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Enregistrement W2550310472 · doi:10.1182/blood.v118.21.206.206

Venous Thromboembolism (VTE) Prevention with Semuloparin in Cancer Patients Initiating Chemotherapy: Benefit-Risk Assessment by VTE Risk in SAVE-ONCO

2011· article· en· W2550310472 sur OpenAlexaff
Daniel J. George, Giancarlo Agnelli, Ajay K. Kakkar, Michael R. Lassen, Patrick Mismetti, Patrick Mouret, Francesca Lawson, Alexander G.G. Turpie

Notice bibliographique

RevueBlood · 2011
Typearticle
Langueen
DomaineMedicine
ThématiqueCancer Treatment and Pharmacology
Établissements canadiensMcMaster UniversityMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineChemotherapyCancerPulmonary embolismDeep veinChemotherapy regimenSurgeryGastroenterologyThrombosisOncology

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 206 Background: Cancer patients receiving chemotherapy are at increased risk for VTE. Recent oncology guidelines emphasize the need for randomized studies with VTE risk assessment in these patients (Streiff MB, et al. JNCCN. 2011;9:714–777). Semuloparin is a new ultra-low-molecular-weight heparin with high anti-factor Xa and minimal anti-factor IIa activities. The SAVE-ONCO study investigated semuloparin vs placebo for VTE prevention in cancer patients receiving chemotherapy. Methods: Patients with metastatic or locally advanced cancer of lung, pancreas, stomach, colon-rectum, bladder or ovary initiating a chemotherapy course, were randomized to once-daily subcutaneous semuloparin 20 mg or placebo until change of chemotherapy. The primary efficacy outcome was a composite of symptomatic deep-vein thrombosis, any non-fatal pulmonary embolism, or VTE-related death. The main safety outcome was clinically relevant bleeding (major and non major). Baseline VTE risk was assessed by a score specifically developed and validated in chemotherapy-treated cancer patients (Khorana AA, et al. Blood. 2008;111:4902–7). According to this predictive model a score of 2 was assigned to very high-risk cancer sites (pancreatic or gastric), a score of 1 was assigned to high-risk cancer sites (lung, ovarian, or bladder cancer) and 1 is added to the score for each of the following parameters: platelet count ≥350 × 109/L, hemoglobin <10 g/dL and/or use of erythropoietin-stimulating agents, leukocyte count >11 × 109/L, and body mass index ≥35 kg/m2. Results: Among the 3212 patients randomized, the majority had lung (36.6%) or colorectal (28.9%) cancer and approximately two-thirds had metastatic cancer. In total, 550 (17.4%) of patients enrolled were at high risk of VTE, 1998 (63.2%) were at moderate risk, and 614 (19.4%) were at low risk (VTE risk score of ≥ 3, 1–2, or 0 points, respectively). All risk groups were well balanced between the treatment groups. Median treatment duration was approximately 3.5 months. Overall, semuloparin significantly reduced VTE or VTE-related death by 64% (p<0.0001; Table) vs placebo. The treatment effect was consistent across various levels of VTE risk (interaction p-value=0.6048; Table). Clinically relevant bleeding occurred in 2.8% and 2.0% of the patients in the semuloparin and placebo groups, respectively (Table). The incidence of major bleeding was similar: 1.2% and 1.1% patients in the semuloparin and placebo groups, respectively (hazard ratio [HR] 1.05; 95% confidence interval [CI] 0.55–1.99). No increased incidence of clinically relevant bleeding was observed with semuloparin vs placebo across various levels of VTE risk (interaction p-value=0.9409; Table). Conclusions: In cancer patients receiving chemotherapy, thromboprophylaxis with semuloparin was consistently associated with a favorable benefit-risk profile across various levels of VTE risk, but greatest in moderate to high risk patients. Antithrombotic prophylaxis should be considered in patients with cancer receiving chemotherapy, particularly in those who are at moderate to high risk of VTE. Disclosures: George: Viamet: Consultancy, Research Funding; Sanofi: Consultancy, Speakers Bureau; Pfizer: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau; Medivation: Consultancy; Janssen: Consultancy, Research Funding, Speakers Bureau; Ipsen: Consultancy, Research Funding; Genentech/Roche: Consultancy, Speakers Bureau; Dendreon: Consultancy, Research Funding, Speakers Bureau; Bayer: Consultancy; Astellas: Consultancy; GSK: Research Funding, Speakers Bureau; BMS: Research Funding; Exelixis: Research Funding. Agnelli:GlaxoSmithKline: Honoraria; Boehringer Ingelheim: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; sanofi-aventis: Honoraria. Fisher:Boehringer Ingelheim: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; sanofi-aventis: Honoraria, Research Funding. Kakkar:Bayer HealthCare: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; sanofi-aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Eisai: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; ARYx Therapeutics: Consultancy; Canyon: Consultancy; GlaxoSmithKline: Honoraria. Lassen:Astellas Pharma Europe: Consultancy; Bayer HealthCare AG: Consultancy; Bristol-Myers Squibb: Consultancy; Boehringer Ingelheim: Consultancy; GlaxoSmithKline: Consultancy; Merck Serono: Consultancy; Pfizer: Consultancy; Protola Pharma: Consultancy; sanofi-aventis: Consultancy. Mismetti:sanofi-aventis: served as a member of Steering Committees. Mouret:Bayer HealthCare: Consultancy, Honoraria; sanofi-aventis: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria. Lawson:Sanofi: Employment. Turpie:Astellas Pharma Europe: Consultancy; Bayer HealthCare AG: Consultancy; Portola Pharma: Consultancy; sanofi-aventis: 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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,308
Écart entre enseignants0,289 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
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

Citations54
Publié2011
Routes d'admission1
Résumé présentoui

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