The Challenges With the Cardiac Evaluation of Liver and Kidney Transplant Candidates
Bibliographic record
Abstract
Cardiovascular events are among the leading cause of mortality in kidney and liver transplant recipients. Thus, screening for cardiovascular disease and risk stratification for cardiovascular events constitute an important part of the pretransplant evaluation. In this review, we first summarize current guidelines in the cardiac risk assessment of kidney and liver transplant candidates. We then elaborate on the limitations of these guidelines, summarize the current knowledge gaps, and narrow down a spectrum of 6 themes that serve as challenges to research and practice development. This spectrum pertains to understanding the disease itself, which is challenging due to the altered cardiac physiology in these patients and current guidelines that do not adequately account for nonischemic diseases and events. We then describe the challenges in assessing these patients, their symptoms, and individualizing their risk of cardiovascular events with a special consideration for nontraditional risk factors. We also explore the limitations of the current and novel diagnostic tests and the lack of evidence of therapeutic efficacy in intervening in patients with asymptomatic disease. The transplant procedure itself can be a potential modifiable risk factor for cardiovascular events, that is, surgical technique, type of donor, and induction immunosuppression. Lastly, we describe the potential issues with the current literature when defining cardiac diseases and events across different studies and shortcomings of extrapolating data from the nontransplant literature. We conclude by proposing research and practice implications of our discussion and that there is a need for evidence to guide the revision of current guidelines.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".