Are Children with Cancer Receiving Myelosuppressive Therapy At Risk for Transfusion-Related Iron Overload?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».