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PF280 INTENSIVE TREATMENT OF ELDERLY AML PATIENTS IS SAFE AND FEASIBLE, PRINCESS MARGARET CANCER CENTRE EXPERIENCE

2019· article· en· W2951452128 on OpenAlexaff
Garrido Reyes, Caroline McNamara, Eshetu G. Atenafu, Jaime O. Claudio, Samson Chan, Aaron D. Schimmer, Shahzain Hassan, Y. Karen, Vijay Gupta, M.D. Minden, Dawn Maze, Anna Schuh

Bibliographic record

VenueHemaSphere · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineMyeloid leukemiaInternal medicineAcute promyelocytic leukemiaInduction chemotherapyHazard ratioCumulative incidenceCancerProportional hazards modelIncidence (geometry)PopulationCohortOncologyChemotherapyPediatricsConfidence interval

Abstract

fetched live from OpenAlex

Background: Acute myeloid leukemia (AML) is a clinically and biologically heterogeneous disease. The incidence of AML increases with age, with the median age at diagnosis currently 67 years. The outcome in older patients is worse than for younger patients, which relates to patient, disease characteristics and possibly changes in drug dosing. Treatment represents a challenge as there is variability in which of these patients will be offered curative intent therapy. Aims: To describe the clinical outcomes in older adults with acute myeloid leukemia treated with induction chemotherapy. Methods: A retrospective review was performed on all patients with a new diagnosis of AML defined by ≥20% blasts in peripheral blood or bone marrow (acute promyelocytic leukemia and myeloid sarcoma excluded), treated intensively at Princess Margaret Cancer Centre between February 2015 and August 2017. Statistical analysis with Kaplan‐Meier product‐limit method, Log‐rank test and Cox regression model were performed using version 9.4 of the SAS system for Windows (2002–2012 SAS Institute, Inc., Cary, NC). Results: We identified 283 patients with a new diagnosis of AML for whom induction chemotherapy was used as the frontline approach. Of these, 136 (48%) were ≥60 years (range 60–82). Demographic and clinical data of both age groups were well balanced. The median age at presentation for the younger cohort was 50 yrs (range 18–59) while for the older group was 68 yrs (range 60–82). Poor risk cytogenetics by MRC classification was more frequently seen in the younger population compared to the older group (27% vs 17%, P = 0.030). Type of induction chemotherapy was evenly distributed between the two groups. In total, 216 patients had a beneficial response (CR/CRi/morphologic leukemia free state) with no differences between the younger and older age groups (76% vs 77%, P = 0.73). There was no significant difference in early death between young (3%) and older population (5%) ( P = 0.339). In the Univariate Cox regression model comparing the two groups, MRC Cytogenetics ( P < 0.0001), type of induction chemotherapy ( P = 0.0079), post induction response ( P < 0.0001), white blood cell count ( P < 0.0001), lactate dehydrogenase ( P = 0.0015) and creatinine ( P = 0.03) were predictive of a shorter survival. Neither the type of diagnosis or ECOG retained prognostic value. In the multivariate analysis only post induction response remained statistically significant for length of survival ( P < 0.0001). With a median follow‐up of 13 months, older patients had a shorter OS (21.0 months) vs 31.6 months in the younger group ( P = 0.0121), Figure 1. The older population was subsequently subclassified into 2 different age groups, 60–69 yrs, 70–79 yrs (1 patient ≥ 80 was identified and excluded from analysis) to further investigate the effect of increasing age on outcomes. Survival for the 60–69 yrs and 70–79 yrs was 22.0 and 17.2 months respectively ( P = 0.473) Figure 2 Summary/Conclusion: This study shows that those older patients undergoing intensive chemotherapy have similar response rates and treatment related mortality compared to their younger colleagues. Furthermore, patients in the 70–79 group had similar median OS compared to their younger counterparts (60–69 yrs). Despite a greater proportion of younger patients having poorer risk disease, the older group had a worse OS suggesting that other disease and patient related factors are important. Transplant‐Molecular data using a 54 myeloid gene panel will also be presented at EHA. image

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.021
GPT teacher head0.308
Teacher spread0.287 · 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 teacher head, not a consensus.

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
Published2019
Admission routes1
Has abstractyes

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