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Disease severity prediction model for patients with plaque psoriasis
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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.
References
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