Timing matters: Detection of clonal hematopoiesis and its association with adverse outcomes in heart transplant recipients
Bibliographic record
Abstract
ABSTRACT Clonal hematopoiesis (CH) promotes inflammation and is associated with the development of cardiovascular disease. Previous studies assessing CH mutations in orthotopic heart transplant (OHT) recipients have revealed inconsistent findings, likely due to small sample size and differing sample collection time. In this study, we investigated the association between CH and post-transplant outcomes with a more consistent sample collection window. This retrospective study included 209 patients who underwent OHT between 2015 and 2022. Targeted sequencing detected CH mutations from samples obtained within a window of six months before or after transplantation. Clinical data were collected from the electronic medical record. Patients undergoing OHT had a median age of 53 years, and 27% were female. CH-associated mutations with a variant allele frequency (VAF) greater than 2% were detected in 29 patients (13.9%). The commonly mutated genes included DNMT3A, TET2, and ASXL1. CH mutations were associated with an increased risk of antibody-mediated rejection (AMR) (HR 2.42, 95% CI 1.07-5.47, p=0.033), but without detected differences in mortality or cardiac allograft vasculopathy (CAV). CH mutations detected at the time of transplant were associated with clinically significant AMR. Sample analysis at the time of transplant provides the clearest association between CH mutations and outcomes in OHT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".