The Adverse Event Landscape of Stem Cell Transplant: Evidence for AGVHD Driving Early Transplant Associated Toxicities
Bibliographic record
Abstract
Although unrelated-donor (URD) hematopoietic cell transplantation (HCT) is associated with many toxicities, a detailed analysis of adverse events, as defined by the Common Terminology Criteria for Adverse Events (CTCAE), has not previously been curated. This represents a major unmet need, especially as it relates to assessing the safety of novel agents. We analyzed a detailed AE database from the "ABA2" randomized, double-blind, placebo-controlled clinical trial of abatacept for acute graft-versus-host disease (AGVHD) prevention, for which the FDA mandated a detailed AE assessment through Day +180, and weekly neutrophil and platelet counts through Day +100. These were analyzed for their relationship to key transplant outcomes, with a major focus on the impact of AGVHD on the development/severity of AEs. A total of 2102 AEs and 1816 neutrophil/platelet counts were analyzed from 142 8/8-HLA-matched URD HCT recipients on ABA2 (placebo cohort, n = 69, abatacept cohort, n = 73). This analysis resulted in 2 major observations. (1) Among graft source, conditioning intensity, age, and Grade 2 to 4 AGVHD, only AGVHD impacted Grade 3 to 5 AE acquisition after the first month post-transplant. (2) The development of Grade 3 to 4 AGVHD was associated with thrombocytopenia. We have created a detailed resource for the transplant community by which to contextualize clinical toxicities after transplant. It has identified AGVHD as a major driver of post-HCT Grade 3 to 5 AEs, and underscored a link between AGVHD and thrombocytopenia. This establishes a critical safety framework upon which the impact of novel post-transplant AGVHD therapeutics should be evaluated. This trial was registered at www.clinicaltrials.gov (#NCT01743131).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".