THU0447 Measuring Outcome in Psoriatic Arthritis: Correlation between Different Skin Scores. (15th July 2016)

• Record Type: Journal Article
• Authors: Elmamoun, M.; Loftus, C.; Szentpetery, A.; Gallagher, P.; Barnes, L.; FitzGerald, O.
• Is Part Of: Annals of the rheumatic diseases. Volume 75(2016)Supplement 2
• Page Start: 353
• Page End: 353
• Publication Date: 2016-07-15
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2016-eular.2285
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Psoriatic arthritis (PsA) is a heterogeneous disease that includes features of peripheral arthritis, spondylitis, dactylitis, enthesitis, and skin and nail disease. PsA affects about 30% of patients with psoriasis (PsO). 1 Measuring outcome in psoriatic arthritis (MOPsA) is a web-based tool, freely available to use (see https://mopsa.ie ), that collects clinical information about all domains affected in PsA including skin disease and patient reported outcome measures (PROMs). MOPsA also calculates the composite psoriatic disease activity index (CPDAI) and minimal disease activity (MDA). 2, 3 Assessment of skin disease in rheumatology clinics can be challenging. Availability of patient self-assessment questionnaires that correlate with, and possibly can replace, physical examination would address this challenge. Objectives: Our aim was to correlate skin-related patient self-assessment questionnaires and skin sores assessed by physicians using MOPsA. Methods: 189 patients with PsA, fulfilling CASPAR criteria, were recruited in this study. 137 patients from rheumatology clinic and 52 patients from dermatology clinic. We calculated the following skin scores: Dermatology Quality of Life Index (DLQI), Psoriasis Symptom Inventory (PSI), Body Surface Area (BSA) and Psoriasis Area Severity Index (PASI). GraphPad Prism version 6 was used for statistical analysis. Results: Data from 189 patients with skin scores were included in this analysis. Mean ± SD age of 44.61 (± 12.54), 99 (52%) patients were males. 96 (51%) patients had nail involvement. Mean ± SD DLQI was 5.57 (± 6.93), PSI 7.36 (±8.68), BSA 7.27 (± 12.17), and PASI 4.57 (± 5.56). There was strong correlation between PASI and BSA (r=0.8502, P<0.0001) and between PASI and DLQI (r=0.7256, P<0.0001), but moderate correlation between PASI and PSI (r=0.5812, P<0.0001). There was strong correlation between BSA and DLQI (r=0.7048, P<0.0001) but moderate correlation between BSA and PSI (r=0.6246, P<0.0001 respectively). Finally, there was strong correlation between DLQI and PSI (r=0.7873, P<0.0001). Conclusions: Our analysis confirms the correlation between PASI and DLQI. The strongest correlation was between PASI and BSA. DLQI correlates very well with all three parameters (PASI, BSA and PSI). These preliminary results suggest that the combination of BSA and DLQI might work equally well to PASI and DLQI when accurately accessing skin involvement in rheumatology clinics. References: Mease PJ, et al: Prevalence of rheumatologist-diagnosed psoriatic arthritis in patients with psoriasis in European/North American dermatology clinics, J Am Acad Dermatol. 69(5):729–35, 2013 Mumtaz A, et al: Development of a preliminary composite disease activity index in psoriatic arthritis, Ann Rheum Dis. 70(2):272–7, 2011 Coates LC, et al. Defining minimal disease activity in psoriatic arthritis: a proposed objective target for treatment. Ann Rheum Dis. 69(1):48–53, 2010 Disclosure of Interest: M. Elmamoun: None declared, C. Loftus: None declared, A. Szentpetery: None declared, P. Gallagher: None declared, L. Barnes: None declared, O. FitzGerald Grant/research support from: Pfizer, AbbVie, BMS, UCB, Consultant for: Pfizer, AbbVie, Janssen, MSD, Cellgene, Novartis, Speakers bureau: Pfizer, AbbVie, Janssen, Cellgene