Improved Outcomes with Myeloablative Conditioning in AML Patients below 65 Years Using a Combination of Antithymocyte Globulin and Post-Transplantation Cyclophosphamide As Gvhd Prophylaxis
Bibliographic record
Abstract
Background: Allogeneic hematopoietic cell transplantation (Allo-HCT) is a critical treatment option for achieving long-term remission in patients with acute myeloid leukemia (AML), particularly those with intermediate or high-risk disease. Although myeloablative conditioning (MAC) improves disease control and reduces relapse rates, resulting in a survival advantage, it is also associated with increased non-relapse mortality. This study compares the outcomes of myeloablative (MAC) versus reduced-intensity conditioning (RIC) in AML patients undergoing Allo-HCT, all of whom received a unified GVHD prophylaxis regimen of a combination of anti-thymocyte globulin (ATG) and post-transplant cyclophosphamide (PTCy). Methods: We conducted a retrospective analysis of 322 AML patients who underwent Allo-HCT at Princess Margaret Cancer Centre between October 2015 and October 2023. Patients were under the age of 65 and received a standardized GVHD prophylaxis regimen that included ATG, PTCy, and cyclosporine. The primary outcomes assessed were overall survival (OS), relapse-free survival (RFS), GVHD-free/RFS (GRFS), acute and chronic GVHD, and graft failure. Results: Of the 322 patients, 207 received RIC (Fludarabine 35 mg/m²/day for 4 days + Busulfan 3.2 mg/kg/day for 2 days + TBI 200), while 115 received MAC (Fludarabine 35 mg/m²/day for 4 days + Busulfan 3.2 mg/kg/day for 4 days). The median age was significantly lower in the MAC group compared to the RIC group (46 years vs. 57 years, p<0.001). Matched unrelated donors (MUD) were the predominant donor type (53.7%), followed by haplo-donors (20.2%), mismatched unrelated donors (MMUD) (13.4%), and matched related donors (MRD) (12.7%). A greater proportion of patients in the RIC group had a hematopoietic cell transplant comorbidity index (HCT-CI) ≥3 (44.1% vs. 18.3%, p<0.001). There was a trend favoring MAC for 2-year OS (72.5% vs. 63.8%, p=0.09) over RIC. The 1-year NRM did not differ significantly between the RIC and MAC groups (14.3% vs. 13.5%, p=0.96). The 2-year relapse rate was significantly higher in the RIC group compared to the MAC group (29% vs. 18%, p=0.04). The incidence of acute GVHD grade I-IV by day 100 was higher in the MAC group than in the RIC group (43.5% vs. 30%, p=0.01). However, there were no significant differences between the groups for acute GVHD grade II-IV (22.6% vs. 15.9%, p=0.23) or grade III-IV (5.2% vs. 5.3%, p=0.84). The 2-year incidence of chronic GVHD was similar between the groups (MAC 16.3% vs. RIC 22.6%, p=0.30). Consequently, the 2-year GRFS was comparable between MAC and RIC (54.8% vs. 49%, p=0.31). Regarding graft failure, there was no significant difference between the groups (MAC 2.6% vs. RIC 1.9%, p=0.68). The incidence of bloodstream (BSI) post-HCT was also similar (MAC 55.7% vs. RIC 44%, p=0.18). Notably, the RIC group had a significantly higher risk of CMV reactivation compared to the MAC group (50.4% vs. 34.8%, p=0.009). Conclusion: Our data suggest that though MAC was associated with less relapse rate, it was not found to be associated with superior overall survival or GRFS while using a combination of ATG and PTCy, that effectively manages the risk of higher-grade acute and chronic GVHD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".