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Enregistrement W3020602513 · doi:10.1093/rheumatology/keaa111.250

P257 Real-world evidence of TNF inhibition in axial spondyloarthritis: can we generalise the results from clinical trials?

2020· article· en· W3020602513 sur OpenAlexaff
Gareth T. Jones, Linda E. Dean, Ejaz Pathan, Gary J. Macfarlane

Notice bibliographique

RevueLara D. Veeken · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueSpondyloarthritis Studies and Treatments
Établissements canadiensToronto Western Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineBASDAIBASFIAnkylosing spondylitisClinical trialPhysical therapyPlaceboInternal medicinePopulationRheumatologyRandomized controlled trialDiseaseAlternative medicinePathology

Résumé

récupéré en direct d'OpenAlex

Abstract Background The development and utility of management guidelines assumes that clinical trial findings are generalisable. Seldom is data available to test this. We aimed to determine, in the British Society for Rheumatology Biologics Register for Ankylosing Spondylitis (BSRBR-AS), the proportion of patients commencing TNF inhibition (TNFi) that would/would not have been eligible for clinical trials that led to TNFi treatment guidelines, and whether treatment response differed between the trials and this real-world population. Methods Biologic-naïve spondyloarthritis patients were recruited from across Great Britain. Data was obtained from clinical records, and participants completed postal questionnaires. Participant characteristics were extracted from the placebo-controlled randomised trials in the NICE Health Technology Assessment: TNF-alpha inhibitors for ankylosing spondylitis and non-radiographic axial spondyloarthritis (TA383). Descriptive statistics were used to examine differences, including treatment response (ASAS-20), between BSRBR-AS participants who would/would not have been eligible for the clinical trials, and the trial participants. Results 816/2420 (34%) BSRBR-AS participants were commencing TNFi. They were younger (mean age 44 versus 50yrs) with shorter disease duration (15 versus 22yrs), more active disease (BASDAI 6.4 versus 4.0), and poorer function (BASFI 6.2 versus 3.8). Fourteen clinical trials were identified. Compared to trial populations, fewer BSRBR-AS participants were male (67% versus 71%; difference: -4.1% (95%CI: -7.8%, -0.4%)) and fewer were HLA-B27 positive (76% versus 82%; difference: -6.6% (-10.6%, -2.6%)). BSRBR-AS participants were 6yrs older than trial participants, with longer symptom duration. They reported similar disease activity (BASDAI: 6.4 versus 6.2; difference 0.2 (-0.3, 0.7)), although significantly poorer function (BASFI: 6.2 versus 5.1; difference 1.1 (0.5, 1.8)) and spinal mobility (BASMI: 4.2 versus 3.3; difference 1.0 (0.8, 1.1)). Only 333 (41%) of BSRBR-AS participants commencing TNFi would have been eligible for any of the relevant trials. Ten trials reported ASAS20 response criteria, and 864/1401 participants reported a positive treatment response (61.7%). Follow-up data was available for 318 (39%) BSRBR-AS participants, of whom 163 (51.3%) achieved an ASAS20 treatment response (difference: 10.4% (4.4%, 16.5%)). There was no difference in ASAS20 response between those who would/would not have been eligible for clinical trials (50% versus 52%; difference 2.0% (-9.4%, 13.4%)). Conclusion In this real-world population, although the likelihood of meeting response criteria was unrelated to factors determining trial eligibility, the proportion of patients responding to TNFi was lower than in the clinical trial literature. Could this be explained by selection bias? Although fewer BSRBR-AS participants provided follow-up data than in the clinical trials, to account for the observed difference participants lost to follow-up would have to be one-third more likely to achieve ASAS20 response than those who provided follow-up data. We believe this is unlikely. These findings have important implications for the generalisability of trial results, and also for the cost-effectiveness of TNFi agents. Disclosures G.T. Jones: Grants/research support; GTJ is/was a grant holder for research funded by Pfizer, AbbVie, UCB and Celgene., GTJ is/was involved in research that received financial support from Novartis. L.E. Dean: Grants/research support; LED is/was involved in research that received financial support from Pfizer, AbbVie, UCB and Novartis. E. Pathan: None. G.J. Macfarlane: Grants/research support; GJM is/was a grant holder for research funded by Pfizer, AbbVie, UCB and Celgene., GJM is/was involved in research that received financial support from Novartis.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,294
score de la tête « metaresearch » (Gemma)0,665
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,706
Score d'incertitude au seuil0,870

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,2940,665
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0080,009
Bibliométrie0,0070,008
Études des sciences et des technologies0,0010,007
Communication savante0,0120,009
Science ouverte0,0070,006
Intégrité de la recherche0,0100,009
Charge utile insuffisante (le modèle a refusé de juger)0,0180,004

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.

Tête enseignante Opus0,157
Tête enseignante GPT0,391
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomaineMéthodes
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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