Background: Several treatment options are currently available for the treatment of psoriasis.

Objective: To explore the main associations between patients' characteristics and systemic treatments prescribed for psoriasis in a large group of patients observed in real-life clinical practice.

Methods: This was a retrospective analysis of baseline data collected within the Swiss Dermatology Network for Targeted Therapies registry in Switzerland between March 2011 and December 2017. Semantic map analysis was used in order to capture the best associations between variables taking into account other covariates in the system.

Results: A total of 549 patients (mean age 46.7 ± 14.7 years) were included in the analysis. Conventional therapies such as retinoids and methotrexate were associated with no previous systemic therapies for psoriasis, a moderate quality of life (QoL) at therapy onset and older age (≥60 years). Fumaric acid derivatives were associated with mild psoriasis (psoriasis area severity index < 10) and long disease duration (≥20 years). On the other side, cyclosporine and psoralen and ultraviolet A/ultraviolet B treatments were linked to a more severe condition, including impaired QoL, hospitalization and inability to work. Regarding biological therapies, both infliximab and adalimumab were connected to the presence of psoriatic arthritis, severe disease condition and other comorbidities, including chronic liver or kidney diseases and tuberculosis. Etanercept, ustekinumab and secukinumab were all connected to a complex history of previous systemic treatments for psoriasis, moderate disease condition, overweight and university education.

Conclusions: The analysis shows multifaceted associations between patients' characteristics, comorbidities, disease severity and systemic treatments prescribed for psoriasis. In particular, our semantic map indicates that comorbidities play a central role in decision-making of systemic treatments usage for psoriasis. Future studies should further investigate specific connections emerging from our data.

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http://dx.doi.org/10.1111/jdv.15983DOI Listing

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