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Record W4411421207 · doi:10.1016/j.ard.2025.05.707

POS0320 TREATMENT WITH ANIFROLUMAB PREVENTS LONG-TERM ORGAN DAMAGE COMPARED TO REAL-WORLD STANDARD OF CARE: FINDINGS FROM THE LASER STUDY

2025· article· en· W4411421207 on OpenAlexaffabout
Zahi Touma, Ian N Bruce, Richard Furie, Eric F. Morand, R. Tummala, Shelly Chandran, Gabriel Abreu, J. Knagenhjelm, Kellyn F Arnold, T. Mehdikhanova, Hopin Lee, Danuta Kielar, Miyoko Waratani

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsAstraZeneca (Canada)Toronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineStandard of careTerm (time)Intensive care medicineDermatologyImmunologyInternal medicine

Abstract

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Background: Patients with systemic lupus erythematosus (SLE) typically accrue irreversible organ damage due to uncontrolled disease activity, disease flares, or long-term glucocorticoid treatment. Early diagnosis and treatment with advanced therapies are key to preventing organ damage accrual. Previous analyses of data from the 1-year pivotal TULIP trials (NCT02446912 and NCT02446899) and their 3-year long-term extension (TULIP LTE, NCT02794285) have demonstrated the efficacy of anifrolumab for controlling disease activity while simultaneously reducing glucocorticoid requirements. However, high attrition in the placebo arm of TULIP LTE due to re-randomization or loss to follow-up have prevented comprehensive analysis of the efficacy of anifrolumab for preventing damage accrual. Thus, the effect of anifrolumab on organ damage accrual rates remains unknown, particularly beyond the 4 years covered by the TULIP trials. Objectives: The LASER study (Long-term Organ Damage: Anifrolumab versus Real-World Standard of Care in Adult Patients with Active Systemic Lupus Erythematosus, NCT06485674) aimed to evaluate the effectiveness of anifrolumab for preventing organ damage up to 4 years and to extrapolate time to organ damage progression up to 10 years in adult patients with moderately to severely active SLE. The study used an external control arm of patients who received real-world standard of care (RW SOC) from the University of Toronto Lupus Clinic (UTLC). Methods: Patients who initiated 300mg anifrolumab in TULIP-1 or -2 were included in the anifrolumab arm; key eligibility criteria were applied to the UTLC to create the RW SOC arm. Propensity score (PS) and censoring weights were used to account for differences in baseline patient characteristics and losses to follow-up, respectively. 14 known predictors of organ damage were included in the PS models (Figure 1), and predictors which remained imbalanced following PS weighting were included in outcome models. Organ damage was measured using the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) score. Using data observed across 4 years of follow-up in TULIP, change in SDI score from baseline was estimated using weighted linear regression and time to first SDI score increase was estimated using Cox regression. Time to first SDI score increase within 10 years was then estimated using 7 separate parametric survival models fitted to data up to 4 years and extrapolated between 4 and 10 years; the best fitting parametric model (Royston-Parmar spline) was selected according to Akaike and Bayes information criteria values. Results: 354 patients were included in the anifrolumab arm, and 561 patients were included in the RW SOC arm. Following PS weighting, baseline patient characteristics were adequately balanced (Figure 1). After accounting for differences in loss to follow-up, patients in the anifrolumab arm accrued 0.416 fewer points of organ damage (95% CI: -0.582, -0.249; p < 0.001) after 4 years and were 59.9% less likely (HR: 0.401, 95% CI: 0.213, 0.753, p=0.005) to experience an increase in organ damage within 4 years compared to patients in the RW SOC arm. Using a Royston-Parmar spline model to extrapolate between 4 and 10 years, patients in the anifrolumab arm were 49.8% less likely (HR: 0.502, 95% CI: 0.391, 0.635, p=0.002) to experience an increase in organ damage within 10 years (Figure 2). Conclusion: Patients who received anifrolumab in the TULIP trials had accumulated significantly less organ damage up to 4 years compared to patients who received RW SOC. These results were supported by novel extrapolation analyses, which suggested that anifrolumab users would accumulate less organ damage over 10 years. The results of this study therefore support the benefit of adding anifrolumab to SOC for preventing long-term organ damage in patients with moderately to severely active SLE. Figure 1Baseline covariate balance before and after propensity score weighting.Note that standardized mean differences < 0.2 (absolute value) indicate good covariate balance. SLEDAI-2K score and proteinuria were included in outcome models to further improve balance.Abbreviations: BP=blood pressure; SDI=Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index; SLE=systemic lupus erythematosus; SLEDAI-2K = Systemic Lupus Erythematosus Disease Activity Index 2000; SMRW=standardized mortality ratio weighting. Figure 2Weighted Kaplan-Meier curves for observed time to organ damage progression up to 4 years, with 10-year extrapolations overlaid.Note that the hazard ratio over 4 years was estimated from observed data using a Cox regression model; the hazard ratio over 10 years was estimated via extrapolation using a Royston-Parmar spline model.Abbreviations: CI=confidence interval; TULIP=Treatment of Uncontrolled Lupus via the Interferon Pathway; UTLC=University of Toronto Lupus Clinic. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: Zahi Touma Speaker - AstraZeneca; Novartis; UCB Biopharma, Consultant - UCB Biopharma; Merck KgaA; GSK; Sarkana Pharma Inc.; Roche, Grant/Research Support - GSK, Ian Bruce Speaker - AstraZeneca; Janssen; GSK; Novartis, Consultant - AstraZeneca; Janssen; GSK; Novartis; Takeda; BMS, Richard A. Furie Speaker - AstraZeneca; GSK, Consultant - GSK; AstraZeneca; Genentech; Biogen; BMS; Kyverna; Novartis; Merck; EMD Serono; Ventas, Grant/Research Support - GSK; AstraZeneca; Genentech; Biogen; BMS; Kyverna; Novartis, Eric Morand Speaker - AstraZeneca; EMD Serono; Roche, Shares/Stock - Dragonfly, Consultant - AstraZeneca; Biogen; BMS; EMD Serono; Takeda; GSK; Novartis; Dragonfly; Alpine, Grant/Research support - Abbvie; AstraZeneca; Biogen; BMS; EMD Serono; Eli Lilly; Janssen; Takeda; UCB; GSK; Novartis; Roche, Raj Tummala Shares/Stock - AstraZeneca, Former Employment - AstraZeneca, Shelly Chandran Shares/Stock - AstraZeneca, Current Employee - AstraZeneca Canada, Gabriel Abreu Current Employee - AstraZeneca, Jacob Knagenhjelm Shares/Stock - AstraZeneca (indirect ownership through a mutual fund), Current Employee - AstraZeneca, Kellyn Arnold Current Employee - IQVIA, Tarana Mehdikhanova: None declared. Zheyuan Yang Current Employee - IQVIA, Hopin Lee Currently Employee - IQVIA, Danuta Kielar Shares/Stock - AstraZeneca, Current Employee - AstraZeneca, Miina Waratani Shares/Stock - AstraZeneca UK, Current Employee - AstraZeneca UK. © 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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.192
GPT teacher head0.431
Teacher spread0.239 · 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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