Efficacy and Safety of Tinzaparin in CAT Patients with Hematological Malignancy
Notice bibliographique
Résumé
Abstract Introduction In patients with hematological cancers, the high risk of bleeding raises serious concerns when anticoagulant therapy is initiated for treatment of acute venous thromboembolism (VTE). In the CATCH trial, we showed that tinzaparin is associated with a significantly lower risk of clinically relevant bleeding (CRB) and clinically relevant non-major bleeding (CRNMB) compared with warfarin therapy in patients with a solid tumor or hematological cancer. Hence, we performed a post-hoc analysis to assess the risk of recurrent VTE (rVTE) and bleeding in the hematological cancer patient subgroup. Risk factors associated with rVTE and bleeding were also explored. Methods CATCH (ClinicalTrials.govNCT01130025; Lee A et al. JAMA 2015) was a Phase III, multinational, randomized, active-controlled, open-label trial. Patients with solid tumors or hematological malignancies were randomized to receive therapeutic dosing of tinzaparin (175 IU/kg OD) vs warfarin (INR target 2.0 - 3.0) for treatment of acute VTE. Treatment was given for up to 6 months but was withheld during periods of severe thrombocytopenia (platelet count < 50 x 10 9/L). The primary efficacy outcome was the composite of symptomatic deep vein thrombosis (DVT), symptomatic nonfatal pulmonary embolism (PE), fatal PE, incidental proximal DVT and incidental proximal PE. All thrombosis and bleeding outcomes, including rPE, rDVT, major bleeding (MB), CRB, and CRNMB were objectively documented and centrally adjudicated in a treatment-blinded fashion. Patients with myeloma, lymphoma or leukemia were included in this sub-group analysis. Treatment effect was assessed by means of a cox-regression with time to first event as outcome and deaths not due to fatal PE as a competing risk factor. The cumulative incidence functions and the corresponding 95% CIs were estimated. Results 94 of 900 subjects (10.4%) in CATCH had a hematological cancer. Of these patients, 44 and 50 received tinzaparin and warfarin, respectively. The most common hematological cancer was lymphoma (59.6%), followed by myeloma (30.9%), chronic leukemia (5.3%) and acute leukemia (4.3%). rVTE occurred in fewer patients assigned to tinzaparin versus warfarin (2.4% vs 6.4%) but this difference was not statistically significant (HR 0.36; 95%CI 0.04-3.14). All recurrent thrombotic events were PE; no rDVT occurred. Also, trends for less bleeding with tinzaparin were noted for MB (tinzaparin 0.0% versus warfarin 5.0%), CRB (tinzaparin 10.4% vs warfarin 23.5%; HR 0.53; 95%CI 0.15-1.83) and CRNMB (tinzaparin 10.4% vs warfarin 19.5%; HR 0.69; 95%CI 0.19-2.51). 3 patients in each treatment arm had a fatal bleeding event and there was no difference in overall mortality (tinzaparin 22.7% vs warfarin 22.0%). There were no risk factors associated with rVTE identified. Factors associated with CRB in patients with hematological cancer included the use of antiplatelet agents (HR 7.63; 95%CI 1.17-49.6; p=0.033) and renal insufficiency (HR 12.4; 95%CI 2.15-72.0; p=0.005). Conclusion In this post-hoc subgroup analysis of patients with hematological cancers who received anticoagulation for acute VTE, tinzaparin may be a more effective and safer alternative to warfarin. Randomized controlled trials are warranted to test this hypothesis in this challenging population with a high risk of bleeding. Figure 1 Figure 1. Disclosures Lee: Leo Pharma: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Servier: Consultancy, Honoraria. Bauersachs: Bayer: Honoraria, Speakers Bureau; BMS: Honoraria, Speakers Bureau; LEO Pharma: Honoraria, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau. Janus: LEO Pharma: Current Employment. Leeder: LEO Pharma: Current Employment. Thoning: LEO Pharma: Current Employment. Khorana: Anthos: Consultancy, Honoraria; Halozyme: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Bristol Myers Squibb: Consultancy, Honoraria.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».