Identifying demographic, social and clinical predictors of biologic therapy effectiveness in psoriasis: a multicentre longitudinal cohort study

Abstract

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.