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Enregistrement W4411413842 · doi:10.1016/j.ard.2025.06.1898

ABS0880 A REAL-WORLD PERSPECTIVE ON THE ASSOCIATION OF IMPROVEMENT IN HEALTH-RELATED QUALITY OF LIFE OUTCOMES AND DISEASE ACTIVITY IN SYSTEMIC LUPUS ERYTHEMATOSUS

2025· article· en· W4411413842 sur OpenAlexaffabout
Pankti Mehta, Polina Katz, V. Strand, Fadi Kharouf, Laura García, Qixuan Li, A. Askanase, Christopher D. Saffore, Denise Kruzikas, Dafna D. Gladman, Zahi Touma

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Lupus Erythematosus Research
Établissements canadiensUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicinePerspective (graphical)Quality of life (healthcare)DiseaseAssociation (psychology)ImmunologySystemic lupus erythematosusIntensive care medicineInternal medicineNursing

Résumé

récupéré en direct d'OpenAlex

Background: Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease associated with substantial morbidity and mortality. Patient-reported outcome (PRO) measures are particularly valuable in SLE as they assess health-related quality of life (HRQoL) and capture disease aspects that may not be fully reflected by conventional disease activity measures, such as features of Type 2 SLE. Objectives: This study aimed to examine the association between clinically meaningful improvements in HRQoL scores, assessed using the Short Form 36 (SF-36), and disease activity, measured by SLEDAI-2K, in patients with SLE in a real-world clinical setting. Methods: A retrospective analysis was conducted using prospectively collected data from SLE patients followed at a single center in Toronto, Canada. Clinical and laboratory data were collected every 3 to 6 months, while SF-36 was administered annually. Patients with active disease (SLEDAI-2K ≥ 6) from 2005 (marking the advent of mycophenolate mofetil use) to 2024, with baseline and one-year follow-up SF-36 data, were included. Minimum clinically important differences (MCID) in SF-36 scores were defined as increases in SF-36 Physical (PCS) and Mental Component Summary (MCS) scores by ≥2.5, individual domain scores by ≥5, and minimum important difference (MID) for SLEDAI-2K as a decrease by ≥4. Associations between improvements in SF-36 (two summary and eight domain scores) and SLEDAI-2K responses at one year were analyzed using chi-square tests. To study the absolute change in SLEDAI-2K, two regression models examined least square mean differences (LSM) in SLEDAI-2K scores from baseline to one year for PCS and MCS score responders versus non-responders at one year. Impact of PCS and MCS score response vs. non-response on SLEDAI-2K over follow-up (one, two, three and five years) was studied using two separate Linear Mixed Models (LMM). Results: A total of 247 patients were included with a median age of 37.1 years (IQR 28.5–46.5) at the study visit, a female-to-male ratio of 8.8:1, and a median SLE duration from diagnosis of 9.25 years (IQR 4.39–16.07). The median SLEDAI-2K score was 8 (IQR 6–12), with common organ involvements being mucocutaneous (46.6%), renal (44.9%), and musculoskeletal (23.5%). Most patients had active serology (79.4%) and a median SDI of 1 (IQR 0–2) at the study visit. Most patients received hydroxychloroquine (83%), with mycophenolate mofetil (49.4%) being the most commonly prescribed immunosuppressant, followed by azathioprine (44.9%). Among MCS score responders, a significantly greater proportion also achieved SLEDAI-2K responses compared to MCS non-responders (52 of 101, 51.5% vs 47 of 146, 32.2%, p<0.01). This association was not observed between PCS score responders and non-responders. For individual SF-36 domains, significantly more patients who reported clinically meaningful improvements in physical function (53 of 111, 47.7 vs. 46 of 136, 33.8%, p=0.04) and mental health (45 of 86, 52.3 vs. 69 of 189, 33.5, p<0.01) domains also achieved SLEDAI-2K responses. No significant differences were reported for other domains, although there was a trend in vitality and role emotional responders to achieve SLEDAI-2K responses (Figure 1). When absolute change in SLEDAI-2K was studied, a significant difference in LSM change in SLEDAI-2K scores from baseline to one year was observed between PCS responders vs. non-responders (-5.13 and -3.09, p<0.01) as well as MCS responders vs. non-responders (-5.19 and -3.14, p<0.01). When the impact of PCS and MCS score response on SLEDAI-2K over 5 years was studied, PCS score responders had a lower SLEDAI-2K at years two [b -0.94, 95% confidence intervals (-1.45,-0.38)], three [-1.66 (-2.22,-1.10)], and five [-1.62 (-2.23,-1.00)] but not at year one as compared to PCS score non responders. In another LMM model for impact of MCS score response on SLEDAI-2K, MCS score responders had a lower SLEDAI-2K at years one [-1.1 (-1.98,-0.21)], two [-0.93 (-1.48,-0.37)], three [-1.65 (-2.22,-1.08)] and five [-1.65 (-2.21,-1.08)] compared to MCS score non responders. Conclusion: In patients with active SLE, clinically important improvements in disease activity were particularly notable among those who reported clinically meaningful improvements in MCS scores, physical function, and mental health domains of the SF-36 at one year. Patients demonstrating meaningful improvements in both MCS and PCS scores also experienced greater reductions in SLEDAI-2K scores at one year. PCS and MCS score responders consistently showed lower SLEDAI-2K values over five years as compared to non-responders. These findings indicate that improvements in some aspects of HRQoL are associated with significant reductions in disease activity in SLE patients. Figure 1Proportion of SLEDAI-2K responders and non-responders in patients reporting improvements ≥ MCID versus non-responders in SF-36 summary and domain scores.PCS- Physical Component Summary Score, MCS- Mental Component Summary Score, GH- General Health, MH-Mental Health, PF- Physical Function, BP- Bodily Pain, RP- Role Physical, SF- Social Function, RE- Role Emotional. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: Pankti Mehta: None declared, Patricia Katz: None declared, Vibeke Strand Abbvie, Alumis, Amgen Corporation, Arthrosi, Artiva, AstraZeneca, Bayer, Blackrock, BMS, Boehringer Ingelheim, Catalys, Citryll, Contura, Cullinan, Fate Therapeutics, Fortress Biotech, Gate Biosciences, Genasence, Genentech/ Roche, GSK, Inmedix, Kiniksa, Lipum, Longitude Capital, MED Institute, Novartis, R-Pharm, RAPT, Royalty Pharma, Sanofi, Scipher, Setpoint, Sobi, Spherix, Synact, Takeda, Topography, Zoe, Fadi Kharouf: None declared, Laura Whitall Garcia: None declared, Qixuan Li: None declared, Anca Askanase: None declared, Christopher D Saffore AbbVie, full-time employee of AbbVie, Denise Kruzikas AbbVie, full-time employee of AbbVie, Dafna D. Gladman AbbVie, AstraZeneca, Amgen, Eli Lilly, Janssen, GSK, Novartis, Pfizer, UCB, AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Zahi Touma: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0020,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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.

Tête enseignante Opus0,049
Tête enseignante GPT0,375
Écart entre enseignants0,326 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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