Antiplatelet Prophylaxis Reduces the Risk of Early Hepatic Artery Thrombosis Following Liver Transplantation in High-Risk Patients
Bibliographic record
Abstract
The prevention of hepatic artery thrombosis (HAT) is pivotal for graft survival immediately after liver transplantation (LT). This study aimed to identify risk factors (RF) for early HAT (eHAT) and assess the benefit of antiplatelet prophylaxis (AP). This retrospective single-center study included 836 adult patients who underwent LT between 2007 and 2022. AP was administered for 3 months in N = 127 patients for surgical reasons. In total, 836 patients underwent LT, of whom 5.5% developed eHAT. In multivariable analysis, arterial anastomotic redo (aHR = 4.33), arterial reconstruction (aHR = 3.72) and cryptogenic liver cirrhosis (aHR = 4.25) were independent RFs for eHAT and AP appeared to be protective (aHR = 0.18). Indeed, in patients with at least one RF who received AP (RF+AP+, n = 94), the eHAT rate was significantly lower (3.2% vs. 21.3%, p < 0.001) than in those with RF who did not receive AP (RF+AP−, n = 89). The effect was even more pronounced when focusing on surgical RF alone (i.e., redo and/or reconstruction) with an additional improvement in 1 year graft survival of 85.3% vs. 70.4%, p = 0.02. AP did not pose an increased risk of bleeding. In conclusion, the main RFs for eHAT include arterial anastomotic redo, arterial reconstruction and cryptogenic liver cirrhosis as LT indications. Our results suggest that AP may protect against eHAT development in these high-risk patients.
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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 | 0.001 | 0.002 |
| 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 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".