Cardiovascular Disease Outcomes Related to Early Stage Renal Impairment After Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: In the general population, even mild renal disease is associated with increased cardiovascular (CV) complications. Whether this is true in liver transplant recipients (LTR) is unknown. METHODS: This was a retrospective cohort study of 671 LTR (2002-2012) from a large urban tertiary care center and 37 322 LTR using Vizient hospitalization data linked to the United Network for Organ Sharing. The 4-variable Modification of Diet in Renal Disease equation estimated glomerular filtration rate (eGFR). Outcomes were 1-year CV complications (death/hospitalization from myocardial infarction, heart failure, atrial fibrillation, cardiac arrest, pulmonary embolism, or stroke) and mortality. Latent mixture modeling identified trajectories in eGFR in the first liver transplantation (LT) year in the 671 patients. RESULTS: Mean (SD) eGFR was 72.1 (45.7) mL/min per 1.73 m. Six distinct eGFR trajectories were identified in the local cohort (n = 671): qualitatively normal-slow decrease (4% of cohort), normal-rapid decrease (4%), mild-stable (18%), mild-slow decrease (35%), moderate-stable (30%), and severe-stable (9%). In multivariable analyses adjusted for confounders and baseline eGFR, the greatest odds of 1-year CV complications were in the normal-rapid decrease group (odds ratio, 10.6; 95% confidence interval, 3.0-36.9). Among the national cohort, each 5-unit lower eGFR at LT was associated with a 2% and 5% higher hazard of all-cause and CV-mortality, respectively (P < 0.0001), independent of multiple confounders. CONCLUSIONS: Even mild renal disease at the time of LT is a risk factor for posttransplant all-cause and CV mortality. More rapid declines in eGFR soon after LT correlate with risk of adverse CV outcomes, highlighting the need to study whether early renal preservation interventions also reduce CV complications.
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 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) | 0.000 | 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 | 0.001 |
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 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".