Affiliation:
1. St. John’s, Newfoundland and Labrador, Memorial University of Newfoundland, Newfoundland, Canada
2. Eli Lilly and Company, Indianapolis, IN, USA
3. CorEvitas LLC, Waltham, MA, USA
4. University of Cincinnati, Cincinnati, OH, USA
5. Central Dermatology, St. Louis, MO, USA
Abstract
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 [R2adj], 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 (R2adj = .72, RMSE = 2.93) and only marginally worse than models including additional variables (R2adj 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.