Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study
Background Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.Objective To develop and validate a personalized prediction model for psoriasis relapse follo...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Journal of Dermatological Treatment |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09546634.2025.2480743 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849345247747768320 |
|---|---|
| author | Shan Huang Yanping Bai Ruozhou Qi Hongda Yu Xingwu Duan |
| author_facet | Shan Huang Yanping Bai Ruozhou Qi Hongda Yu Xingwu Duan |
| author_sort | Shan Huang |
| collection | DOAJ |
| description | Background Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.Objective To develop and validate a personalized prediction model for psoriasis relapse following the discontinuation of biologics.Methods This study enrolled patients who achieved remission following biologic therapy. Relapse predictors were identified using the Boruta algorithm combined with multivariate Cox regression. A nomogram and an online calculator were created to aid in the visualization and computation of outcomes. The model’s performance was thoroughly assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), C-statistics, calibration plots, and Decision Curve Analysis (DCA).Results The study included 597 patients, with 534 in the derivation cohort and 63 in the validation cohort. Anxiety, disease duration, prior biologic treatments, treatment duration, time to achieve PASI 75, and maximum PASI response were identified as influential factors for relapse and were incorporated into the model. Both internal and external evaluations indicate that the model exhibits good predictive accuracy.Conclusion A multivariate model leveraging standard clinical data can relatively accurately predict the risk of psoriasis relapse post-biologic discontinuation, guiding personalized treatment strategies. |
| format | Article |
| id | doaj-art-3a87f76f652145aebc90c12fb84717b7 |
| institution | Kabale University |
| issn | 0954-6634 1471-1753 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Dermatological Treatment |
| spelling | doaj-art-3a87f76f652145aebc90c12fb84717b72025-08-20T03:42:30ZengTaylor & Francis GroupJournal of Dermatological Treatment0954-66341471-17532025-12-0136110.1080/09546634.2025.2480743Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort studyShan Huang0Yanping Bai1Ruozhou Qi2Hongda Yu3Xingwu Duan4Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Chinese and Western Medicine, Beijing, ChinaDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Chinese and Western Medicine, Beijing, ChinaDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaBackground Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.Objective To develop and validate a personalized prediction model for psoriasis relapse following the discontinuation of biologics.Methods This study enrolled patients who achieved remission following biologic therapy. Relapse predictors were identified using the Boruta algorithm combined with multivariate Cox regression. A nomogram and an online calculator were created to aid in the visualization and computation of outcomes. The model’s performance was thoroughly assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), C-statistics, calibration plots, and Decision Curve Analysis (DCA).Results The study included 597 patients, with 534 in the derivation cohort and 63 in the validation cohort. Anxiety, disease duration, prior biologic treatments, treatment duration, time to achieve PASI 75, and maximum PASI response were identified as influential factors for relapse and were incorporated into the model. Both internal and external evaluations indicate that the model exhibits good predictive accuracy.Conclusion A multivariate model leveraging standard clinical data can relatively accurately predict the risk of psoriasis relapse post-biologic discontinuation, guiding personalized treatment strategies.https://www.tandfonline.com/doi/10.1080/09546634.2025.2480743Psoriasisbiologicsrelapseprediction modelmachine learning |
| spellingShingle | Shan Huang Yanping Bai Ruozhou Qi Hongda Yu Xingwu Duan Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study Journal of Dermatological Treatment Psoriasis biologics relapse prediction model machine learning |
| title | Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study |
| title_full | Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study |
| title_fullStr | Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study |
| title_full_unstemmed | Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study |
| title_short | Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study |
| title_sort | personalized prediction of psoriasis relapse post biologic discontinuation a machine learning driven population cohort study |
| topic | Psoriasis biologics relapse prediction model machine learning |
| url | https://www.tandfonline.com/doi/10.1080/09546634.2025.2480743 |
| work_keys_str_mv | AT shanhuang personalizedpredictionofpsoriasisrelapsepostbiologicdiscontinuationamachinelearningdrivenpopulationcohortstudy AT yanpingbai personalizedpredictionofpsoriasisrelapsepostbiologicdiscontinuationamachinelearningdrivenpopulationcohortstudy AT ruozhouqi personalizedpredictionofpsoriasisrelapsepostbiologicdiscontinuationamachinelearningdrivenpopulationcohortstudy AT hongdayu personalizedpredictionofpsoriasisrelapsepostbiologicdiscontinuationamachinelearningdrivenpopulationcohortstudy AT xingwuduan personalizedpredictionofpsoriasisrelapsepostbiologicdiscontinuationamachinelearningdrivenpopulationcohortstudy |