MétaCan
Menu
← Back to cohort

Outcomes of Elderly Patients with AML in the Hamilton Area: How Are We Doing?

2008· article· en· W2992713422 on OpenAlexaff
Sadiya Kukaswadia, Tina Hsu, Parveen Wasi

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMyeloid leukemiaPediatricsPopulationInternal medicineLeukemiaMyelodysplastic syndromesBone marrow

Abstract

fetched live from OpenAlex

Abstract Acute myeloid leukemia (AML) is the most common form of leukemia amongst adults. Elderly patients (i.e. over the age of 60) with AML consistently have poorer outcomes than their younger counterparts and very few guidelines exist on the optimal management of AML in this population. A retrospective chart review of patients 60 years of age or older diagnosed with AML was conducted to better define this population, identify biological and patient characteristics that predict outcomes with treatment, and determine factors influencing management decisions. A total of 142 patients diagnosed with AML between April 2002 and April 2007 were included. Patients were analyzed together, as well as in pre-specified age groups (60–65, 66–75, and >75 years). Patients ranged from 60 to 92 years old, with a median of 70 years. Very few patients had a favorable cytogenetic profile (3.5%) at the time of diagnosis (Table 1). In addition, 41.5% of patients had a preceding hematologic diagnosis. Of these, 61.0% had pre-existing myelodysplastic syndrome and 16.9% had chronic myeloid leukemia. The proportion of patients with secondary transformation increased with age (age 60–65 −25%; 65–75 −39%; >75 – 51.3%). Over 60% of patients were induced, with younger patients opting for induction more often (age 60–65 − 88.6%; 65–75 − 66.1%; >75 – 20.5%). The most commonly cited reasons for not treating were the presence of comorbidities (32.1%), patient preference (26.8%), age (17.9%), and preceding hematological conditions (16.1%). Only 25% of those treated were able to complete the entire course of treatment. Despite this, more than half (58.1%) of patients were able to attain remission. As expected, overall survival was dismal across all age groups with 10.6% surviving to one year. Median survival was 2 months with survival decreasing with increasing age (Table 2). With treatment one year survival increased to 15.1% with a median survival of 3.75 months (treated vs. untreated - 15.1% vs. 3.6%, p <0.005). This result was largely driven by survival in the 60–65 year age group, in whom those treated did significantly better than those who were not (1 year survival 20.5% vs. 0%, p <0.005; median 6 vs. 0.18 months). In terms of economic resources, patients who were induced had significantly more outpatient appointments (22.2 vs. 6.4, p <0.0001), hospital days (58.8 vs. 11.6, p <0.0001), and used more blood products (65.7 vs. 12.1, p <0.001), presumably due to increased survival in those who were treated. This dramatic difference between those treated and those who were palliated was seen both in patients age 60–65 and 66–75, but was markedly attenuated in patients older than 75. Our findings are consistent with previous studies. Elderly patients with AML do poorly, with worsening outcomes with increasing age, and survival that is measured in months. This may be due to the increasing prevalence of patients with preceding hematological disorders and secondary transformation with age, as well as poor cytogenetic profiles of this population. In addition, as age and comorbidities increase, more patients opt out of induction chemotherapy. Further research is needed to establish optimal management and improve outcomes of elderly patients with AML. Table 1: Cytogenetics All patients (n; % of patients) Age 60–65 years (n; % of patients) Age 66–75 years (n; % of patients) Age 76 years and greater (n; % of patients) No of Patients 142 44 59 39 Cytogenetics Unknown 40 (28.2%) 10 (22.7%) 11 (18.6%) 19 (48.7%) Favourable 5 (3.5%) 1 (2.3%) 2 (3.5%) 2 (5.1%) 12 Intermediate 59 (41.5%) 23 (52.3%) 24 (40.7%) (30.8%) 6 Unfavourable 38 (26.8%) 10 (22.7%) 22(37.3%) (15.4%) Table 2: Survival Rates All patients (n; % of patients) Age 60–65 years (n; % of patients) Age 66–75 years (n; % of patients) Age 76 years and greater (n; % of patients 1 year survival 15 (10.6%) 8 (18.2%) 5 (8.5%) 2 (5.1%) All 2/56 (3.6%) 0/5 (0%) 1/20 (5.0%) 1/31 (3.2%) Untreated 13/86 (15.1%) 8/39 (20.5%) 4/39 (10.3%) 1/8 (12.5%) Treated (p = 0.004) (p = 0.003) (p = 0.41) (p = 0.23) Median survival (months) 2 3 2 1 All 1 5.5 days 1.5 1 Untreated 3.75 2 6 2.9 Treated Lost to Follow Up 52 (36.3%) 17 (38.6%) 24 (40.7%) 11 (28.2%)

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)0.002
metaresearch head score (Gemma)0.015
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 score0.980
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicAcute Myeloid Leukemia Research→French-language works237,207→