Alternative tumour necrosis factor inhibitors (TNFi) or abatacept or rituximab following failure of initial TNFi in rheumatoid arthritis: the SWITCH RCT
Notice bibliographique
Résumé
BACKGROUND: Rheumatoid arthritis (RA), the most common autoimmune disease in the UK, is a chronic systemic inflammatory arthritis that affects 0.8% of the UK population. OBJECTIVES: To determine whether or not an alternative class of biologic disease-modifying antirheumatic drugs (bDMARDs) are comparable to rituximab in terms of efficacy and safety outcomes in patients with RA in whom initial tumour necrosis factor inhibitor (TNFi) bDMARD and methotrexate (MTX) therapy failed because of inefficacy. DESIGN: Multicentre, Phase III, open-label, parallel-group, three-arm, non-inferiority randomised controlled trial comparing the clinical and cost-effectiveness of alternative TNFi and abatacept with that of rituximab (and background MTX therapy). Eligible consenting patients were randomised in a 1 : 1 : 1 ratio using minimisation incorporating a random element. Minimisation factors were centre, disease duration, non-response category and seropositive/seronegative status. SETTING: UK outpatient rheumatology departments. PARTICIPANTS: Patients aged ≥ 18 years who were diagnosed with RA and were receiving MTX, but had not responded to two or more conventional synthetic disease-modifying antirheumatic drug therapies and had shown an inadequate treatment response to a first TNFi. INTERVENTIONS: Alternative TNFi, abatacept or rituximab (and continued background MTX). MAIN OUTCOME MEASURES: The primary outcome was absolute reduction in the Disease Activity Score of 28 joints (DAS28) at 24 weeks post randomisation. Secondary outcome measures over 48 weeks were additional measures of disease activity, quality of life, cost-effectiveness, radiographic measures, safety and toxicity. LIMITATIONS: Owing to third-party contractual issues, commissioning challenges delaying centre set-up and thus slower than expected recruitment, the funders terminated the trial early. RESULTS: = 40). The numbers, as specified, were analysed in each group [in line with the intention-to-treat (ITT) principle]. Comparing alternative TNFi with rituximab, the difference in mean reduction in DAS28 at 24 weeks post randomisation was 0.3 [95% confidence interval (CI) -0.45 to 1.05] in the ITT patient population and -0.58 (95% CI -1.72 to 0.55) in the per protocol (PP) population. Corresponding results for the abatacept and rituximab comparison were 0.04 (95% CI -0.72 to 0.79) in the ITT population and -0.15 (95% CI -1.27 to 0.98) in the PP population. General improvement in the Health Assessment Questionnaire Disability Index, Rheumatoid Arthritis Quality of Life and the patients' general health was apparent over time, with no notable differences between treatment groups. There was a marked initial improvement in the patients' global assessment of pain and arthritis at 12 weeks across all three treatment groups. Switching to alternative TNFi may be cost-effective compared with rituximab [incremental cost-effectiveness ratio (ICER) £5332.02 per quality-adjusted life-year gained]; however, switching to abatacept compared with switching to alternative TNFi is unlikely to be cost-effective (ICER £253,967.96), but there was substantial uncertainty in the decisions. The value of information analysis indicated that further research would be highly valuable to the NHS. Ten serious adverse events in nine patients were reported; none were suspected unexpected serious adverse reactions. Two patients died and 10 experienced toxicity. FUTURE WORK: The results will add to the randomised evidence base and could be included in future meta-analyses. CONCLUSIONS: How to manage first-line TNFi treatment failures remains unresolved. Had the trial recruited to target, more credible evidence on whether or not either of the interventions were non-inferior to rituximab may have been provided, although this remains speculative. TRIAL REGISTRATION: Current Controlled Trials ISRCTN89222125 and ClinicalTrials.gov NCT01295151. FUNDING: ; Vol. 22, No. 34. See the NIHR Journals Library website for further project information.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».