E065 Disagreement between patient and physician global assessment over time in psoriatic arthritis: insight into treatment priorities
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».