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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 on 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

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsMcGill 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
Fundersnot available
KeywordsMedicineInterquartile rangeHazard ratioInternational Prognostic Scoring SystemProportional hazards modelInternal medicineMyelodysplastic syndromesComorbidityRetrospective cohort studyUnivariate analysisMultivariate analysisPediatricsConfidence interval

Abstract

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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.

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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)0.001
metaresearch head score (Gemma)0.002
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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".

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Citations5
Published2015
Admission routes2
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