Splenectomy as Flow Modulation Strategy and Risk Factors of De Novo Portal Vein Thrombosis in Adult‐to‐Adult Living Donor Liver Transplantation
Bibliographic record
Abstract
Portal vein thrombosis (PVT) is a severe complication after liver transplantation that can result in increased morbidity and mortality. Few data are available regarding risk factors, classification, and treatment of PVT after living donor liver transplantation (LDLT). Between January 2004 and November 2014, 421 adult-to-adult LDLTs were performed at our institution, and they were included in the analysis. Perioperative characteristics and outcomes from patients with no-PVT (n = 393) were compared with those with de novo PVT (total portal vein thrombosis [t-PVT]; n = 28). Ten patients had early portal vein thrombosis (e-PVT) occurring within 1 month, and 18 patients had late portal vein thrombosis (l-PVT) appearing later than 1 month after LDLT. Analysis of perioperative variables determined that splenectomy was associated with t-PVT (hazard ratio [HR], 3.55; P = 0.01), e-PVT (HR, 4.96; P = 0.04), and l-PVT (HR, 3.84; P = 0.03). In contrast, donor age was only found as a risk factor for l-PVT (HR, 1.05; P = 0.01). Salvage rate for treatment in e-PVT and l-PVT was 100% and 50%, respectively, without having an early event of rethrombosis. Mortality within 30 days did not show a significant difference between groups (no-PVT, 2% versus e-PVT, 10%; P = 0.15). No significant differences were found regarding 1-year (89% versus 92%), 5-year (79% versus 82%), and 10-year (69% versus 79%) graft survival between the t-PVT and no-PVT groups, respectively (P = 0.24). The 1-year (89% versus 96%), 5-year (82% versus 86%), and 10-year (79% versus 83%) patient survival was similar for the patients in the no-PVT and t-PVT groups, respectively (P = 0.70). No cases of graft loss occurred as a direct consequence of PVT. In conclusion, the early diagnosis and management of PVT after LDLT can lead to acceptable early and longterm results without affecting patient and graft survival.
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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 | 0.001 |
| 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) | 0.001 | 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".