S1441 Serum Fibrosis and Steatosis Biomarkers for the Prediction of Mortality in Liver Transplant Recipients
Bibliographic record
Abstract
Introduction: Liver transplantation (LT) is a life-saving procedure that resolves complications of cirrhosis. However, the metabolic risk factors for nonalcoholic fatty liver disease (NAFLD) persist and potentially worsen in the post-transplant setting, thereby increasing the risk of liver fibrosis. Liver biopsy is the gold standard to diagnose NAFLD and liver fibrosis, but this procedure is invasive and less than ideal for longitudinal monitoring after LT. We aimed to investigate the association of serum steatosis and fibrosis biomarkers with mortality and graft loss in LT recipients. Methods: We included consecutive adults who received a liver transplant at the MUHC in 2014-2021 and were followed up annually. The outcomes measured were death and graft loss, graft loss being defined as graft failure leading to death or re-transplantation. We assessed the prognostic value of the biomarkers ALT, AST, GGT, AST-to-Platelet Ratio Index (APRI), fibrosis-4 index (FIB-4), and hepatic steatosis index (HSI). Hepatic steatosis was defined as HSI >36, and liver fibrosis was characterized as FIB-4 > 3.64 or APRI > 1. Survival analysis and Generalized Estimating Equation (GEE) models were used to assess the association between the biomarkers and the outcomes. Results: Two hundred nineteen patients were followed for 30 months on average. Graft loss and mortality occurred in 12 patients (9%) and 38 (29.5%) resulting in incidence rates of 29.5 (95% CI 20.9-40.6) and 9.6 (95% CI 5-16.8) per 100 person-years, respectively. Patients who died during the follow-up were older, had an older donor, and had a history of diabetes. On multivariable analysis using the GEE model (see Table 1), higher ALT and higher AST were associated with mortality after adjustment for sex, BMI, age, albumin, and platelets. In the time-to-event analysis, the Kaplan Meier curves showed that APRI >1 could be a potential predictor of mortality (P = 0.0729 see Figure 1). Neither FIB-4 (log-rank, P = 0.199) nor HSI (log-rank, P = 0.919) were associated with mortality. None of the biomarkers or liver transaminases were associated with graft loss. Conclusion: Liver transaminases and the serum fibrosis biomarker APRI are associated with mortality in LT recipients. The hepatic steatosis biomarker HSI does not seem to be valuable in predicting outcomes in this population. None among liver transaminases, steatosis or fibrosis biomarkers predicted graft loss. Table 1. - GEE of liver transaminases on death in LT patients Characteristics Univariate Multi-Variate * Odds Ratio 95% CI P-value Odds Ratio 95% CI P-value ALT Moderate/Severe 0.9999 0.9999–1.0000 0.0931 1.0001 1.0000–1.0002 0.0040 AST Moderate/Severe 1.0000 0.9999–1.0000 0.3934 1.0001 1.0000–1.0001 0.0037 ALP Moderate/Severe 0.9999 0.9996–1.0001 0.2807 1.0000 0.9998–1.0001 0.6113 GGT Moderate/Severe 0.9998 0.9996–1.0000 0.0236 0.9998 0.9997–1.0000 0.0596 Smoking status No 1.00 1.00 Yes 0.921 0.71–1.20 0.5444 0.934 0.74–1.18 0.5649 *multivariate models are adjusted for age, sex, albumin, BMI and platelets. Figure 1.: Kaplan-Meier survival curve in LT patients according to the APRI biomarker.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".