Predicting the Time to Relapse Following Withdrawal from Different Biologics in Patients with Psoriasis who Responded to Therapy: A 12-Year Multicenter Cohort Study
Journal
American Journal of Clinical Dermatology
Series/Report No.
American Journal of Clinical Dermatology
Journal Volume
25
Journal Issue
6
Start Page
997-1008
ISSN
1175-0561
1179-1888
Date Issued
2024-01-01
Author(s)
Huang, Yu-Huei
Hung, Sung Jen
Lee, Chaw-Ning
Wu, Nan-Lin
Hui, Rosaline Chung-yee
et al.,
DOI
10.1007/s40257-024-00887-8
Abstract
ackground: For patients with psoriasis, discontinuation of biologics following remission has become more common in daily practice.
Objective: We aimed to identify predictors and construct a predictive model for time to relapse following withdrawal from biologics.
Methods: This 12-year, multicenter, observational cohort study was performed in six dermatology centers between February 2011 and February 2024. We identified biological treatment episodes in patients with moderate-to-severe psoriasis and included only treatment episodes in which a clinical response (≥ 50% reduction in Psoriasis Area and Severity Index score [PASI 50] from baseline) was achieved and the patient withdrew from biological therapy with a well-controlled status (PASI < 10 and ≥ 50% improvement in PASI from baseline). The primary outcome was time to relapse, which was defined as the period from the last biologic administration to relapse. An extended multivariate Cox proportional hazards analysis (Prentice-Williams-Peterson Gap time model) was used to predict relapse and generate a predictive model.
Results: This study screened 1613 biological treatment episodes, and 991 treatment episodes were enrolled. The time to relapse decreased significantly as the number of previous withdrawals from biological treatment increased (p < 0.001). Similarly, the time to relapse decreased significantly as the number of previous biologics used increased (p < 0.001). The maximum PASI improvement during biological treatment decreased and the PASI score at withdrawal of biological treatment increased in parallel as the number of prior withdrawals from biologics increased. The time to relapse following withdrawal was longest for interleukin (IL)-23 inhibitors (IL-23i), followed by the IL-12/23i, IL-17 inhibitors (IL-17i), and tumor necrosis factor-α inhibitors. After adjustment, multivariate Cox regression identified the following significant predictors of relapse following withdrawal: the mechanisms of action of biologics (hazard ratio [HR] for IL-17i vs IL-12/23i, 1.59; HR for IL-23i vs IL-12/23i, 0.60), number of previous withdrawals from biological treatment (HR 1.23; 95% confidence interval [CI] 1.13‒1.33), time to achieve PASI 50 (HR 1.01; 95% CI 1.00‒1.02), maximum PASI improvement on biologics (HR 0.98; 95% CI 0.98‒0.99), and PASI at the end of therapy (HR 1.03; 95% CI 1.01‒1.05). The model had good predictive and discriminative ability.
Conclusions: These results have the potential to help physicians and patients make individualized treatment decisions; information on the risk of relapse of psoriasis at specific timepoints following the withdrawal of biologics is particularly valuable for patients considering discontinuation of biologics or as-needed biologic therapy. However, the benefit and risk of repeated withdrawals of biologics should be carefully weighed, as the treatment efficacy and duration of remission decline as the number of withdrawals increases.
Publisher
Springer Science and Business Media LLC
Type
journal article