Clinical Outcomes and Treatment Patterns in Adults With FLT3-ITDmut+ Acute Myeloid Leukemia Undergoing Allogeneic Hemopoietic Cell Transplantation in the United States and Canada
Bibliographic record
Abstract
Allogeneic hematopoietic cell transplantation (alloHCT) is used to treat patients with acute myeloid leukemia (AML) with internal tandem duplication of the FMS-like tyrosine kinase 3 gene (FLT3-ITDmut+). However, the effect of different characteristics on outcomes after transplant is not fully understood. To determine the impact of patient, disease, and transplant characteristics on clinical outcomes and trends in maintenance therapy for patients with FLT3-ITDmut+ AML who underwent their first alloHCT. This was an observational cohort study of adults ≥18 years who were recipients of human leukocyte antigen identical sibling, haploidentical, 8/8 or 7/8 unrelated, or cord blood donor alloHCT in the United States and Canada between 2014‒2019. Patient, disease, and transplant characteristics were collected from CIBMTR (Center for International Blood & Marrow Transplant Research) between 2014‒2022. Patients enrolled in the MORPHO clinical trial (NCT02997202) were excluded. Clinical outcomes were measured from the time of alloHCT by disease status: first complete remission (CR1), second or greater CR (≥CR2) or relapsed/refractory (R/R). The primary endpoints of this study were overall survival (OS) and leukemia-free survival (LFS). Key secondary endpoints included relapse after alloHCT, non-relapse mortality (NRM), time from diagnosis to complete remission, time from complete remission to alloHCT, and maintenance therapy before and after alloHCT. Univariate analyses were conducted with Gray's test and log-rank test, while multivariable analyses were conducted using Cox proportional hazards models. A total of 3147 eligible patients (CR1, n=2389; ≥CR2, n=340; R/R, n=418) were included. Most patient, disease, and transplant characteristics were similar between different disease statuses. In univariate analyses, disease status of CR1 compared with ≥CR2 or R/R was significantly (p<0.001) associated with improved OS and LFS, and decreased probability of relapse; NRM likely differed across cohorts after alloHCT (p=0.003). In multivariable analyses, patients with a disease status of ≥CR2 and R/R compared with CR1 had significantly shorter OS (hazard ratio [HR] 95% confidence interval [CI], 1.43 [1.19–1.72], p=0.0001, and 2.14 [1.88–2.44], p<0.0001, respectively). Patients with a disease status of CR1 at ≤2.6 months had better LFS compared with ≥CR2 and R/R (HR [95% CI], 2.03 [1.56–2.63], p<0.0001 and 3.98 [3.07–5.17], p<0.0001, respectively). Patients with a ≥CR2 or R/R disease status at ≤2.6 months had an increased likelihood of relapse compared with CR1 (HR [95% CI], 2.46 [1.82–3.33], p<0.0001 and 4.68 [3.46–6.34], p<0.0001, respectively). Disease status was not significantly associated with NRM. We also identified several additional patient, disease, and transplant characteristics that may be associated with inferior OS and/or LFS and greater relapse and/or NRM. Maintenance therapy usage after alloHCT increased from 2014 to 2019 primarily due to increased FLT3 inhibitor use. In this largest study to date of patients from the US and Canada with FLT3-ITDmut+ AML, disease status of CR1 at the time of alloHCT was associated with better clinical outcomes. Additional factors were identified that may also impact clinical outcomes, and in total, have the potential to inform clinical decision-making.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".