Pre-Transplant Left Ventricular Geometry and Major Adverse Cardiovascular Events After Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND Preventing major adverse cardiovascular events (MACE) after kidney transplantation motivates pre-transplant cardiac evaluation that includes two-dimensional transthoracic echocardiography (TTE). The relationship of relative wall thickness (RWT) to left ventricular mass index (LVMI) in predicting post-transplant MACE is unclear. MATERIAL AND METHODS In this multi-ethnic Canadian single-center cohort study, we identified 1063 adults undergoing pre-transplant TTE within 1 year pre-transplant and with minimum 6 months of post-kidney transplant follow-up for MACE, defined as a composite of coronary revascularization, myocardial infarction, stroke, and cardiac death. Left ventricular hypertrophy (LVH, >131 g/m² in men and >100 g/m² in women) and increased RWT (>0.45) were a priori used to define normal (no LVH, normal RWT), concentric remodeling (no LVH, increased RWT), eccentric hypertrophy (LVH, normal RWT), and concentric hypertrophy (LVH, increased RWT). RESULTS There were 134 MACE over 3577 patient-years of post-transplant follow-up. Both LVH (HR 1.58, p=0.022) and high RWT (HR 1.44, p=0.041) predicted MACE in multivariate survival regression analysis independently of common pre-transplant MACE risk factors. Concentric remodeling, concentric hypertrophy, and eccentric hypertrophy all increased the risk for MACE (4.44, 5.05, and 5.55 events per 100 patient-years, respectively) versus normal echocardiography (2.71 events per 100 patient-years, all p<0.05 for difference). In Cox interactive regression analysis, LVMI and RWT were independently associated with MACE (p=0.015, p=0.025) and significantly interacted (p=0.008). CONCLUSIONS LV geometric parameters beyond LVH alone can assist post-transplant prognostication in kidney transplant candidates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".