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Transfusional Iron Overload: An Underappreciated Danger in AML Patients?

2009· article· en· W2570279763 on OpenAlexaff
Jacob Rozmus, Louis D. Wadsworth, John K. Wu

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsDexrazoxaneAnthracyclineMedicineDoxorubicinCardiotoxicityCancerCardiomyopathyPopulationInternal medicineAdverse effectToxicityOncologyChemotherapyBreast cancerHeart failure

Abstract

fetched live from OpenAlex

Abstract Abstract 4133 The survival rate for childhood cancer has improved steadily over the last 3 decades creating an increasing population of survivors. Though this is one of the great successes in medicine, there is a growing awareness that survivors are at increased risk for late therapy related adverse effects including cardiovascular toxicity. The Childhood Cancer Survivor Study showed that the standardized mortality ratio for cardiac causes was > 8 times higher than expected and cumulative probability of cardiac death increased 15-25 years after cancer diagnosis.[i] The cardiotoxic effects of anthracyclines are well documented in the literature. They are an essential component of treatment for AML. However, their use is limited by dose-related cardiomyopathy. An important factor in anthracycline toxicity is iron's role in promoting the formation of toxic oxygen species. Cardiac tissue is recognized to be especially vulnerable to free radical damage. Anthracyclines cause altered expression of iron-regulated genes and change intracellular iron trafficking. It is known that, dexrazoxane-an iron chelator, is an effective cardioprotective agent against doxorubicin effects in animal models. The American Society of Clinical Oncology recommends its use in metastatic breast cancer patients receiving a doxorubicin dose of >300 mg/m2.[ii] Dexrazoxane was found to prevent or reduce cardiac injury associated with doxorubicin use in childhood ALL without compromising the anti-leukemic effect.[iii] We hypothesize that cardiomyocytes damaged by anthracyclines are more susceptible to iron accumulation, potentiating anthracycline toxicity in patients with a heavy transfusion burden. Consecutive adolescent patients with AML admitted to our institution were reviewed. These patients received a cumulative anthracycline dose of 200 to 300 mg/m2. Iron loading was estimated from the number of red cell units given. The iron content of a single red cell unit is approximately 200 mg. 10 AML patients received 24-59 units of blood (median 35) over a median of 222 days. This equates to 65-235 mg of iron/kg (median of 129 mg/kg). Iron loading was identified in AML patients due to transfusion. The iron load is less than seen in children with thalassemia but in AML patients, who are known to have increased adverse cardiac events, it is possible that anthracycline induced cardiomyopathy could have been exacerbated by transfused iron. To prove the hypothesis the next step is to investigate T2* MRI detectable myocardial iron deposition and cardiac dysfunction and markers of myocardial injury in AML patients. These observations may provide evidence for using iron chelation therapy in the treatment of AML. [i] Lipshultz S, Alvarez JA, Scully RE. Anthracycline associated cardiotoxicity in survivors of childhood cancer. Heart 2008; 94: 525-533 [ii] Carver JR, Shapiro CL, Ng A, et al. ASCO Cancer Survivorship Expert Panel. American Society of Clinical Oncology clinical evidence review on the ongoing care of adult cance survivors: cardiac and pulmonary late effects. J Clin Oncol 2007; 25: 3991-4008 [iii] Lipshultz SE, Rifai N, Dalton VM, et al. The effect of dexrazoxane on myocardial injury in doxorubicin-treated children with acute lymphoblastic leukemia. N Engl J Med 2004; 351: 145-53 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)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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
Published2009
Admission routes1
Has abstractyes

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