Development of clinical prediction models for good or bad response to classic systemic drugs, anti-TNFs, and ustekinumab in psoriasis, based on the BIOBADADERM cohort.

Background: Identifying patients likely to have very good or bad results from systemic psoriasis therapy could improve efficiency of therapy.
Objective: To develop prognostic models for good or bad response to classic systemic drugs, anti-TNFs, and ustekinumab in psoriasis.
Methods: Multivariable logistic regression of a prospective multicenter cohort of psoriatic patients in clinical practice (6449 person-years of follow-up). We used as possible predictors demographic characteristics, comorbidities, characteristics of the psoriasis (type, PASI, arthritis), history of past therapy at entry in the cohort, and history of response to previous cycles while in the cohort. We defined good response to a treatment cycle as either cycle end due to disease remission or a cycle longer than 2 years that does not end later due to inefficacy in the follow-up period. Bad response to a treatment cycle was defined as a cycle that is finished due to inefficacy, based on the physician judgment, after more than 3 months of treatment.
Results: Patients with fewer previous therapies, lower body mass index, older at start of therapy, and with previous history of good responses to therapy are more likely to have positive results of therapy. However, the predictive characteristics of models are poor.
Conclusion: Predictive models of clinical response to systemic drugs in psoriasis with the studied variables do not seem to outperform drug selection by a dermatologist.