Management and Outcomes of Major Bleeding On Dabigatran or Warfarin
Notice bibliographique
Résumé
Abstract Abstract 19 Background. Although the use of dabigatran reduces bleeding compared to warfarin, these bleeds need to be managed when they occur. We describe the management of major bleeds and the prognosis in patients who bleed after treatment with dabigatran or warfarin. Methods. We analyzed the RE-LY trial for the use of resources and length of stay after major bleeds. Additionally, two independent investigators reviewed all reports on 1,121 major bleeds in 5 phase III long-term trials of dabigatran in atrial fibrillation, acute treatment and secondary prevention of venous thromboembolism involving 27,419 patients followed for 6 to 36 months. The effect of different blood products and factor concentrates to manage the bleeding was compared between patients on dabigatran and on warfarin. The effect was assessed as good, moderate or poor taking into account the totality of clinical data available. Results. Patients with major bleeds on dabigatran were older, had lower creatinine clearance and more frequent use of aspirin or non-steroid anti-inflammatory agents than those on warfarin. Factor concentrates were only used for 11 patients treated with dabigatran (prothrombin complex concentrate, 2; recombinant factor VIIa, 9), which was too few to determine the efficacy of these measures. Other results are shown in the table below. Patients on warfarin were transfused with red blood cells less frequently, but required more plasma transfusions and more vitamin K for bleeding management than patients on dabigatran. The difference in 30-day mortality in favor of dabigatran (univariate P=0.044) became stronger in logistic regression analysis (multivariate P=0.007). Conclusion. The prognosis after a major bleed on dabigatran was, despite lack of a specific antidote, better than with warfarin. There was also a shorter stay in intensive care with dabigatran compared to warfarin. Disclosures: Brueckmann: Boehringer Ingelheim Pharma GmbH &Co. KG: Employment. Connolly:Boehringer Ingelheim: Advisory Board Other, Consultancy, Research Funding, Speakers Bureau; Sanofi-Aventis: Advisory Board, Advisory Board Other, Consultancy, Research Funding, Speakers Bureau; Portola: Advisory Board, Advisory Board Other, Consultancy, Research Funding, Speakers Bureau; Bristol Myers Squibb: Research Funding; Merck: Advisory Board Other, Consultancy. Eikelboom:Boehringer Ingelheim: Consultancy, Research Funding, Speakers Bureau; Astra Zeneca: Consultancy, Research Funding, Speakers Bureau; Sanofi-Aventis: Consultancy, Research Funding, Speakers Bureau; GlaxoSmithKline: Consultancy, Research Funding, Speakers Bureau; Eisai Pharmaceuticals: Consultancy, Speakers Bureau; Eli Lilly: Consultancy, Speakers Bureau; McNeil: Consultancy, Speakers Bureau; Bristol-Myers Squibb: Consultancy; Corgenix Medical Corporation: Consultancy; Daiichi Sankyo: Consultancy. Ezekowitz:Boehringer Ingelheim: Advisory Board Other, Consultancy, Speakers Bureau; Astra Zeneca: Advisory Board, Advisory Board Other, Consultancy; Eisei: Advisory Board, Advisory Board Other, Consultancy; Pozen Inc: Advisory Board, Advisory Board Other, Consultancy; ARYx Therapeutics: Advisory Board Other, Consultancy; Pfizer: Advisory Board, Advisory Board Other, Consultancy; Sanofi: Advisory Board Other, Consultancy; Bristol Myers Squibb: Advisory Board, Advisory Board Other, Consultancy; Portola: Advisory Board, Advisory Board Other, Consultancy; Diachi Sanko: Advisory Board Other, Consultancy; Medtronics: Advisory Board, Advisory Board Other, Consultancy; Merck: Advisory Board, Advisory Board Other, Consultancy; Johnson & Johnson: Advisory Board, Advisory Board Other, Consultancy; Gilead: Advisory Board Other, Consultancy; Janssen Scientific Affairs: Advisory Board, Advisory Board Other, Consultancy. Wallentin:Boehringer Ingelheim: Advisory Board Other, Consultancy, Honoraria, Research Funding; Astra Zeneca: Advisory Board, Advisory Board Other, Consultancy, Honoraria, Research Funding; Glaxo Smith Kline: Advisory Board, Advisory Board Other, Consultancy, Honoraria, Research Funding; Eli Lilly: Advisory Board, Advisory Board Other, Consultancy, Honoraria; Schering-Plough: Advisory Board Other, Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Advisory Board, Advisory Board Other, Consultancy, Honoraria, Research Funding; Regardo Biosciences: Advisory Board Other, Consultancy; Athera Biosciences: Advisory Board, Advisory Board Other, Consultancy. Yusuf:Boehringer Ingelheim: Advisory Board Other, Consultancy, Honoraria, Research Funding; Astra Zeneca: Advisory Board, Advisory Board Other, Consultancy; Sanofi-Aventis: Advisory Board, Advisory Board Other, Consultancy; Bristol Myers Squibb: Advisory Board, Advisory Board Other, Consultancy. Schulman:Boehringer Ingelheim: Consultancy, Speakers Bureau; Bayer Healthcare: Consultancy, Research Funding; Sanofi-Aventis: Consultancy, Honoraria; Leo Pharma: Honoraria; GlaxoSmithKline: Consultancy; Astra Zeneca: Consultancy.
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».