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Are Children with Cancer Receiving Myelosuppressive Therapy At Risk for Transfusion-Related Iron Overload?

2012· article· en· W2552817076 on OpenAlexaff
Rebecca Barty, Erin Jamula, Grace Wang, Yang Liu, Nancy M. Heddle, Uma H. Athale

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineTransfusion therapyBlood transfusionPopulationChelation therapyPediatricsFerritinAnemiaIntensive care medicineEmergency medicineThalassemiaSurgeryInternal medicine

Abstract

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Abstract Abstract 1180 Background Transfusion-related iron overload (TRIO) is associated with significant morbidity and mortality. Adequate screening for iron overload (IO) and the use of iron chelators, if needed, should be employed for chronically transfused individuals. However, with the exception of patients with hemoglobinopathies, screening for iron overload is not a consistent part of routine care for patients receiving multiple red cell transfusions, and is not identified as a treatable problem. Objective This study aimed to identify the population at risk for TRIO and to evaluate current screening practices. Methods All children (≤ 18 years) receiving at least one red blood cell (RBC) transfusion between January 1, 2008 and December 31, 2011 at our institution were identified using the TRUST (Transfusion Registry for Utilization, Surveillance and Tracking) database. Only patients receiving chronic RBC transfusion were included in this study, which was defined as receiving ≥20 units of RBC or ≥ 20 RBC transfusions dosed at 15ml/kg within 12 consecutive months where the transfusions were not administered in the setting of an operating room, trauma or surgical procedure(s), not administered 7 days prior/post-surgical procedures and not all administered within one day. Adjudication by a second reviewer resolved any ambiguity regarding study inclusion. Medical records of eligible patients were reviewed to collect patient demographics, underlying diagnosis and reason for transfusions, and to evaluate IO screening practices (e.g. ferritin level, testing for systemic IO (e.g. FerriScan) if persistently high ferritin) and frequency of iron chelation therapy. Results A total of 35 patients fulfilled the eligibility criteria, with a mean age of 8.82 years (SD 5.36). Table 1 summarizes the demographics of the population, the transfusion requirements, how often the patient subgroups were screened and the screening results. In summary, 20 patients had ferritin levels checked, where 2 (AML and hepatoblastoma) patients had values under 500 μg and no screening was required. Of the remaining 18, 10 patients were diagnosed with a hemoglobinopathy (8) and congenital anemia (2) requiring chronic transfusions and underwent regular screening for iron overload and received iron chelation therapy. The remaining 8 patients had ferritin level >500 μg but no IO screening ordered. Of these 8 patients the majority were diagnosed with a cancer (leukemia, solid tumours) (5), acquired aplastic anemia (2), and hemophagocytic syndrome (1). The total number of transfusions for these 8 patients ranged from 20 to 52 with a median of 25 transfusions. Conclusion The majority (63%) of chronically transfused patients in this cohort had underlying cancer requiring aggressive chemotherapy. Only 32% of these patients had ferritin level tested and none were evaluated for systemic IO. TRIO may represent an additional, as yet unidentified, co-morbidity of cancer therapy. Therapies such as anthracycline or radiation may potentiate the end organ effect of TRIO at levels lower than that observed in patients with a hemoglobinopathy. Hence, it is important to develop strategies to evaluate children with cancer at risk for IO and to study the impact of transfusional iron accumulation on end organ function. 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)0.000
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.237
Teacher spread0.228 · 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".

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Citations1
Published2012
Admission routes1
Has abstractyes

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