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Enregistrement W2464869795 · doi:10.1182/blood.v126.23.1701.1701

Iron Chelation Is Associated with Improved Survival Adjusting for Disease and Patient Related Characteristics in Low/Int-1 Risk MDS at the Time of First Transfusion Dependence: A MDS-CAN Study

2015· article· en· W2464869795 sur OpenAlexaffabout
Ambica Parmar, Heather A. Leitch, Richard A. Wells, Thomas J. Nevill, Nancy Zhu, Karen Yee, Brian Leber, Mitchell Sabloff, Ève St‐Hilaire, Rajat Kumar, Michelle Geddes, John M. Storring, Andrea Kew, April Shamy, Mohamed Elemary, Martha Lenis, Alex Mamedov, Rena Buckstein

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensMcGill UniversityQueen Elizabeth II Health Sciences CentreMontreal General HospitalFoothills Medical CentreHealth Sciences CentreCancerCare ManitobaOttawa HospitalPrincess Margaret Cancer CentreUniversity of AlbertaUniversity of SaskatchewanLeukemia & Lymphoma Society of CanadaSunnybrook Health Science CentreMcMaster UniversitySt. Paul's HospitalUniversity of British ColumbiaUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineInterquartile rangeHazard ratioInternational Prognostic Scoring SystemProportional hazards modelInternal medicineMyelodysplastic syndromesComorbidityRetrospective cohort studyUnivariate analysisMultivariate analysisPediatricsConfidence interval

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Transfusional hemosiderosis is common in myelodysplastic syndromes (MDS). There are multiple retrospective analyses demonstrating a survival benefit associated with iron chelation therapy (ICT) in lower risk, transfusion dependent (TD) MDS patients. However, these studies are limited by their retrospective nature, potential for bias and by the use of risk scores at diagnosis rather than at the onset of TD. Since January 2012 the Canadian MDS Registry has prospectively collected disease and patient-related data on MDS patients including comorbidity (Charlson and MDS-CI), frailty (Rockwood clinical frailty scale) and disability [Lawton Brody Instrumental Activities of Daily Living (sIADL)]. We compared characteristics and clinical outcomes of lower risk TD MDS patients who received ICT to non chelated TD patients, adjusting for MDS and patient-related factors. Methods: Only patients who remained International Prognostic Scoring System (IPSS) low or intermediate (int)-1 risk at the time of first TD were included with MDS and patient-related factors analyzed at first TD rather than at MDS diagnosis or registry enrollment. Univariate and multivariate Cox proportional hazard models were used to determine significant predictive factors for overall survival (OS) and the model with the highest R2 was selected. Results: 219 Low (n=69)/Int-1 (n=149) risk MDS patients at the time of first TD were included. Median age was 73 [interquartile range (IQR 65,80)] with a median time from diagnosis until TD of 7 months (IQR 1,28). 60% were male with a median ECOG of 1 and median blast of 3% (IQR 1,4). By WHO classification, 39% and 37% had unilineage and multilineage dysplasia respectively, 11% CMML and 13% had excess blasts. By IPSS-revised (R), very low, low, intermediate, high and very high risk groups were 12%, 34%, 38%, 15% and 0.5%, respectively. Seventy (32%) patients received ICT with desferrioxamine (n=6), deferasirox (n=56) or both (n=8). At the time of first TD, chelated patients were younger, had higher ferritins and had lower IPSS-R risk scores (Table 1). Importantly, frailty, comorbidity, and disability scores did not differ. At a median follow up of 2.7 (IQR 2.2-3.3) years from diagnosis, OS was 6.1(IQR 4.5-7.5) years. OS was significantly improved among MDS patients treated with ICT as compared to those without (median 8.62 vs. 4.38 years, respectively, p = 0.0005, Figure 1.) By univariate analysis, age, ICT, IPSS, IPSS-R, MDS-CI, frailty, karyotype, time from diagnosis until TD, and disability were associated with improved OS. By multivariate analysis, ICT, age at TD and IPSS-R at TD were independently predictive of OS (Table 2). Conclusions: Adjusting for patient and disease related factors at the time of TD, ICT remains predictive of improved OS in patients with low/int-1 risk MDS who become TD. The adjustment for patient-related factors and analysis from TD rather than MDS diagnosis, diminishes the impact of selection bias that may have favored ICT patients in past analyses and lends additional support to the role for ICT in lower risk MDS. Table 1. Comparing Clinical Factors at the time of First Transfusion Dependency (TD) between Chelated and Non-Chelated Patients Factors at Time of TDMedian (IQR) Without Iron Chelation (n=149) With Iron Chelation (n=70) p-value Age (y) 75 (67,81) 69 (62,75) 0.0008 Ferritin (ug/L) 664 (346,1118) n=92 1201 (883,1691) n=44 <0.0001 RA/RARS, del5q, MDS-U, RCUD [N(%)] RCMD+/-RS CMML/MDS/MPN RAEB1 RAEB2 51 (34) 56(38) 18 (12) 17 (11) 7 (4) 35 (50) 25 (36) 3 (4) 6(9) 1(1) 0.11 IPSS [N(%)] Low Int-1 42 (28) 106 (72) 27 (39) 43 (61) 0.16 IPSS-R [N(%)] Very Low Low Intermediate High Very High 15 (10) 48 (32) 54 (36) 30 (20) 1 (0) 12 (17) 26 (37) 29 (41) 3 (4) 0 (0) 0.01 Frailty N=96 3 (2,4) N=37 3 (2,4) 0.40 Charlson Comorbidity N=95 1 (0,2) N=37 0(0, 1) 0.06 MDS-CI N=95 1 (0,2) N=37 0 (0,2) 0.26 Lawton Brody Disability N=90 1 (0,2) N=36 0 (0,2) 0.68 Time from diagnosis until TD (mo) 6 (1,23) 14 (0,38) 0.19 Table 2. Predictive factors for Overall Survival by Multivariate Analysis Predictive Factors p-value HR 95% CI of HR R2 (%) Iron chelation (no vs. yes) 0.0152 1.821 1.122 2.953 14.76 Age at time of diagnosis (yr) 0.0125 1.025 1.005 1.045 IPSS-R at time of TD 0.0018 High/vHigh vs. Low 0.0004 2.866 1.601 5.132 Int. vs. Low/vLow 0.0775 1.523 0.955 2.429 High/vHigh vs. Low 0.0292 1.882 1.066 3.322 Figure 1. Kaplan-Meier Curve of Overall Survival of Chelated and Non-Chelated Patients Figure 1. Kaplan-Meier Curve of Overall Survival of Chelated and Non-Chelated Patients Disclosures Leitch: Alexion: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Exjade: Speakers Bureau. Wells:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Alexion: Honoraria, Research Funding. Nevill:Celgene: Honoraria. Zhu:Novartis Canada: Membership on an entity's Board of Directors or advisory committees; Celgene Canada: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Yee:Oncoethix: Research Funding; Novartis Canada: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Canada: Membership on an entity's Board of Directors or advisory committees, Research Funding. Leber:Celgene Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees. Sabloff:Celgene: Honoraria. Kumar:Celgene Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees. Geddes:Celgene: Honoraria. Storring:Celgene Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kew:Celgene: Honoraria. Shamy:Novartis Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees. Elemary:Celgene Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees. Buckstein:Celgene: Honoraria, 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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,020
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,008
Tête enseignante GPT0,214
Écart entre enseignants0,206 · 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 source (Gemma direct ou Codex distillé), 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

Citations5
Publié2015
Routes d'admission2
Résumé présentoui

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