Cardiovascular disease assessment and management in liver transplantation
Bibliographic record
Abstract
The prevalence and mortality related to end-stage liver disease (ESLD) continue to rise globally. Liver transplant (LT) recipients continue to be older and have inherently more comorbidities. Among these, cardiac disease is one of the three main causes of morbidity and mortality after LT. Several reasons exist including the high prevalence of associated risk factors, which can also be attributed to the rise in the proportion of patients undergoing LT for metabolic dysfunction-associated steatohepatitis (MASH). Additionally, as people age, the prevalence of now treatable cardiac conditions, including coronary artery disease (CAD), cardiomyopathies, significant valvular heart disease, pulmonary hypertension, and arrhythmias rises, making the need to treat these conditions critical to optimize outcomes. There is an emerging body of literature regarding CAD screening in patients with ESLD, however, there is a paucity of strong evidence to support the guidance regarding the management of cardiac conditions in the pre-LT and perioperative settings. This has resulted in significant variations in assessment strategies and clinical management of cardiac disease in LT candidates between transplant centres, which impacts LT candidacy based on a transplant centre's risk tolerance and comfort level for caring for patients with concomitant cardiac disease. Performing a comprehensive assessment and understanding the potential approaches to the management of ESLD patients with cardiac conditions may increase the acceptance of patients, who appear too complex, but rather require extra evaluation and may be reasonable candidates for LT. The unique physiology of ESLD can profoundly influence preoperative assessment, perioperative management, and outcomes associated with underlying cardiac pathology, and requires a thoughtful multidisciplinary approach. The strategies proposed in this manuscript attempt to review the latest expert experience and opinions and provide guidance to practicing clinicians who assess and treat patients being considered for LT. These topics also highlight the gaps that exist in the comprehensive care of LT patients and the need for future investigations in this field.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".