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Enregistrement W2562380517 · doi:10.1182/blood.v118.21.1732.1732

Improved Survival with Iron Chelation Therapy for Lower IPSS Risk MDS Appears to Have a Stronger Association in Patients with a Non-RARS Diagnosis

2011· article· en· W2562380517 sur OpenAlexaff
Heather A. Leitch, Christopher T. Chan, Chantal S. Leger, Lynda Foltz, Khaled M. A. Ramadan, Linda M. Vickars

Notice bibliographique

RevueBlood · 2011
Typearticle
Langueen
DomaineMedicine
ThématiqueHematological disorders and diagnostics
Établissements canadiensSt. Paul's HospitalQueen's UniversityUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineMyelodysplastic syndromesConcomitantInternational Prognostic Scoring SystemRetrospective cohort studyHematologyChelation therapyGastroenterologySurgeryBone marrowThalassemia

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 1732 Background: Several retrospective analyses suggest that transfusional iron overload portends inferior survival in lower risk MDS and that iron chelation therapy (ICT) is associated with improved survival in this group of patients. However an analysis of 126 patients with RARS from the Mayo Clinic showed no association between elevated ferritin level at diagnosis or transfusion burden on overall survival (OS). We performed a retrospective analysis of 268 MDS patients seen at our center to determine whether an association between transfusional iron overload or receiving iron chelation therapy (ICT) and survival differed between RARS and other lower risk MDS. Methods: Patients were identified from the clinical database of the hematology practice. Patients with a diagnosis (dx) of MDS confirmed by bone marrow biopsy (bmbx) were included. Clinical and laboratory data were collected by retrospective chart review. Survival analyses were performed using SPSS version 19. Results: 268 patients with a bmbx confirmed diagnosis of MDS by WHO or FAB criteria were identified. The following patients were excluded: uncertain IPSS score, n=35; intermediate-2 risk, n=33; high risk, n=16; RAEB-t, n=3; concomitant diagnosis of advanced stage non-Hodgkin lymphoma of uncertain type, n=1. The remaining 182 patients had the following characteristics: median age 69.5 (range 30–94) years and 109 (69.9%) were male. Specific MDS dx were: RA, n=27; RARS, n=53; RCMD, n=34; RAEB, n=15; MDS-U, n=22; hypocellular MDS, n=6; 5Q- syndrome, n=6; CMML, n=21. IPSS scores for all patients were: intermediate-1, n=101; low, n=74; uncertain (but IPSS score not >1.0), n=7. The marrow blast count was 6–9 x109/L in 4 patients and <5 x109/L in all others. Specific MDS treatment (rx) was: supportive care, n=82; erythropoiesis stimulating agents (ESA), n=22; immunosuppressive therapy (IST), n=10; lenalidomide, n=7; and chemotherapy, n=6. 137 patients received RBC transfusions and 38 received ICT: deferasirox (DFX), n=19; deferoxamine (DFO), n=9; DFO followed by DFX, n=9; and DFX followed by DFO, n=1. The median duration of ICT was 10.5 (range 0.5–64) months. Clinical features significantly associated with OS in univariate analyses of all 182 patients included: specific MDS dx; IPSS score; total number of red blood cell (RBC) units transfused over the course of follow-up; receiving ICT; specific MDS rx received; requirement for hospitalization; experiencing at least one episode of infection; and AML transformation (P</=0.01 for all); serum ferritin level >1000ng/mL was not significant in this analysis (P=not significant [NS]). In a multivariate analysis (MVA), the following factors remained significant for OS: specific MDS dx; IPSS score; receiving ICT; specific MDS rx; and AML transformation (P</=0.01 for all). In an MVA stratified for RARS, significant were: specific MDS dx (P<0.0001); IPSS score (P=0.005); specific MDS rx (P=0.038) and receiving ICT (P=0.039). At a median follow-up of 28 (0.1–245.9) months, 121 patients were alive (non-RARS, n=83 [64.3%]; RARS, n=38 [71.6%]) and the projected median OS for all patient was 99 months. The projected median OS for non-RARS patients without ICT and with ICT was 44 months and not reached (NR), respectively, and for RARS without and with ICT was 99 and 134.4 months (P<0.0001). The 5 year OS in these four groups was 39.2% and 91.7% (P=0.04); and 72.4% and 76.3%, respectively (P=NS). However, when RARS ICT patients were compared to only RBC transfusion dependent RARS patients not receiving ICT, the median OS was 73.8 vs 134.4 months, respectively, and 5 year OS was 59.9% and 76.3%, respectively (P=0.025). Conclusions: These results suggest an association between receiving iron chelation therapy and survival in lower IPSS risk MDS, in keeping with prior analyses. However, the association between ICT and OS in non-RARS MDS appeared to be stronger than in RARS, in keeping with data from Mayo suggesting transfusional iron overload may not have a major association with outcome in RARS. The median follow-up in the current study was just over 2 years, and median duration of ICT only 10.5 months; longer follow-up may be needed in RARS to determine whether ICT is potentially beneficial in this subgroup of patients with a relatively long expected survival. As with all retrospective analyses, these results must be considered hypothesis generating, and prospective trials are needed for firm conclusions to be drawn. Disclosures: Leitch: Novartis Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Off Label Use: Iron chelation agents for the treatment of transfusional iron overload in MDS. Vickars:Novartis Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,413

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,226
Écart entre enseignants0,213 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2011
Routes d'admission1
Résumé présentoui

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