MétaCan
Menu
Back to cohort

Survey of Red Cell Transfusion Patterns in Patients with Chronic Bone Marrow Failure Disorders in Northern Alberta, Canada

2015· article· en· W2575981235 on OpenAlexaffabout
Jameel Abdulrehman, Susan Nahirniak, Nancy Zhu

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicHematological disorders and diagnostics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineBone marrowBone marrow failurePopulationMyeloid leukemiaAnemiaInternal medicineSurgeryHaematopoiesisStem cellBiology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Patients with chronic bone marrow failure syndromes often become dependent on recurrent red blood cell (RBC) transfusions (txn). Given the lack of standard RBC txn guidelines in this population, txn practices can be quite variable. Our aim is to study the pattern of RBC txns in chronically transfused patients with bone marrow failure syndromes in our jurisdiction to better understand Hemoglobin (Hb) triggers and volumes transfused. METHODS A chronic txn database containing information regarding all patients receiving a RBC txn at least once every two months for a minimum of six consecutive months between January 1, 2011 and December 31, 2014 exists for all 13 sites within the Edmonton Zone in Alberta, Canada. This database was queried for all adult patients (age > 18) with chronic bone marrow failure syndromes. We defined bone marrow failure as the intrinsic inability of the bone marrow to produce adequate amounts of blood cells, but not due to acute leukemia or non-myeloid malignancies. Patients were excluded if bone marrow failure was not the primary indication for the RBC txns. The data elements extracted included median pre-txn Hb, median number of RBCs transfused per episode, and location of txn. Data was inserted into an EXCEL spreadsheet for analysis. RESULTS We found 126 patients in the chronic transfusion database with bone marrow failure syndromes, who received RBCs between January 1, 2011 and December 31, 2014. We removed 10 patients with concurrent acute leukemia or non-myeloid malignancies, 7 with additional non-bone marrow causes for cytopenias, 4 with non-hematologicalmalignancies, and 3 who were <18 years of age. We included 102 out of 126 patients, 65 with myelodysplastic syndrome, 22 with myeloproliferative neoplasm, 9 with aplastic anemia, 5 with chronic myelomonocytic leukemia and 4 with pure red cell aplasia. Three patients had combined diagnoses. Mean age at time of txn was 73.9 years(range 23 to 99) and 66.72% were male. Medical therapies used included erythropoietin stimulating agents (29), iron chelation therapy (28), Azacitidine (17), Hydroxyurea (17), and stem cell transplantation (5). In total, there were 5889 units of RBC transfused, 3809 (65%) at the Hematology based tertiary care facility (HEM), 1025 (17%) in non-Hematology tertiary centers (Non-HEM), and 1055 (18%) in community based facilities (COM). The differences in pre-txn Hbs based on site are in Table 1. Over time, normalization across sites has occurred. Over the 4 years, the mean total units of RBCs transfused per patient was 57.74 (range 13 to 297). The median quantity of units of RBCs transfused per transfusion event was 2 in HEM and non-HEM, compared to 3 in COM. There were no changes in the medians through the years. CONCLUSIONS Pre-tx Hb remained stable in tertiary centers, but started higher in community centers and trended downward becoming similar to tertiary centers by 2014. However, the number of units of blood given at each transfusion event remains higher in community centers compared to tertiary centers. Table 1. Median Pre-Transfusion Hemoglobin (g/L) by Region Over Time 2011 2012 2013 2014 Community 85.5 85 83 80 Non Hem 80 79.5 77 78 Hematology 80 81 81 80 ALL 81 81 81 80 Disclosures Zhu: Celgene Canada: Membership on an entity's Board of Directors or advisory committees; Novartis Canada: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees.

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 score0.131
Threshold uncertainty score0.363

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBloodSame topicHematological disorders and diagnosticsFrench-language works237,207