Performance of the ELN 2022 risk stratification in a real-world Canadian AML patient population
Bibliographic record
Abstract
Abstract Introduction The 2022 European Leukemia Network (ELN) risk stratification system for acute myeloid leukemia (Dohner et al, Blood, 2022) added several myelodysplasia-related (MR) genetic mutations to the adverse risk category, assigned FLT3-ITD mutations to the intermediate risk category, and designated bZIP in-frame mutations in CEBPA as favorable risk. Several studies have validated this system, though many of these are based on clinical trial cohorts with a bias towards younger patients. We performed an analysis of outcomes using this risk stratification system on a Canadian, population-based cohort of intensively treated AML patients. Methods Adults diagnosed with AML in British Columbia (BC) from 2016-2023, who received cytarabine-anthracycline induction, were identified from the provincial Leukemia/BMT registry. All patients had conventional karyotyping and a next-generation sequencing panel performed at diagnosis. The primary outcome was overall survival (OS) according to the ELN 2022 classification. Secondary outcomes included rate of complete remission (CR) or CR with incomplete count recovery (CRi), event-free survival (EFS), outcome within risk categories, and outcome following stem cell transplant. Categorical variables were compared using Chi-square and Fisher's exact tests and continuous variables were compared using paired t-tests. The Kaplan-Meier method was used to estimate survival and log-rank test was used to compare differences between groups. Results Among 499 patients who met our inclusion criteria, the median age was 62 years, with 59.2% males. De novo AML represented 83.4% of cases, 10.8% arose from prior MDS/MPN, and 5.8% had therapy-related AML. Allogeneic stem cell transplant (alloSCT) was performed in 57.1% of cases (48.9% in CR1). The rates of alloSCT in CR1 in ELN 2022 favorable, intermediate and adverse risk categories were 16.2%, 64.5% and 62.8%, respectively. By ELN 2017 and ELN 2022 criteria, 36.6% and 30.9% of patients were favorable risk, 20% and 27.7% were intermediate risk, and 37.9% and 41.5% were adverse risk, respectively; 26 (5%) patients were not classifiable by ELN 2017 as the FLT3-ITD allelic ratio was not available. After a median follow-up of 40 months in surviving patients, median OS of ELN 2022 favorable-risk disease differed significantly from those with intermediate- and adverse-risk disease (75 months vs 30 months and 27 months, p=0.011). Median OS of the intermediate- and adverse-risk groups did not differ significantly. Three-year OS was 61%, 44% and 47% in the favorable, intermediate and adverse risk groups, respectively. After 1 cycle of intensive therapy, the rates of CR/CRi were 85.7%, 62.3% and 43.5% in favorable, intermediate and adverse risk patients (p<0.001). Event-free survival (EFS) was longer for the favorable risk category, but not significantly different between the intermediate and adverse risk categories (3-year median EFS 47%, 35% and 36%, respectively). OS within the defined subtypes of ELN 2022 favorable (p=0.19) and intermediate risk (p=0.16) disease was not significantly different, but differed significantly within the adverse risk group subtypes (p<0.001). Patients with AML with mutated TP53, KMT2A rearrangement, or MECOM rearrangement had a relatively shorter OS with median OS of 6.5 months, 13.5 months and 26 months respectively. In contrast, patients with wild-type TP53 with MR mutations or with complex karyotype, −5/del(5q) or −7 had a median OS 44 months and 46 months, respectively. The median OS for patients with t(6;9) was similar to the favorable risk group, with the median OS was not reached in the small number of patients (n=6). OS of patients following alloSCT was not statistically different according to the ELN 2022 risk groups (p=0.075), although this was performed in CR2 or later for most favorable risk patients. Conclusions In our cohort, which represents an unselected, recent and ethnically diverse real-world population, the ELN 2022 criteria does not discriminate between survival outcomes for intermediate- and adverse-risk patients. A relatively high proportion of patients underwent alloSCT within our cohort, which may have improved the very poor outcome usually associated with the adverse risk group. Further work is needed to refine prognostication, particularly within the currently defined adverse risk group.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".