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Record W3096249124 · doi:10.1182/blood-2020-141154

Red Blood Cell Transfusion and the Use of Intravenous Iron in Iron Deficient Patients Presenting to the Emergency Department

2020· article· en· W3096249124 on OpenAlexaffabout
Arvand Barghi, Robert Balshaw, Emily Rimmer, Murdoch Leeies, Allan Garland, Brett L. Houston, Donald S. Houston, Ryan Zarychanski

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsCancerCare ManitobaGeorge & Fay Yee Centre for Healthcare InnovationUniversity of Manitoba
Fundersnot available
KeywordsMedicineMean corpuscular volumeEmergency departmentAnemiaTransferrin saturationFerritinHemoglobinIron-deficiency anemiaBlood transfusionIncidence (geometry)Retrospective cohort studyPopulationInternal medicineIron deficiencySickle cell anemiaPediatrics

Abstract

fetched live from OpenAlex

Background: Red blood cell (RBC) transfusions are often used to treat patients with iron deficiency who present to the emergency department (ED) with symptomatic anemia. Intravenous (IV) iron is the preferred treatment in this setting, as it has been shown to increase hemoglobin concentration rapidly and durably. We aim to determine the incidence of iron deficiency anemia (IDA) and the management of these patients in the ED setting. Objectives: To evaluate the incidence of IDA, the frequency of RBC transfusion and iron supplementation, and factors associated with RBC transfusion. Study Design: Retrospective cohort study of all adult patients presenting to the St Boniface Hospital (Winnipeg, CAN) ED from January 2014 to January 2019. Methods: We used electronic data from the Emergency Department Information System (EDIS) and Laboratory Information Services (LIS) databases to identify patients presenting with IDA, defined as anemia (hemoglobin <120 g/L) with either a transferrin saturation less than 20% or ferritin less than 30 umol/L, or mean corpuscular volume (MCV) of < 75 fL. A ferritin greater than 100 umol was used to exclude IDA, regardless of MCV. We extracted patient demographics, diagnoses, markers of iron storage, RBC transfusion and use of IV iron. Multivariate logistic regression analysis was used to evaluate factors associated with RBC transfusion. Results: Of 39222 patients, 17945 (45%) were anemic. Of the anemic patients, iron parameters were ordered in 1848 (10.3%) patients, and IDA was present in 910 (5.1 %). In the IDA population, 95 patients (10.4 %) received 1 RBC unit, and 197 patients (21.6 %) received 2 or more units. Oral iron and IV iron were prescribed for 64 (7 %) and 14 (1.5 %) patients, respectively. Our logistic regression model demonstrated that hemoglobin concentration was the main determinant of whether patients received RBC transfusion. Other variables including patient age, cardiac symptoms, heart rate, blood pressure, and CTAS score were not associated with increased likelihood of receiving RBC transfusion. Conclusion: Iron parameters were infrequently ordered in the evaluation of anemia in the ED, with limited use of oral and IV iron. The decision to transfuse RBCs was primarily influenced by hemoglobin concentration, but not other surrogates of hemodynamic instability. An interventional study to improve education and access to oral and IV iron is planned to reduce unnecessary RBC transfusions and their associated risks in patients with IDA. Disclosures No relevant conflicts of interest to declare.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes2
Has abstractyes

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