and stratify response to biologic therapies in psoriasis. Methods: Using a multicentre, observational, prospective pharmacovigilance study (BADBIR), we identified biologic‐naive patients starting biologics with outcome data at 6 ( n = 3079) and 12 ( n = 3110) months. Associations between 31 putative predictors and outcomes were investigated in univariate and multivariable regression analyses. Potential stratifiers of treatment response were investigated with statistical interactions. Results: Eight factors associated with reduced odds of achieving ≥ 90% improvement in Psoriasis Area and Severity Index (PASI 90) at 6 months were identified (described as odds ratio and 95% confidence interval): demographic (female sex, 0·78, 0·66–0·93); social (unemployment, 0·67, 0·45–0·99); unemployment due to ill health (0·62, 0·48–0·82); ex‐ and current smoking (0·81, 0·66–0·99 and 0·79, 0·63–0·99, respectively); clinical factors (high weight, 0·99, 0·99–0·99); psoriasis of the palms and/or soles (0·75, 0·61–0·91); and presence of small plaques only compared with small and large plaques (0·78, 0·62–0·96). White ethnicity (1·48, 1·12–1·97) and higher baseline PASI (1·04, 1·03–1·04) were associated with increased odds of achieving PASI 90. The findings were largely consistent at 12 months. There was little evidence for predictors of differential treatment response. Conclusions: Psoriasis phenotype and potentially modifiable factors are associated with poor outcomes with biologics, underscoring the need for lifestyle management. Effect sizes suggest that these factors alone cannot inform treatment selection.