POS1044 EFFECT OF SECUKINUMAB VERSUS ADALIMUMAB ON ACR CORE COMPONENTS AND HEALTH-RELATED QUALITY OF LIFE IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS FROM THE EXCEED STUDY
Bibliographic record
Abstract
<h3>Background:</h3> EXCEED (NCT02745080) was the first fully blinded head-to-head trial to evaluate the efficacy and safety of secukinumab (SEC) versus (vs) adalimumab (ADA) monotherapy in patients with active psoriatic arthritis (PsA) with a primary endpoint of American College of Rheumatology (ACR) 20 at Week 52. Although SEC narrowly missed statistical significance for superiority vs ADA, numerically higher response for other musculoskeletal endpoints and composite indices were observed with SEC.<sup>1</sup> <h3>Objectives:</h3> To explore the effect of SEC and ADA on ACR core components, function and Health-related Quality of Life (HRQoL) outcomes. <h3>Methods:</h3> Patients were randomised 1:1 to receive SEC 300 mg (N=426) subcutaneous (s.c.) at baseline, Week 1-4, followed by every 4 weeks until Week 48 or ADA 40 mg (N=427) s.c. at baseline followed by same dosing every 2 weeks until Week 50. The primary, key secondary and some exploratory endpoints at Week 52 were previously reported.<sup>1</sup> A supportive analysis for ACR50 response using logistic regression model and trimmed means model for Health Assessment Questionnaire-Disability Index (HAQ-DI) with gender and smoking status as factors was performed to adjust for imbalances in baseline characteristics. An exploratory analysis of ACR core components with SEC vs ADA at Week 52 was conducted using a mixed-effects repeated measures model that included tender and swollen joint counts, patient and physician global assessment, PsA pain (VAS) and erythrocyte sedimentation rate. HRQoL variables were also exploratory and assessed based on Short Form Health Survey Physical/Mental Component Summary (SF-36 PCS/MCS) scores and Dermatology Life Quality Index (DLQI). <h3>Results:</h3> The demographic and baseline disease characteristics were comparable across treatment groups, except for an imbalance in sex (females: 51.2% vs 46.4%) and smoking status (yes: 21.8% vs 17.8%) in SEC and ADA group, respectively. At Week 52, ACR50 responses were 49.0% and 44.8% (<i>P</i>=0.0929) and HAQ-DI mean change from baseline were −0.69 and −0.58 (<i>P</i>=0.0314) in SEC and ADA treatment groups, respectively after adjusting for gender and smoking status. No major difference across ACR core components was observed in both treatment groups at Week 52 (Table 1). At Week 52, SEC presented similar improvement in SF-36 PCS/MCS score and numerically higher improvement in DLQI compared to ADA (Figure 1). <h3>Conclusion:</h3> Secukinumab provided similar improvements in ACR core components and SF-36 based quality of life at Week 52 with adalimumab. Greater improvement in HAQ-DI response and DLQI was demonstrated with secukinumab compared to adalimumab. <h3>References:</h3> [1]McInnes IB, et al. <i>Lancet</i>. 2020; 395:1496–505. <h3>Disclosure of Interests:</h3> Philippe Goupille Speakers bureau: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Janssen, Eli Lilly, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Consultant of: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Janssen, Eli Lilly, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Grant/research support from: AbbVie, Amgen, Biogen, BMS, Celgene, Chugai, Janssen, Eli Lilly, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi and UCB, Frank Behrens Paid instructor for: Eli Lilly, Consultant of: Pfizer, AbbVie, Sanofi, Eli Lilly, Novartis, Genzyme, Boehringer Ingelheim, Janssen, MSD, Celgene, Roche and Chugai, Grant/research support from: Pfizer, Janssen, Chugai, Celgene and Roche, Laura C Coates Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Biogen, BMS, Celgene, Domain, Eli Lilly, Gilead, GSK, Janssen, Medac, Novartis, Pfizer, Serac and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Jordi Gratacos-Masmitja Speakers bureau: AbbVie, Amgen, BMS, Celgene, Janssen, Eli Lilly, Novartis and Pfizer, Consultant of: AbbVie, Amgen, BMS, Celgene, Janssen, Eli Lilly, Novartis and Pfizer, Grant/research support from: AbbVie, Amgen, BMS, Celgene, Janssen, Eli Lilly, Novartis and Pfizer, Philip J Mease Speakers bureau: AbbVie, Amgen, Genentech, Janssen, Eli Lilly, Merck, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Boehringer Ingelheim, Galapagos, Celgene, Genentech, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Galapagos, Genentech, Gilead, Janssen, Eli Lilly, Merck, Novartis, Pfizer, SUN Pharma, and UCB, Dafna D Gladman Consultant of: Amgen, AbbVie, BMS, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Amgen, AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Peter Nash Speakers bureau: Novartis, Abbvie, Roche, Pfizer, BMS, Janssen, Celgene, UCB, Eli Lilly, MSD, Sanofi, Gilead, Consultant of: Novartis, Abbvie, Roche, Pfizer, BMS, Janssen, Celgene, UCB, Eli Lilly, MSD, Sanofi, Gilead, Grant/research support from: Novartis, Abbvie, Roche, Pfizer, BMS, Janssen, Celgene, UCB, Eli Lilly, MSD, Sanofi, Gilead, Arthur Kavanaugh Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, and UCB, Ruvie Martin Shareholder of: Novartis, Employee of: Novartis, Weibin Bao Shareholder of: Novartis, Employee of: Novartis, Corine Gaillez Shareholder of: Novartis and BMS, Employee of: Novartis, Iain McInnes Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Novartis, Pfizer, and UCB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".