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Published on: April 8, 2016
Development and validation of a prediction model for primary non-response to IL-17A inhibitors in psoriasis
Qian Yu1,2, Yuxiong Jiang1,2, Yifan Hu1,2
1Department of Dermatology, Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai, China.
Background:
Interleukin-17A (IL-17A) inhibitors have revolutionized the treatment of moderate-to-severe plaque psoriasis, but a substantial proportion of patients experience primary non-response during the early treatment phase.
Objectives:
To identify clinical predictors of primary non-response to IL-17A inhibitors and to develop and validate a prediction model for individualized risk assessment.
Methods:
A total of 617 patients initiating IL-17A inhibitors in the Shanghai Psoriasis Effectiveness Evaluation Cohort (SPEECH) were randomly allocated into a training set (n = 370) and an internal validation set (n = 247) in a 6:4 ratio. An external validation cohort included 511 psoriasis patients receiving IL-17A inhibitor across 26 hospitals in China. Primary non-response was defined as failure to achieve ≥75% improvement in the Psoriasis Area and Severity Index (PASI75) at week 12. Logistic regression was used to identify independent predictors, and a multivariable model was developed and validated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
Results:
In the training cohort, 23.2% of patients met the definition of primary non-response. Clinical predictors included higher BMI, absence of family history of psoriasis, prior biologic exposure, and higher baseline PASI score. In addition to these clinical factors, failure to achieve PASI40 at week 4 was a strong indicator of subsequent non-response. The final prediction model exhibited strong discrimination (AUC 0.766, 95% CI, 0.690-0.842), good calibration between predicted and observed probabilities, and meaningful clinical usefulness as demonstrated by DCA. Performance remained robust in both the internal validation cohort (AUC 0.706) and the external validation cohort (AUC 0.708). An online calculator was developed to facilitate individualized risk estimation.
Conclusion:
This study identified key baseline and early-response predictors of primary non-response to IL-17A inhibitors and established a validated prediction model with clinical utility. The web-based tool may support precision decision-making in psoriasis management by enabling early identification of high-risk patients.
