Cardiovascular magnetic resonance in the diagnosis of acute heart transplant rejection: a review
Bibliographic record
Abstract
BACKGROUND: Screening for organ rejection is a critical component of care for patients who have undergone heart transplantation. Endomyocardial biopsy is the gold standard screening tool, but non-invasive alternatives are needed. Cardiovascular magnetic resonance (CMR) is well suited to provide an alternative to biopsy because of its ability to quantify ventricular function, morphology, and characterize myocardial tissue. CMR is not widely used to screen for heart transplant rejection, despite many trials supporting its use for this indication. This review summarizes the different CMR sequences that can detect heart transplant rejection as well as the strengths and weaknesses of their application. RESULTS: T2 quantification by spin echo techniques has been criticized for poor reproducibility, but multiple studies show its utility in screening for rejection. Human and animal data estimate that T2 quantification can diagnose rejection with sensitivities and specificities near 90%. There is also a suggestion that T2 quantification can predict rejection episodes in patients with normal endomyocardial biopsies.T1 quantification has also shown association with biopsy proven rejection in a small number of trials. T1 weighted gadolinium early enhancement appeared promising in animal data, but has had conflicting results in human trials. Late gadolinium enhancement in the diagnosis of rejection has not been evaluated.CMR derived measures of ventricular morphology and systolic function have insufficient sensitivity to diagnose mild to moderate rejection. CMR derived diastolic function can demonstrate abnormalities in allografts compared to native human hearts, but its ability to diagnose rejection has not yet been tested.There is promising animal data on the ability of iron oxide contrast agents to illustrate the changes in vascular permeability and macrophage accumulation seen in rejection. Despite good safety data, these contrast agents have not been tested in the human heart transplant population. CONCLUSION: T2 quantification has demonstrated the best correlation to biopsy proven heart transplant rejection. Further studies evaluating diastolic function, late gadolinium enhancement, and iron oxide contrast agents to diagnose rejection are needed. Future studies should focus on combining multiple CMR measures into a transplant rejection scoring system which would improve sensitivity and possibly reduce, if not eliminate, the need for endomyocardial biopsy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".