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Record W4405092599 · doi:10.1182/blood-2024-211183

Impact of Frailty in a Prospective Cohort of Patients with MDS Treated with Hypomethylating Agents

2024· article· en· W4405092599 on OpenAlexaff
James T. England, Liying Zhang, Karen Yee, Michelle Geddes, Nancy Zhu, April Shamy, Heather A. Leitch, Mitchell Sabloff, Grace Christou, Brett L. Houston, Brian Leber, Dina Khalaf, Ève St‐Hilaire, Nicholas Finn, Thomas J. Nevill, Amy M. Trottier, John M. Storring, Mohamed Elemary, Robert Delage, Rena Buckstein

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversité LavalUniversity of SaskatchewanQueen Elizabeth II Health Sciences CentreMcMaster UniversityJuravinski Cancer CentreUniversity of ManitobaDr. Georges-L.-Dumont University Hospital CentreSt. Paul's HospitalUniversity of British ColumbiaSunnybrook Health Science CentreJewish General HospitalUniversity of AlbertaMcGill University Health CentreMcGill UniversityVancouver General HospitalPrincess Margaret Cancer CentreUniversity of CalgarySaskatchewan Cancer AgencyCancerCare ManitobaOttawa HospitalUniversity Health NetworkHealth Sciences Centre
Fundersnot available
KeywordsMedicineHypomethylating agentProspective cohort studyMyelodysplastic syndromesDecitabineAzacitidineCohortInternal medicineOncologyBone marrowDNA methylation

Abstract

fetched live from OpenAlex

Introduction: Myelodysplastic neoplasms (MDS) are a group of clonal hematopoietic disorders characterized by cytopenias and a risk of progression to Acute Myeloid Leukemia. Frailty is an age-related state of vulnerability that reflects multifactorial loss of physiologic reserve. Up to 25% of patients with MDS are observed to be vulnerable or frail reflecting the advanced age of patients at diagnosis. Frailty has been observed to independently predict survival in patients with myelodysplastic syndromes, even after adjustment for standard risk categories, disease-related factors, and comorbidity indexes. An important determinant affecting survival in higher-risk MDS is the ability to receive an adequate trial of therapy with a hypomethylating agent (HMA). The concern of upfront toxicity in patients with frailty may lead to many not being offered HMA therapy and directly impede their survival. Up to 4-6 cycles of HMA therapy may be needed to derive hematopoietic response and survival benefit and patients with frailty may discontinue HMA prior to deriving benefit. We investigated the independent effect of frailty on overall survival and the likelihood of completion of at least 4-6 cycles of HMA therapy in MDS-CAN, a multi-centre, prospective cohort (NCT02537990) of patients with MDS or chronic myelomonocytic leukemia (CMML). Methods: Consecutive patients with a diagnosis of MDS or CMML who received at least one cycle of HMA (azacytidine, decitabine, decitabine/cedazuridine) were included in the study. Frailty, comorbidity, instrumental activities of daily living, disability, quality of life, fatigue and physical performance measures were evaluated at baseline. Frailty was measured using the Rockwood CSHA 9 point clinical frailty scale (FS) and the MDS-specific 15-item frailty scale (FS-15). Overall Survival (OS) years was defined as the time from HMA start date to death or last follow-up. Cox proportional hazard models were constructed to evaluate the impact of patient- and disease- related variables on OS. Logistic regression models were used to evaluate predictors of completing ≥4 or ≥6 cycles of HMA therapy. Results: A total of 513 patients were included in the study with revised international prognostic scoring system (IPSS-R) risk categories of: very low/low n=22 (4%), intermediate n=103 (20%), high n=145 (28%), and very high n=173 (34%). Median [range] age was 72.7 [66-81] years and 352 (69%) were male. Azacytidine was used in 442 patients while 71 received IV or oral decitabine. The median [interquartile range] number of cycles received was 9 [5-13]. A total of 411 (80%) patients died during the study period, with median [95% confidence interval (CI)] OS for the entire cohort of 1.69 [1.51-1.91] years from HMA start. Frailty was predictive of OS whether measured by the Rockwood FS (P<0.001) or MDS FS-15 (P=0.002). For patients with Rockwood FS categories of 1-2, 3, and ≥4 the actuarial median (95% CI) OS were 2.27 (1.85-2.53) years, 1.38 (1.18-1.71) years, and 1.14 (0.87-1.41) years, respectively. The median [95%CI] OS for MDS FS-15 scores of ≤0.20, 0.21-0.30, and >0.30 were 2.43 (1.07-3.47) years, 1.95 (1.15-2.74) years, and 1.51 (1.32-1.67) years. In multivariable analysis including patient- and disease-specific factors, OS from start of HMA therapy was predicted by older age (Hazard Ratio 1.03 [95%CI 1.01-1.04]), transfusion dependence (HR 1.36 [1.03-1.79], high-risk IPSS-R (HR 2.81 [1.15-6.86]), HMA <4 cycles (HR 3.74 [2.67-5.24]), Rockwood FS (HR 1.14 [1.02-1.27]), and MDS FS-15 score (HR 3.57 [1.25-10.2]). In the study cohort 418 (81%) of patients completed at least 4 cycles of HMA therapy, and 363 (71%) completed ≥6 cycles. In multivariable logistic regression analysis, completion of at least 4- or 6-cycles of HMA therapy were predicted by higher hemoglobin (Odds Ratio 1.03 [95%CI 1.01-1.04] for ≥4 cycles; OR 1.02 [1.001-1.03] for ≥6 cycles), and lower MDS FS-15 scores (OR 0.14 [0.02-0.96] for ≥4 cycles; OR 0.14 [0.03-0.69] for ≥6 cycles). Conclusion: Our study provides further evidence for the importance of frailty evaluation for predicting survival in patients with MDS treated with HMA therapy. The MDS-specific FS-15 measurement of frailty was predictive of patients at risk for not completing 4-6 cycles of HMA therapy and understanding these reasons will be important. Frailty evaluation before HMA may identify patients in need of further supports or alternative therapy strategies.

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)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.050
GPT teacher head0.374
Teacher spread0.324 · 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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Citations0
Published2024
Admission routes1
Has abstractyes

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