IMPORTANCE Identifying the optimal long-term biologic therapy for patients with psoriasis
is often done through trial and error.
OBJECTIVE To identify the optimal biologic therapy for individual patients with psoriasis
using predictive statistical and machine learning models.
DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study used data from
Danish nationwide registries, primarily DERMBIO, and included adult patients treated for
moderate-to-severe psoriasis with biologics. Data were processed and analyzed between
spring 2021 and spring 2022.
MAIN OUTCOMES AND MEASURES Patient clusters of clinical relevance were identified and
their success rates estimated for each drug. Furthermore, predictive prognostic models to
identify optimal biologic treatment at the individual level based on data from nationwide
registries were evaluated.
RESULTS Assuming a success criterion of 3 years of sustained treatment, this study included
2034 patients with a total of 3452 treatment series. Most treatment series involved male
patients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) had
finished an education longer than primary school. The average ages were 24.9 years at
psoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decision
trees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17
inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%,
and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting in
success, gradient boost and logistic regression had accuracies of 48.5% and 44.4%,
top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%.
CONCLUSIONS AND RELEVANCE Of the treatment prediction models used in this cohort study
of patients with psoriasis, gradient-boosted decision trees performed significantly better
than logistic regression when predicting specific biologic therapy (by drug as well as target)
leading to a treatment duration of at least 3 years without discontinuation. Predicting the
optimal biologic could benefit patients and clinicians by minimizing the number of failed
treatment attempts.