A new predictive model has identified that fibromyalgia, nail and pustular psoriasis, and steroid use are significant predictors of treatment difficulty in psoriatic arthritis, according to research.
The single-center study, published in Frontiers in Medicine, analyzing 267 consecutive adult psoriatic arthritis (PsA) patients in Milan, Italy, found that only 2.9% met the current difficult-to-treat (D2T) criteria, while 17.2% of patients demonstrated higher treatment difficulty using a new predictive model based on biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) failure rates.
When applying the predicted actual-to-expected number of cycled b/tsDMARDs, the proportion of difficult-to-treat patients reduced to 8.4% among the 177 patients analyzed using the predictive model.
The study, conducted between September 2022 and February 2023, revealed several key factors associated with treatment difficulty through univariate analysis, including female sex, psoriasis pattern, fibromyalgia, and steroid therapy. Multivariate analysis confirmed trends of association with nail involvement, palmoplantar psoriasis, steroid use, concomitant fibromyalgia, and HAQ and PhGA scores. Conversely, plaque psoriasis and scalp psoriasis were linked to lower levels of treatment difficulty.
Authors Ennio Giulio Favalli, et al. emphasize that while current D2T PsA criteria identify a small subset of patients with significant treatment challenges, the criteria require substantial refinement to better reflect the complex nature of the disease.
The research focused on developing a statistical model to intercept prognostic factors associated with more complex management. The primary outcome measured the identification of patients with increased difficulty in managing their condition, defined by an elevated number of failed b/tsDMARDs relative to the expected rate of treatment failures.
Psoriatic arthritis, a chronic autoimmune disease affecting both joints and skin, presents unique treatment challenges due to its heterogeneous nature. Despite advances in biologic and targeted therapies, some patients do not respond adequately, and there is currently no universally accepted definition for D2T PsA.
The researchers emphasized that this predictive model could improve patient stratification by providing insights into characteristics most associated with treatment failure. This could potentially lead to more tailored treatment approaches for patients with resistant disease.
Several limitations were noted, including the relatively low sample size from a monocentric cohort and the study's cross-sectional design, which partially limit the generalizability of the results. Nevertheless, this research represents an important step toward refining the definition of D2T PsA and improving treatment outcomes.
The authors report no commercial or financial relationships that could present a potential conflict of interest.
