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Efficacy and safety of azacitidine in the treatment of elderly patients with higher-risk myelodysplastic syndromes.

2025· article· en· W4410812091 on OpenAlexaff
Nupur Krishnan, Leah Kogan, Ian R. Drennan, Lauren Gerard, Rouslan Kotchetkov

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSunnybrook Health Science CentreBarrie Urology GroupMcMaster University
Fundersnot available
KeywordsMedicineAzacitidineMyelodysplastic syndromesOncologyInternal medicineIntensive care medicineBone marrow

Abstract

fetched live from OpenAlex

e18578 Background: Hypomethylating agent Azacitidine (AZA) is standard of care for high-risk myelodysplastic syndromes (MDS). There is limited real-life data, however, characterizing the efficacy and safety profile of AZA in elderly patients. Methods: A retrospective chart review of high-risk MDS patients at our institution who received front-line AZA therapy. Patients who proceeded with stem cell transplant were excluded. Patients were stratified by age: ≥75 years (elderly) vs <75 years (younger) and patterns of administration, safety, and efficacy of AZA in patients were compared. Results: Among a total of 105 patients, 56 were elderly (median age 79), including 27 patients >80 years, 49 were younger (median age 69). ECOG 3 had 8.9% of older & 12.2% of younger patients. Median R-IPSS score was 6.5 in younger vs 5.8 in elderly groups. Median number of comorbidities was 5 vs 6 in younger and elderly groups. 98% of elderly and 90% of younger patients received the full dose of AZA (75mg/m2). Median number of AZA cycles was 8 [range 1-107] in elderly vs 6 [1-96] in younger cohorts. Treatment delays had 35.7% of elderly vs 30.6% of younger patients, most commonly due to infection complications. Neutropenia, thrombocytopenia and anemia rates were 29.3%, 29.2%, and 26.8% in younger vs 43.6%, 25.2%, and 34.5% in older patients. Overall response rate was 92.8% in younger and 96.4% of elder patients, including complete remission in 53.7% and 58.3%, respectively. Relapse disease occurred in 48.8% of younger and 40.0% of elder patients. Transformation to AML was found in 9.8% of younger and 19.2% of elderly patients. Median OS was 17.3 months in the younger subgroup vs 15.7 in the elder group and 11.9 months in patients over 80 years. Rates of death were: 53.1% in younger vs 46.4% in the elderly. Causes of death were similar, including disease progression, sepsis, febrile neutropenia, pneumonia. Conclusions: AZA monotherapy is well-tolerated and effective in higher-risk MDS, even in very elderly patients (>80 years). Compared to clinical trial, patients in the real-world setting have shorter survival, higher ECOG status, and more comorbidities, potentially contributing to inferior outcomes. Clinical and hematological responses. Efficacy parameter/Age cohort, years (N) <75 (49) ≥75 (56) p value Hematological Response (median [min-max]) Baseline Hemoglobin (g/L) 77 [46-100] 80 [56-140] 0.014 Best Hemoglobin achieved on AZA (g/L) 103 [74-152] 108 [70-155] NS Baseline ANC (x10 9 /L) 0.7 [0-27] 0.89 [0.02-58] NS Best ANC achieved on AZA (x10 9 /L) 1.4 [0.02-12.3] 2.2 [0.04-9.6] 0.047 Baseline platelet count (x10 9 /L) 49 [2-600] 57 [16-558] 0.029 Best achieved on AZA (x10 9 /L) 128 [21-474] 155 [10-767] NS Transformation to AML N, (%) 4 (9.8) 10 (18.2) NS Reached transfusion independence, N (%) 30 (61.0) 38 (67.2) NS Response rate, N (%) ORR 46 (92.8) 54 (96.4) NS Complete Response 26 (53.7) 33 (58.2) NS Partial Response 11 (22.0) 11 (20.0) NS Stable Disease 8 (17.1) 10 (18.2) NS Mean Leukemia Free Survival (years) 1.13 1.05 NS Mean Overall Survival (years) 1.14 1.05 NS

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.421
Teacher spread0.366 · 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 designRandomized trial
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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Citations0
Published2025
Admission routes1
Has abstractyes

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