Development of Psoriasis Assessment Tools Among Patients in the CorEvitas Psoriasis Registry. Issue 2 (April 2023)

• Record Type: Journal Article
• Authors: Gulliver, Wayne P.; See, Kyoungah; Zhu, Baojin; Konicek, Bruce W.; Harrison, Ryan W.; McLean, Robert R.; Kerti, Samantha J.; Burge, Russel T.; Leonardi, Craig L.
• Is Part Of: Journal of psoriasis and psoriatic arthritis. Volume 8:Issue 2(2023)
• Page Start: 74
• Page End: 82
• Publication Date: 2023-04
• Subjects: psoriasis -- PASI -- DLQI -- patient-reported outcome measures -- psoriasis assessment tool; Psoriasis -- Periodicals; Psoriasis -- Treatment -- Periodicals; Psoriatic arthritis -- Periodicals; Psoriatic arthritis -- Treatment -- Periodicals; Electronic journals; 616.722
• DOI: 10.1177/24755303231155118
• Languages: English
• ISSNs: 2475-5303

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 [R 2 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 (R 2 adj = .72, RMSE = 2.93) and only marginally worse than models including additional variables (R 2 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.