E065 Disagreement between patient and physician global assessment over time in psoriatic arthritis: insight into treatment priorities
Bibliographic record
Abstract
Abstract Background/Aims The psoriatic arthritis (PsA) core domain set developed by the outcome measures in rheumatology working group includes musculoskeletal disease, fatigue, physical function, and structural damage, of which arthritis activity, pain, and fatigue were identified as essential by both patients (Pts) and physicians (Phs). Assessing agreement between Pt and Ph global assessments (GA) may provide valuable insight into differential importance of specific PsA manifestations to Pts vs Phs. Although previous studies have assessed Pt/Ph disagreement, they have not evaluated potential variation over time. This research sought to assess agreement of PtGA and PhGA through week (W) 24 and identify factors driving disagreement between PtGA and PhGA using pooled data (N = 1120) from the phase 3 DISCOVER (D)-1 & -2 studies of the fully human IL-23p19 subunit inhibitor (i), guselkumab (GUS). Methods Pts with active PsA despite standard therapies (D1: ≥3 swollen/tender joint counts [SJC/TJC], CRP ≥0.3 mg/dL, ∼30% with prior TNFi; D2: ≥5 SJC/TJC, CRP ≥0.6 mg/dL, biologic-naïve) were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, Q8W; or placebo. Pt/Ph agreement was defined as a difference of -15 PhGA) among pts with PtGA/PhGA disagreement were assessed with the same logistic regression model considering pt demographics, disease characteristics, and pt-reported outcomes (PROs). The effect of GUS on disease parameters identified as determinants of PtGA vs PhGA disagreement was assessed with repeated measures mixed models adjusting for treatment group, baseline (BL) levels, prior TNFi use, and BL DMARD use. Results At BL, mean (SD) SJC=11.5 (7.4), TJC=20.6 (13.3), FACIT-Fatigue score=29.9 (10.0), PtGA=66.9 (19.9), and PhGA=64.8 (15.9) were consistent with moderate to high disease activity. Agreement between PtGA and PhGA was seen in most instances (61.2%); 23.2% of cases were characterized by PtGA>PhGA and 15.7% by PhGA>PtGA. The proportion of pts with PtGA>PhGA increased to 39.1% at W24, while that with PhGA>PtGA decreased to 11.2%. The main determinant of PtGA>PhGA was higher Pt Pain (all time points); additional factors included worse physical health-related quality of life at BL and worse fatigue at W24. Conversely, Phs emphasized objective disease measures, namely higher SJC (all time points) and TJC (W8 to W24), and elevated CRP (BL to W16). GUS treatment was associated with prompt and sustained significant improvements in all identified determinants, including those driving PtGA>PhGA. Conclusion PtGA and PhGA were aligned in most encounters. PtGA>PhGA disagreement was driven by pain, fatigue, and physical health being weighed more by Pts than Phs. These findings have important implications in shared decision making and highlight the need to prioritize treatments addressing the full spectrum of PsA symptoms, including PROs. Disclosure W. Tillett: Consultancies; AbbVie; Amgen; Eli Lilly; Janssen; MSD; Novartis; Pfizer and UCB. Member of speakers’ bureau; Abbvie; Amgen; Eli Lilly; Janssen; MSD; Novartis; Pfizer and UCB. Grants/research support; AbbVie; Amgen; Eli Lilly; Janssen and UCB. P. Rahman: Consultancies; AbbVie; Amgen; Bristol Myers Squibb; Celgene; Eli Lilly; Janssen; MSD; Novartis; Pfizer and UCB. Grants/research support; Janssen and Novartis. L.C. Coates: Consultancies; AbbVie; Amgen; Boehringer Inelheim; Bristol Myers Squibb; Celgene; Eli Lilly; Gilead; Galapagos; Janssen; Novartis; Pfizer and UCB. Member of speakers’ bureau; AbbVie; Amgen; Biogen; Celgene; Eli Lilly; Galapagos; Gilead; Janssen; Medac; Novartis; Pfizer and UCB. Grants/research support; AbbVie; Amgen; Celgene; Eli Lilly; Janssen; Novartis; Pfizer and UCB. P. Nash: Grants/research support; AbbVie; Boehringer Ingelheim; Bristol Myers Squibb; Celgene; Eli Lilly; Gilead; Janssen; Pfizer; Novartis; Roche; Sandoz; and Sun Pharmaceutical Industries. A. Deodhar: Consultancies; AbbVie; Amgen; Aurinia; Bristol Myers Squibb; Celgene; Eli Lilly; GlaxoSmithKline; Janssen; MoonLake; Novartis; Pfizer and UCB. Member of speakers’ bureau; AbbVie; Eli Lilly; Janssen; Novartis; Pfizer and UCB. Grants/research support; AbbVie; Eli Lilly; GlaxoSmithKline; Novartis; Pfizer and UCB. F. Nantel: Consultancies; Janssen. Shareholder/stock ownership; Johnson & Johnson. E. Rampakakis: Corporate appointments; Employee of JSS Medical Research. Consultancies; Janssen. L. Bessette: Consultancies; AbbVie; Amgen; Bristol Myers Squibb; Eli Lilly; Fresenius Kabi; Gilead; Janssen; MSD; Novartis; Pfizer; Sandoz; Sanofi; Teva and UCB. Member of speakers’ bureau; AbbVie; Amgen; Bristol Myers Squibb; Eli Lilly; Fresenius Kabi; Janssen; MSD; Novartis; Pfizer; Sandoz; Sanofi; Teva; and UCB. Grants/research support; AbbVie; Amgen; Bristol Myers Squibb; Celgene; Eli Lilly; Janssen; MSD; Novartis; Pfizer; Sanofi and UCB. M. Marrache: Corporate appointments; Employee of Janssen Inc. Toronto, Cananda. Shareholder/stock ownership; Johnson & Johnson. F. Lavie: Corporate appointments; Employee of Immunology Global Medical Affairs, Janssen Pharmaceutical Companies of Johnson & Johnson. Shareholder/stock ownership; Johnson & Johnson. M. Shawi: Corporate appointments; Employee of Immunology Global Medical Affairs, Janssen Pharmaceutical Companies of Johnson & Johnson. Shareholder/stock ownership; Johnson & Johnson.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".