Real-world treatment adherence and persistence of FLT3 inhibitors as post-alloHCT maintenance therapy in patients with AML in the United States: a cohort study using administrative claims data
Bibliographic record
Abstract
In FLT3-mutated (FLT3mut+) acute myeloid leukemia (AML), relapse after allogeneic hematopoietic cell transplantation (alloHCT) is the leading cause of treatment failure and mortality. We evaluated real-world adherence and persistence of FLT3-like–tyrosine kinase inhibitors as alloHCT maintenance in FLT3mut+ AML. Claims data were extracted from adults with AML with ≥1 alloHCT between January 2016 and June 2022 who received gilteritinib, midostaurin, or sorafenib as post-alloHCT maintenance. Adherence (PDC; days covered ≥80% during follow-up) and persistence (days receiving treatment without switch/gap >60 days) were assessed. Of 162 patients, 41, 53, and 68 received post-alloHCT gilteritinib, midostaurin, or sorafenib, respectively. Adherence was higher in patients with a history of relapsed/refractory disease before alloHCT (n = 106 [65.4%], p = .021). Although this study did not focus on outcomes, no significant differences in post-alloHCT relapse by PDC were found. Discontinuation risk was higher for midostaurin (HR = 2.79, p = .0005) and sorafenib (HR = 1.74, p = .046) versus gilteritinib in patients with Commercial insurance vs Medicare/Medicaid (HR = 1.68, p = .019).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".