AB0970 GREAT DIVERSITY IN B/TSDMARD TREATMENT IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS AROUND THE WORLD AND THE COMPARISONS WITH THE 2022 UPDATED ASAS-EULAR RECOMMENDATIONS
Notice bibliographique
Résumé
Background The 2022 updated ASAS-EULAR recommendations for the management of axial spondyloarthritis (SpA) uses the Ankylosing Spondylitis Disease Activity Scores(ASDAS) ≥2.1 as the single disease activity index indicated for the using of b/tDMARDs. Unlike the care of rheumatoid arthritis, there is a diversity in the disease activity criteria for b/tsDMARDs in axSpA worldwide, which is worthy of note. Objectives This study aimed to investigate the diversity of b/tsDMARDs using in axSpA care worldwide in the light of the 2022 ASAS-EULAR recommendations for SpA, by comparing the population fulfilled each disease activity criteria indicated for b/tsDMARDs. Methods Biologic-naïve patients fulfilled the ASAS axSpA criteria were recruited in the rheumatology clinic in the Shuang-Ho Hospital from 2018 to 2020. In each visit, disease activity including BASDAI, ASDAS-CRP, ESR and CRP were recorded and the fulfillment of different disease activity criteria indicated for b/tsDMARDs were labelled, including the latest two versions of ASAS-EULAR recommendations and the reimbursement criteria of Australia, Singapore, Korea, Hong Kong, Canada and Taiwan. The data having the highest disease activity of each patient were selected for analysis, and the complete data including every visit recorded were also analyzed as sensitivity test. The population size fulfilled different activity criteria were compared. Results This study recruited 396 biologic-naive patients with axSpA with 1390 disease activity data totally. The mean age was 41 years, and among the cohort, 129(32.6%) were female. In the 396 disease activity measures, there were 262(66.2%) patients met the disease activity criteria for b/tsDMARDs according to the 2022 ASAS-EULAR recommendations, which was more than twice the population (121) indicated by the activity criteria of the prior version. In comparison with the 2022 recommendations, the disease activity criteria of many countries are stricter leading to less b/tsDMARDs using. Using the population indicated by the 2022 recommendations as reference, the rate of patients eligible by the disease activity criteria in different countries varied greatly, which ranged from as low as 8.40% in Taiwan to 100% in Singapore. The result was consistent in analyzing the whole 1390 dataset. Conclusion There is a great expansion of the population indicated for b/tsDMARDs brought by the 2022 update, in comparison with the 2016 version. There is a great diversity for b/tsDMARDs using in axSpA care around the world. In some countries, a surprisingly huge disparity in b/tsDMARDs using was noted. Only less than 10% patients indicated for b/tsDMARDs using by the 2022 ASAS-EULAR recommendation met the reimbursement criteria in Taiwan, analyzed in the single measure setting. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests None Declared.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».