Improved Leukemia-Free and Overall Survival in Patients with Myelodysplastic Syndrome Receiving Iron Chelation Therapy: A Subgroup Analysis.
Notice bibliographique
Résumé
Abstract Myelodysplastic syndrome (MDS) is characterized by ineffective hematopoiesis and risk of progression to acute myeloid leukemia (AML). Many MDS patients (pts) require red blood cell (RBC) transfusions, risking iron overload (IOL)-related organ dysfunction. We previously showed in a multivariate analysis of 178 pts, improved survival in 18 pts with low and int-1 IPSS risk MDS and IOL receiving iron chelation therapy (ICT), and now examine the effect of ICT on AML-free survival (LFS). The effect of ICT on cytopenias and RBC transfusion requirements (TR); was also examined. To control for possible bias favoring ICT pts, a subgroup analysis was performed. Each of 18 ICT pts had a non-ICT control pt (CP) selected and these groups are matched for gender; neutrophil count (NC), platelet count (PLTC) and hemoglobin (Hb) at diagnosis (Dx); MDS subtype; no of cytopenias, karyotype; IPSS score; ECOG Performance Status; no of serious infections; initial ferritin level (FL); total RBC units (U) received; primary MDS treatment (Rx); and duration of follow-up (FU; all p=NS). Median age in ICT pts was 64 (range 32–70) years (y) vs. 78 (39–81)y for CP (p=0.04). Features of the 178 pts are previously reported. In a univariate analysis (n=178), factors significant for LFS were: MDS subtype; IPSS risk; increased FL; total RBCU transfused; ≥1 serious infection; and receipt of ICT (all p<0.05). Factors significant for LFS in a multivariate analysis (n=178) were: IPSS score (p=0.0001) and receipt of ICT (p=0.03). For low and int-1 IPSS, median LFS was not reached (NR) at 226 months (mo) for ICT pts and 40 mo for non-ICT pts (n=76) and 4y LFS was 64% and 42% respectively (p=0.002). In the subgroup analysis comparing ICT pts to 18 CP with matched features, at a median FU of 51.4 (7.1–225.8) mo and 44.8 (10.1–224) mo respectively, median LFS was NR at 226 mo vs. 40 mo in CP, and 4y LFS was 64% and 49% respectively (p=0.009). Median OS for ICT pts was NR at 226 mo vs. 40.5 mo for CP, and 4y OS was 64% and 49% respectively (p=0.01). There were 5 deaths (28%) in ICT pts (cardiac/IOL, n=2; infection, n=1; other MDS-related, n=1; AML, n=1) and 15 (83%) in CP (other MDS-related, n=6; AML, n=4; bleeding, n=2; infection, n=2; MDS-unrelated, n=1). Although non-ICT pts were older, only 1 death was age-related and all others were from MDS. One ICT pt developed AML at 15 mo from MDS Dx as did 4 non-ICT pts (p=0.06) at a median of 35 (19–71) mo; 2 pts received chemotherapy and both died of progressive AML. In ICT pts, mean ± standard error of the mean (sem) initial/pre-ICT FL was higher than in CP (4038±627 vs. 1759±1108 ug/L respectively, p=0.09), and FU levels decreased for ICT pts (3070±411, p=0.09) but not for CP (2185±996 ug/L, p=NS). There was no difference between ICT pts and CP in mean ± sem initial (2.0±0.3 and 2.0±0.5) and FU (3.4±0.9 and 7.4±4) x109/L NC; initial (236±32 and 104±25) and FU (164±24 and 82±20) x109/L PLTC; or initial (2.6±0.3 and 3.0±0.5) and FU (4.2±0.5 and 4.3±0.9) RBC-TR (no RBCU/4wk; all p=NS). In conclusion, LFS and OS in MDS pts with IOL receiving ICT were improved compared to non-ICT control pts matched for baseline features. These results support findings in the larger cohort of MDS pts and suggest there may be a beneficial effect of ICT on AML transformation and OS. Prospective studies of ICT in MDS pts are warranted.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».