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Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Mia-Louise Nielsen, University of Copenhagen
  • ,
  • Troels Christian Petersen, University of Copenhagen
  • ,
  • Julia-Tatjana Maul, University of Zurich
  • ,
  • Jashin J Wu, Dermatology Research and Education Foundation, Irvine, CA, USA.
  • ,
  • Mads Kirchheiner Rasmussen, DERMBIO
  • ,
  • Trine Bertelsen
  • Kawa Khaled Ajgeiy, DERMBIO, University of Southern Denmark
  • ,
  • Lone Skov, DERMBIO, University of Copenhagen
  • ,
  • Simon Francis Thomsen, University of Copenhagen
  • ,
  • Jacob P Thyssen, University of Copenhagen
  • ,
  • Alexander Egeberg, DERMBIO, University of Copenhagen

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.

Original languageEnglish
JournalJAMA Dermatology
Pages (from-to)1149-1156
Number of pages8
Publication statusPublished - Oct 2022

Bibliographical note

Publisher Copyright:
© 2022 American Medical Association. All rights reserved.

    Research areas

  • Adult, Biological Products/therapeutic use, Biological Therapy, Cohort Studies, Humans, Interleukin-17, Middle Aged, Psoriasis/drug therapy

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