Magnetic Resonance Elastography-derived Stiffness Predicts Renal Function Loss and Is Associated With Microvascular Inflammation in Kidney Transplant Recipients
Bibliographic record
Abstract
Background. Organ stiffening can be caused by inflammation and fibrosis, processes that are common causes of transplant kidney dysfunction. Magnetic resonance elastography (MRE) is a contrast-free, noninvasive imaging modality that measures kidney stiffness. The objective of this study was to assess the ability of MRE to serve as a prognostic factor for renal outcomes. Methods. Patients were recruited from the St Michael’s Hospital Kidney Transplant Clinic. Relevant baseline demographic, clinical, and Banff histologic information, along with follow-up estimated glomerular filtration rate (eGFR) data, were recorded. Two-dimensional gradient-echo MRE imaging was performed to obtain kidney “stiffness” maps. Binary logistic regression analyses were performed to examine for relationships between stiffness and microvascular inflammation score. Linear mixed-effects modeling was used to assess the relationship between stiffness and eGFR change over time controlling for other baseline variables. A G2-likelihood ratio Chi-squared test was performed to compare between the baseline models with and without “stiffness.” Results. Sixty-eight transplant kidneys were scanned in 66 patients (mean age 56 ± 12 y, 24 females), with 38 allografts undergoing a contemporaneous biopsy. Mean transplant vintage was 7.0 ± 6.8 y. In biopsied allografts, MRE-derived allograft stiffness was associated only with microvascular inflammation (Banff g + ptc score, Spearman ρ = 0.43, P = 0.01), but no other histologic parameters. Stiffness was negatively associated with eGFR change over time (Stiffness × Time interaction β = –0.80, P < 0.0001), a finding that remained significant even when adjusted for biopsy status and baseline variables (Stiffness × Time interaction β = –0.46, P = 0.04). Conversely, the clinical models including “stiffness” showed significantly better fit (P = 0.04) compared with the baseline clinical models without “stiffness.” Conclusions. MRE-derived renal stiffness provides important prognostic information regarding renal function loss for patients with allograft dysfunction, over and above what is provided by current clinical variables.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".