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REAL-WORLD OBSERVATIONAL STUDY OF ANIFROLUMAB IN ADULT SYSTEMIC LUPUS ERYTHEMATOSUS: SINGLE-CENTER EXPERIENCE FROM THE UNITED ARAB EMIRATES

2025· article· en· W4410513217 on OpenAlexvenueno aff
Rajaie Namas, Sarah Al Qassimi, Jawahir Alameri, Reem Alblooshi, Фатима Абдулла, Muriel Ghosn, Amir Malik, Raghda Almaashari, Fulvio Salvo, Asia Mubashir, Ahmed S. Aldhaheri, Mohamed Elarabi

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObservational studySingle CenterLupus erythematosusCenter (category theory)DermatologySystemic diseaseImmunologyInternal medicineImmunopathologyAntibody

Abstract

fetched live from OpenAlex

PV243 / #243 Poster Topic: AS24 - SLE-Treatment Background/Purpose Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multiorgan organ damage, drug-related toxicities related to prolonged treatment, and early mortality.[1] Anifrolumab, a human monoclonal antibody targeting the type I interferon receptor, was approved in 2021 for the treatment of moderate to severe SLE [2]. However, its efficacy and safety profile has not yet been described in our region. In this study, we assessed the efficacy and safety of anifrolumab in an Arab population at a tertiary care center in the United Arab Emirates. Methods Patients with SLE were identified from the hospital’s electronic database between April 2015 and October 2024. Those aged 18 years or older who were receiving anifrolumab were included in the study. Data collected encompassed sociodemographic details, clinical features, SLE organ involvement, laboratory findings, previous and current medications along with their adverse effect profiles, and disease activity indices. Serial measurements of C3, C4, dsDNA, SLEDAI-2k, CLASI-A, and CLASI-D, as well as adverse effects related to anifrolumab, were tracked over a 12-month period. Descriptive statistics were used for data analysis. Results We identified 21 patients with SLE who were treated with anifrolumab. The majority were Emirati nationals (76%) and predominantly female (86%). The mean age at SLE diagnosis was 29.4 ± 10.5 years, with a mean age of presentation to the clinic of 32.8 ± 10.5 years, and an average disease duration of 94 ± 75.2 months. Non-SLE dermatological conditions included cystic acne (3 patients), alopecia areata (2 patients), atopic dermatitis (1 patient), and seborrheic dermatitis (1 patient). The most commonly affected SLE domains were mucocutaneous (71%), musculoskeletal (67%), hematological (57%), renal (14%), and neurological (14%). Serological results showed positive ANA in 76%, anti-Smith in 43%, SSA in 62%, and SSB in 19%. Anifrolumab treatment was initiated at a mean age of 36.4 ± 11.6 years and continued for an average of 9 ± 4.9 months. Concurrent therapies included hydroxychloroquine (67%), prednisolone (29%) with a mean dose of 7 ± 3.5 mg, azathioprine (24%), and mycophenolate mofetil (24%). Two patients reported minor adverse effects in the form of upper respiratory tract infections after starting anifrolumab, but did not require discontinuation of the medication (Table 1). Table 1. Baseline laboratory values included mean C3 (91 ± 22.5 mg/dL), C4 (19 ± 10.3 mg/dL), dsDNA (65 ± 102.4 IU/mL), CRP (9 ± 16.9 mg/L), and ESR (46 ± 27.9 mm/hr). Baseline disease activity scores were SLEDAI-2k (8.9 ± 3.1), CLASI-A (11.8 ± 6.9), and CLASI-D (3.8 ± 6.5). At the 12-month follow-up, disease activity scores showed numerical improvement with SLEDAI-2k (3.7 ± 1.7), CLASI-A (3.5 ± 2.2), and CLASI-D (2.7 ± 3.3) (Figure 1). Figure 1. Conclusions Anifrolumab was well tolerated, showing consistent improvements in disease activity scores across multiple organ domains over a 12-month follow-up period.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.062
GPT teacher head0.334
Teacher spread0.272 · 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 routes1
Has abstractyes

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