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

2015· article· en· W2979407471 on 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

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcute leukemiaInternal medicinePopulationLeukemiaBlood transfusionClinical trialCancer

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.243
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2015
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