Development of Psoriasis Assessment Tools Among Patients in the CorEvitas Psoriasis Registry.

Background: Dermatologists would benefit from an easy to use psoriasis severity assessment tool in the clinic.

Objective: To develop psoriasis assessment tools to predict PASI and Dermatology Life Quality Index (DLQI) using simple measures typically collected in clinical practice.

Methods: Data included 33 605 dermatology visits among plaque psoriasis patients enrolled in the CorEvitas Psoriasis Registry (4/15/15-7/11/20). Performance (adjusted coefficient of determination [R² _adj], root mean square error [RMSE]) in predicting PASI and DLQI was assessed for 16 different linear regression models (specified a priori based on combinations of BSA, Investigator's Global Assessment [IGA], itch, skin pain, patient global assessment, age, sex, BMI, comorbidity index, prior biologic use), and 2 stepwise selection models and 1 elastic net model based on 56 available variables. For each prediction model, concordance (sensitivity, specificity) of predicted PASI75, PASI90 and DLQI 0/1 with observed values was evaluated.

Results: Mean (SD) age, BSA, and PASI were 51 (14) years, 6 (11), and 4 (6), respectively; 46% were women, and 87% were biologic experienced. A model predicting PASI using BSA plus IGA performed best among a priori specified models (R² _adj = .72, RMSE = 2.93) and only marginally worse than models including additional variables (R² _adj range .64-.74, RMSE range 2.82-3.36). Models including IGA had the best concordance between predicted and observed PASI75 (sensitivity range 83-85%, specificity range 88-91%) and PASI90 (sensitivity range 76-82%, specificity range 94-98%). DLQI prediction was limited.

Conclusion: An assessment tool for psoriasis including BSA and IGA may be an ideal option to predict PASI in a clinic setting.