Low-Density Lipoprotein Cholesterol Level Trends and the Development of Cardiac Allograft Vasculopathy After Heart Transplantation
Bibliographic record
Abstract
BACKGROUND: Unlike the relationship with atherosclerotic coronary artery disease, that between low-density lipoprotein cholesterol (LDL-C) and cardiac allograft vasculopathy (CAV) is unclear. Our objectives were to characterize lipid profiles early after heart transplantation (HT) and evaluate the relationship between early LDL-C and the development of CAV. METHODS: We retrospectively reviewed consecutive adults who underwent HT at 2 centres during the time period 2010-2018. The primary outcome was the incidence of angiographic CAV. The relationship between LDL-C and CAV was assessed using Cox proportional hazards and logistic regression models adjusted a priori for clinically important covariates, including recipient and donor age, recipient sex, ischemic time, and pre-HT diabetes. RESULTS: < 0.01). Of 309 patients who underwent angiography, 54% had CAV. The risk of CAV did not vary according to baseline, 1-year, or change from baseline to 1-year LDL-C. The odds of CAV at 1 year were equally likely across LDL-C values (adjusted odds ratio 1.00, 95% confidence interval: 0.61-1.63 for baseline, and adjusted odds ratio 1.25, 95% confidence interval: 0.74-2.10 for 1-year LDL-C). CONCLUSIONS: No association was identified between early LDL-C and the development of CAV. Our findings do not support targeting a specific LDL-C for patients who do not otherwise meet criteria for guideline-recommended LDL-C target levels. Randomized studies are warranted to determine if lipid-lowering to a specific LDL-C target level modifies the risk of CAV.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".