All content on this site is intended for healthcare professionals only. By acknowledging this message and accessing the information on this website you are confirming that you are a healthcare professional.
The PsOPsA Hub uses cookies on this website. They help us give you the best online experience. By continuing to use our website without changing your cookie settings, you agree to our use of cookies in accordance with our updated Cookie Policy
Introducing
Now you can personalise
your PsOPsA Hub experience!
Bookmark content to read later
Select your specific areas of interest
View content recommended for you
Find out moreThe PsOPsA Hub website uses a third-party service provided by Google that dynamically translates web content. Translations are machine generated, so may not be an exact or complete translation, and the PsOPsA Hub cannot guarantee the accuracy of translated content. The PsOPsA Hub and its employees will not be liable for any direct, indirect, or consequential damages (even if foreseeable) resulting from use of the Google Translate feature. For further support with Google Translate, visit Google Translate Help.
The PsOPsA Hub is supported by educational grants. All educational content is developed independently by SES in collaboration with our expert steering committee, with no input or influence from financial supporters. We would like to express our gratitude to the following companies for their support: • UCB: For website development, launch, and ongoing maintenance. • UCB and Bristol Myers Squibb: For educational content and news updates.
Disease severity prediction model for patients with plaque 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.
- 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
More about...
Your opinion matters
11 votes - 45 days left ...
Related articles
Newsletter
Subscribe to get the best content related to Psoriasis and Psoriatic Arthritis delivered to your inbox