Efficacy of guselkumab, a selective IL-23 inhibitor, in Preventing Arthritis in a Multicentre Psoriasis At-Risk cohort (PAMPA): protocol of a randomised, double-blind, placebo controlled multicentre trial

Introduction Psoriatic arthritis (PsA) is a complex, immune-mediated disease associated with skin psoriasis that, if left untreated, can lead to joint destruction. Up to 30% of patients with psoriasis progress to PsA. In most cases, psoriasis precedes synovio-entheseal inflammation by an average of 5–7 years, providing a unique opportunity for early and potentially preventive intervention in a susceptible and identifiable population. Guselkumab is an effective IL-23p19 inhibitor Food and Drug Administration (FDA)-approved for treatment of moderate-to-severe psoriasis and PsA. The Preventing Arthritis in a Multicentre Psoriasis At-Risk cohort (PAMPA) study aims to evaluate the efficacy of guselkumab in preventing PsA and decreasing musculoskeletal power Doppler ultrasound (PDUS) abnormalities in a population of patients with psoriasis who are at-increased risk for PsA progression.Methods and analysis The PAMPA study is a multicentre, randomised, double-blind, placebo-controlled, interventional, preventive trial comparing PDUS involvement and conversion to PsA in patients with psoriasis at-increased risk for progression treated with guselkumab compared with non-biological standard of care. The study includes a screening period, a double-blind treatment period (24 weeks) and an open-label follow-up period (72 weeks). At baseline, 200 subjects will be randomised (1:1) to receive either guselkumab 100 mg (arm 1) or placebo switching to guselkumab 100 mg starting at week 24 (arm 2). Arm 3 will follow 150 at-risk psoriasis patients who decline biological therapy and randomisation. Changes from baseline in the PDUS score at week 24 and the difference in proportion of patients transitioning to PsA at 96 weeks will be examined as the coprimary endpoints.Ethics and dissemination Ethics approval for this study was granted by the coordinating centre’s (NYU School of Medicine) Institutional Review Board (IRB). Each participating site received approval through their own IRBs. The findings will be shared in peer-reviewed articles and scientific conference presentations.Trial registration number NCT05004727

Clinical validation of digital assessment tools and machine learning models for remote measurement of psoriasis and psoriatic arthritis: a proof-of-concept study

Objective Psoricatic disease remains underdiagnosed and undertreated. We developed and validated a suite of novel, smartphone sensor-based assessments that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease. Methods Participants with psoriasis, psoriatic arthritis, or healthy controls were recruited between June 5, 2019, and November 10, 2021, at two academic medical centers. Concordance and accuracy of digital measures and image-based machine learning models were compared to their analogous clinical measures from trained rheumatologists and dermatologists. Results Of 104 study participants, 51 (49%) were female and 53 (51%) were male, with a mean age of 42.3 years (SD: 12.6). Seventy-nine (76%) participants had psoriatic arthritis, 16 (15.4%) had psoriasis and 9 (8.7%) were healthy controls. Digital patient assessment of percent body surface area (BSA) affected with psoriasis demonstrated very strong concordance (CCC = 0.94, [95%CI = 0.91–0.96]) with physician-assessed BSA. The in-clinic and remote target-lesion Physician Global Assessments showed fair to moderate concordance (CCC erythema =0.72 [0.59–0.85]; CCC induration =0.72 [0.62–0.82]; CCC scaling =0.60 [0.48–0.72]). Machine learning models of hand photos taken by patients accurately identified clinically-diagnosed nail psoriasis with an accuracy of 0.76. The Digital Jar Open assessment categorized physician-assessed upper extremity involvement, considering joint tenderness or enthesitis (AUROC = 0.68 (0.47–0.85)). Conclusion The Psorcast digital assessments achieved significant clinical validity, although they require further validation in larger cohorts before use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and freely available.info:eu-repo/semantics/publishe