Younger Matched Unrelated Donors Confer Decreased Relapse Risk Compared to Older Sibling Donors in Older Patients with B Cell Acute Lymphoblastic Leukemia Undergoing Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Although allogeneic hematopoietic cell transplantation (alloHCT) offers cure for older patients with acute lymphoblastic leukemia (ALL), disease relapse remains a major issue. Whether matched sibling donors (MSDs) are still the preferred donor choice compared to younger matched unrelated donors (MUDs) in the contemporary era of improved transplantation practices remains unknown. This retrospective cohort registry study queried the Center for International Blood and Marrow Transplant Research (CIBMTR) database in patients with B cell ALL (B-ALL) age ≥ 50 years undergoing alloHCT from older MSDs (age ≥ 50 years) or younger MUDs (age ≤ 35 years) between 2011 and 2018. The study included common allograft types, conditioning regimens, and graft-versus-host disease (GVHD) prophylaxis strategies. The primary outcome was relapse risk, and secondary outcomes included nonrelapse mortality (NRM), GVHD, leukemia-free survival (LFS), and overall survival (OS). Among 925 eligible patients in the study cohort, 386 underwent alloHCT with an older MSD (median donor age, 58 years) and 539 underwent alloHCT from a younger MUD (median donor age, 25 year). In multivariable analysis, younger MUDs conferred a significantly decreased risk of relapse (hazard ratio [HR], .68; P = .002) compared with older MSDs. The adjusted cumulative incidence of relapse at 5 years was significantly lower with younger MUDs than with older MSDs (26% versus 37%; P = .001). Younger MUDs were associated with a greater risk of chronic GVHD compared to older MSDs (HR, 1.33; 95% confidence interval [CI], 1.10 to 1.61; P = .003). Compared to older MSDs, younger MUDs conferred an increased NRM (HR, 1.38; P = .02) and higher adjusted cumulative incidence of NRM at 5 years (31% versus 22%; P = .006). There were no differences in post-alloHCT OS or LFS rates between younger MUDs and older MSDs (OS: HR, 1.09; [P = .37]; LFS: HR, .95 [P = .57]). The use of younger MUDs could be considered as a possible way to prevent relapse after alloHCT in older adults with ALL. Combining the use of younger MUDs with improved strategies to reduce GVHD merits further exploration to improve outcomes.
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 | 0.002 |
| 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) | 0.002 | 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".