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2024-11-15T20:06:47.000Z

Disease severity prediction model for patients with plaque psoriasis

Nov 15, 2024
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Learning objective: After reading this article, learners will be able to cite a new clinical development in psoriasis.


Identifying independent risk factors associated with severe plaque psoriasis is essential for the development of effective treatment strategies and improving outcomes.1 A multicenter study retrospectively analyzed data from 2,109 patients with plaque psoriasis from 12 hospitals in China to develop a severity risk prediction model. The LASSO logistic regression was used for multivariable analysis to identify independent risk factors and develop a predictive nomogram for PASI ≥10. Results from this study were published in Scientific Reports by Wang et al.1


Key learnings
The Nomogram-10 prediction model incorporates 10 key risk factors associated with severe plaque psoriasis, including DELPHI consensus dichotomy, DLQI, BSA, age, sex, weight, career, scalp involvement, facial involvement, and arthropathy.
In internal validation, the C-index was 0.863 (95% CI, 0.848–0.879), which was affirmed by bootstrap = 1,000 validation (C-index, 0.860; 95% CI, 0.836–0.885).
The model was also validated using two external cohorts (n = 198 and n = 193), with a C-index of 0.910 (95% CI, 0.868–0.953) and 0.951 (95% CI, 0.924–0.977) in Cohort 1 and Cohort 2, respectively, indicating good discrimination.
The Nomogram-10 model developed in this study offers an efficient and easily applicable predictive model to assess the risk of moderate-to-severe plaque psoriasis and can be used to develop personalized treatment strategies based on individual patients' risk.

Abbreviations: BSA, body surface area; C-index, concordance index; CI, confidence interval; DLQI, Dermatology Life Quality Index; LASSO, least absolute shrinkage and selection operator; PASI, Psoriasis Area and Severity Index. 

  1. Wang H, Shi J, Hou S, et al. A large-scale retrospective study in China explores risk factors for disease severity in plaque psoriasis. Sci Rep. 2024;28;14(1):25749. DOI: 1038/s41598-024-73408-6

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