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Record 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 on 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

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePerspective (graphical)Quality of life (healthcare)DiseaseAssociation (psychology)ImmunologySystemic lupus erythematosusIntensive care medicineInternal medicineNursing

Abstract

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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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.049
GPT teacher head0.375
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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