POS1069 SYSTEMATIC LITERATURE REVIEW INFORMING THE EULAR POINTS TO CONSIDER TASK FORCE ON THE INITIATION OF TARGETED THERAPIES IN PATIENTS WITH INFLAMMATORY ARTHRITIDES AND A HISTORY OF CANCER
Notice bibliographique
Résumé
<h3>Background</h3> Potential associations between targeted therapies in patients with an inflammatory arthritis (IA) and malignancy are a frequent concern in daily rheumatology practice. No specific framework has been proposed to evaluate the benefit/risk balance of initiating or reinitiating a targeted therapy (bDMARDs/tsDMARDs) in patients with IA and a history of cancer. <h3>Objectives</h3> To perform a systematic literature review (SLR) to inform the task force formulating. the EULAR Points to Consider on the initiation of targeted therapies in patients. with IA and a history of cancer. <h3>Methods</h3> Specific research points were defined with the task force before formulating. the research questions with a librarian under supervision of two methodologists. The task force agreed to focus the SLR on clinical data in patients treated with any targeted therapy for an inflammatory or autoimmune rheumatic or skin or bowel disease. All studies up to. the 15th July 2022 were searched through Pubmed and Embase. Inclusion criteria required studies reporting on the initiation of a targeted therapy in patients with history of cancer, a control group. of patients treated with a conventional DMARDs or healthy controls, and report of a relative risk measure (e.g. Hazard Ratio (HR)) of cancer recurrence between groups. Two reviewers independently performed standardized article selection, data extraction, synthesis, and risk of bias assessment. The quality of the studies was graded according to the Newcastle-Ottawa quality assessment scale. <h3>Results</h3> A total of 1555 publications were identified of which 79 articles fulfilled inclusion criteria, including. 13 published articles and 1 EULAR abstract. All studies were high quality observational data from cohorts or registries, representing 4522 patients (13030 patient-years). Most of the patients included were treated for rheumatoid arthritis. The previous cancer was a solid cancer for more than 90% of the patients. The targeted therapy evaluated was a TNF inhibitor in all the studies, and 4 studies evaluated rituximab as well. The overall HR of cancer recurrence was 1.02 (0.83-1.26) in patients treated with a targeted therapy compared to those treated with a conventional DMARD (Figure 1). In patients treated with. a TNF-inhibitor, the HR was 1.01 (0.86-1.18). In patients treated with rituximab, the HR. was 1.10 (0.72-1.67). In subgroup analyses, no difference in cancer recurrence was observed if. the targeted therapy was initiated before or after 5 years since the diagnosis of the initial cancer; no difference in cancer recurrence was observed depending on the initial cancer type. <h3>Conclusion</h3> The SLR informing EULAR PTC show that overall, the targeted therapies and clinical context covered by the included studies were not associated with an increased risk of cancer recurrence when compared with conventional synthetic DMARDs. This SLR also shows the lack of data for other targeted therapies, for other clinical contexts, and for other conditions than RA. <h3>REFERENCES:</h3> NIL. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Eden Sebbag: None declared, Juan Molina Collada: None declared, Kim Lauper: None declared, Daniel Aletaha: None declared, Johan Askling: None declared, Karolina Benesova: None declared, Heidi Bertheussen: None declared, Samuel Bitoun: None declared, Ertugrul Cagri Bolek: None declared, Gerd Rüdiger Burmester: None declared, Helena Canhão: None declared, Katerina Chatzidionysiou: None declared, Jeffrey Curtis: None declared, François-Xavier Danlos: None declared, vera guimaraes: None declared, Merete Lund Hetland: None declared, Florenzo Iannone: None declared, Marie Kostine: None declared, Tue Wenzel Kragstrup Speakers bureau: Pfizer, Bristol-Myers Squibb, Eli Lilly, Novartis, UCB, and Abbvie, Consultant of: Bristol-Myers Squibb, UCB, Gilead, and Eli-Lilly, Tore K. Kvien Speakers bureau: Grünenthal, Sandoz, UCB, Consultant of: AbbVie, Amgen, Celltrion, Gilead, Novartis, Pfizer, Sandoz, UCB, Grant/research support from: AbbVie, Amgen, BMS, Galapagos, Novartis, Pfizer, UCB, Anne Regierer: None declared, Hendrik Schulze-Koops: None declared, Lucía Silva-Fernández Speakers bureau: Novartis, MSD, Sanofi, Janssen, Pfizer, Consultant of: Lilly, BMS, Abbvie, Novartis, Janssen., Zoltan Szekanecz: None declared, Maya H Buch: None declared, Axel Finckh Speakers bureau: AbbVie, BMS, Pfizer, Eli-Lilly, Sandoz, Consultant of: AbbVie, Novartis, Pfizer, MSD, Lilly, Grant/research support from: AbbVie, BMS, Galapagos, Lilly, Pfize, Jacques-Eric Gottenberg Consultant of: Abbvie, BMS, Galapagos, Gilead, Jannsen, Lilly, Roche Chugai, Sanofi, Pfizer, UCB, Grant/research support from: Abbvie, BMS, Pfizer.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».