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Enregistrement W2979407471 · doi:10.1182/blood.v126.23.1138.1138

North American Cooperative Group Members' Patterns of Blood Products Transfusion for Patients with Acute Leukemia

2015· article· en· W2979407471 sur OpenAlexaboutno aff
Alexander B. Pine, Eun‐Ju Lee, Mikkael A. Sekeres, David P. Steensma, Thomas Prébet, Amy E. DeZern, Rami S. Komrokji, Mark R. Litzow, Selina M. Luger, Richard M. Stone, Harry P. Erba, Guillermo Garcia‐Manero, Alfred Ian Lee, Nikolai A. Podoltsev, Lisa Barbarotta, Jeanne E. Hendrickson, Steven D. Gore, Amer M. Zeidan

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueNeutropenia and Cancer Infections
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineAcute leukemiaInternal medicinePopulationLeukemiaBlood transfusionClinical trialCancer

Résumé

récupéré en direct d'OpenAlex

Abstract Background. Transfusion of blood products is an integral component of the management of patients with acute leukemias. However, high-quality evidence and clinical trial data to support specific practices of blood product transfusion in this population are limited. We hypothesized that there is wide variation in blood product transfusion practices among providers in North America who manage patients with acute leukemia. Methods. A 30-question web-based survey about transfusion practices was emailed to members of the Eastern Cooperative Oncology Group (ECOG)-ACRIN Cancer Research Group, Alliance for Clinical Trials in Oncology (Alliance), and the Southwest Oncology Group (SWOG), and the Cancer Trials Support Unit (CTSU) on 7/6/2015 with 4 subsequent weekly reminders. The distribution list included 9,859 recipients, of whom at least 741 were providers who treated patients with acute leukemia. Responses were anonymous, and the survey distribution was approved by the respective cooperative group chairs. Descriptive statistics were used to analyze the data. Results. Of 254 responses received, 113 were excluded as they were returned by recipients not directly treating patients with acute leukemia. Another 30 responses were excluded due to incomplete data, leaving 111 responses (43.7%) eligible for the primary analysis. Of those, 109 responders were from North America representing 83 institutions in 33 states and the province of Ontario. Eighty-four of the included responders (75.7%) were physicians, and 44 (39.6%) were females. Median age of responders was 44 years (range, 26-76). A hemoglobin (Hb) level of ≤7 g/dL was the most commonly used threshold (44%) for red blood cell (RBCs) transfusions to asymptomatic stable hospitalized patients, followed closely by 8 g/dL (38%) (Figure 1). In the outpatient setting, the most commonly reported threshold was 8 gm/dL (49%) (Figure 1). Most providers reported that they "always" use leukocyte-reduced (93%) and irradiated (79%) RBCs. A platelet level of 10,000/µL was the threshold for platelet transfusions in the stable non-bleeding hospitalized patients cited by the majority of responders (76%) (Figure 2). This platelet level remained the most common threshold for platelet transfusions for non-bleeding patients in the outpatient setting (49%), although 31% of responders reported a higher threshold of 20,000/µL. A preference for single-donor apheresis platelets was reported by 81% respondents, and 57% use these products exclusively. Most providers (70%) reported always using irradiated platelets. With respect to cryoprecipitate and plasma infusions, nearly half of the respondents reported a threshold fibrinogen level of 100 mg/dL as a trigger for administering a product to a stable non-bleeding patient (Figure 3). The most commonly reported platelet threshold for performing a bone marrow biopsy (BMB) was 10,000/μL (69% of responders), followed by 20,000 (21%), 50,000 (6%), and 30,000 (4%). Therapeutic anticoagulation was held by most responders (80%) before performing a BMB. Most responders (73%) reported a platelet level of 50,000/µL as the lowest level for performing a lumbar puncture (LP) without prophylactic platelet transfusion. Conclusions. This survey demonstrates wide variability in blood product transfusion patterns among providers who treat patients with acute leukemias. A platelet level of 10,000/µL is the most common trigger for platelet transfusions for stable non-bleeding patients in both inpatient and outpatient settings. This, along with a platelet threshold of 50,000/µL for performing an LP, appear to be most widely accepted practices. Our findings emphasize the need to obtain high-quality data to develop consensus evidence-based guidelines for transfusion practices in acute leukemia with the goal of limiting unnecessary transfusions without compromising patient outcomes. Figure 1. The most common reported hemoglobin level thresholds for red blood cell transfusions in the inpatient and outpatient settings. Figure 1. The most common reported hemoglobin level thresholds for red blood cell transfusions in the inpatient and outpatient settings. Figure 2. The most common reported platelet level thresholds for platelet transfusions in the inpatient and outpatient settings. Figure 2. The most common reported platelet level thresholds for platelet transfusions in the inpatient and outpatient settings. Figure 3. The most common reported fibrinogen level thresholds for cryoprecipitate or plasma transfusions in the inpatient and outpatient settings. Figure 3. The most common reported fibrinogen level thresholds for cryoprecipitate or plasma transfusions in the inpatient and outpatient settings. Disclosures Sekeres: TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Steensma:Onconova: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Celgene: Consultancy. Prebet:CELGENE: Research Funding. Komrokji:Celgene: Consultancy, Research Funding; Incite: Consultancy; Novartis: Speakers Bureau; GSK: Research Funding. Stone:Merck: Consultancy; AROG: Consultancy; Celgene: Consultancy; Sunesis: Consultancy, Other: DSMB for clinical trial; Abbvie: Consultancy; Novartis: Research Funding; Juno: Consultancy; Amgen: Consultancy; Roche/Genetech: Consultancy; Celator: Consultancy; Agios: Consultancy; Karyopharm: Consultancy; Pfizer: Consultancy. Erba:GlycoMimetics; Janssen: Other: Data Safety & Monitoring Committees; Sunesis; Pfizer; Daiichi Sankyo; Ariad: Consultancy; Millennium/Takeda; Celator; Astellas: Research Funding; Seattle Genetics; Amgen: Consultancy, Research Funding; Novartis; Incyte; Celgene: Consultancy, Patents & Royalties. Barbarotta:Celgene, BMS, Novartis: Speakers Bureau. Gore:Celgene: Consultancy, Honoraria, Research Funding.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,033
Score d'incertitude au seuil0,066

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,011
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,011
Tête enseignante GPT0,243
Écart entre enseignants0,231 · 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 source (Gemma direct ou Codex distillé), 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

Citations1
Publié2015
Routes d'admission1
Résumé présentoui

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