Frailty risk assessment and impact on acute myeloid leukemia outcomes (FRAIL-AML): A population-based study from Ontario, Canada.
Bibliographic record
Abstract
6506 Background: In acute myeloid leukemia (AML), treatment decisions, specifically intensive versus non-intensive approaches, depend on the clinician’s assessment of patient fitness. Frailty as a measure of fitness is a broader concept than commodities and is associated with worse outcomes in many cancers. Frailty assessment is incorporated to provide the precision to oncology treatment selections in addition to disease-related factors. The impact of frailty on outcomes in AML is not well studied. Methods: This retrospective cohort study from population-based health administrative databases in Ontario, Canada (ICES) included all patients (pts) ≥18 years diagnosed with AML between 2006 and 2021, treated within 90 days after diagnosis. Frailty was measured using McIsaacs’s frailty index (MFI)- a validated tool. Progressively increasing tertiles of MFI were categorized as fit (FT), pre-frail (PFR), or frail (FR). Treatment intensity was classified as intensive (IT) or non-intensive (NIT) based on standard practices. The primary outcome was overall survival (OS). Association of frailty with OS was measured using Cox regression separately for IT and NIT AML. Results: Out of 5450 pts, with a median age of 65 (IQR 54-74), 55.8% were males and 44.2% were females. 65% (n=3543) received IT, out of which 29.4% and 35.5% pts were FR and PFR respectively. Remaining 35% (n=1907) received NIT, with 41.1% and 32.3% pts being FR and PFR. Median overall survival in months (OS, 95% CI) for the entire group, IT, and NIT were 12.5 (12.0-13.2), 16.7 (15.7-18.2), and 7.6 (7.0-8.2), respectively. OS (table) was notably lower in FR pts compared to fit pts (p<0.0001) in both IT and NIT. Univariate and multivariate analyses identified frailty, advanced age, and previous non-AML malignancy as risk factors associated with worse OS in both IT and NIT groups (table). Conclusions: Higher frailty is independently associated with worse OS in AML pts after adjusting for advanced age. A substantial proportion of AML pts in both IT and NIT groups exhibit a mismatch in treatment intensity assignments based on their frailty status, with about 30 % FR pts receiving IT and over 25 % FT pts receiving NIT. This study sheds light on the need for frailty evaluations using standardized tools to optimize treatment decisions in AML pts. [Table: see text]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".