Outcomes of Allogeneic Hematopoietic Stem Cell Transplantation in Adult Acute Myeloid Leukemia: A Systematic Review
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative treatment for adult acute myeloid leukemia (AML), but its outcomes are influenced by donor selection, conditioning regimens, and patient-specific factors. This systematic review synthesizes recent evidence on survival, relapse, non-relapse mortality (NRM), and prognostic factors to guide clinical decision-making. A comprehensive search of PubMed, Scopus, Web of Science, Embase, and ClinicalTrials.gov (January 2020-August 2025) identified 10 studies meeting the inclusion criteria. Studies were evaluated for outcomes (overall survival (OS), disease-free survival (DFS), relapse, and NRM) and risk of bias using the Newcastle-Ottawa Scale (NOS) and Cochrane Risk of Bias (RoB) 2 tool. Haploidentical HSCT improved one-year survival (77.9% vs. 62.0% with chemotherapy) in elderly AML, with lower relapse (16.5% vs. 56.6%). Matched sibling donors (MSD) yielded superior two-year OS (62.4%) compared to unrelated/haploidentical donors, though relapse rates were comparable. Reduced-intensity conditioning (RIC) increased NRM in patients ≥70 years, while nonmyeloablative regimens improved tolerability. Transplantation in complete remission (CR) was critical (five-year OS: 58% vs. 6% for non-CR). Autologous HSCT outperformed allo-HSCT in acute promyelocytic leukemia (APL) complete remission 2 (CR2) (82.4% vs. 64.3% two-year OS). Allo-HSCT benefits intermediate/poor-risk AML, with MSD and CR transplantation offering optimal outcomes. Haploidentical HSCT is viable for older patients lacking matched donors. Future research should prioritize prospective trials to refine donor selection and conditioning strategies.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".