Cardiac magnetic resonance imaging in detection of progressive graft dysfunction in pediatric heart transplantation
Bibliographic record
Abstract
BACKGROUND: Chronic graft failure (CGF) in pediatric heart transplant (PHT) is multifactorial and may present with findings of fibrosis and microvessel disease (MVD) on endomyocardial biopsy (EMB). There is no optimal CGF surveillance method. We evaluated associations between cardiac magnetic resonance imaging (CMR) and historical/EMB correlates of CGF to assess CMR's utility as a surveillance method. METHODS: Retrospective analysis of PHT undergoing comprehensive CMR between September 2015 and January 2022 was performed. EMB within 6 months was graded for fibrosis (scale 0-5) and MVD (number of capillaries with stenotic wall thickening per field of view). Correlation analysis and logistic regression were performed. RESULTS: Forty-seven PHT with median age at CMR of 15.7 years (11.6, 19.3) and time from transplant of 6.4 years (4.1, 11.0) were studied. Cardiac allograft vasculopathy (CAV) was present in 11/44 (22.0%) and historical rejection in 14/41 (34.2%). CAV was associated with higher global T2 (49.0 vs. 47.0 ms; p = 0.038) and peak T2 (57.0 vs. 53.0 ms; p = 0.013) on CMR. Historical rejection was associated with higher global T2 (49.0 vs. 47.0 ms; p = 0.007) and peak T2 (57.0 vs. 53.0 ms; p = 0.03) as well as global extracellular volume (31.0 vs. 26.3%; p = 0.03). Higher fibrosis score on EMB correlated with smaller indexed left ventricular mass (rho = -0.34; p = 0.019) and greater degree of MVD with lower indexed left ventricular end-diastolic volume (rho = -0.35; p = 0.017). CONCLUSION: Adverse ventricular remodeling and abnormal myocardial characteristics on CMR are present in PHT with CAV, historical rejection, as well as greater fibrosis and MVD on EMB. CMR has the potential use for screening of CGF.
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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".