S1441 Serum Fibrosis and Steatosis Biomarkers for the Prediction of Mortality in Liver Transplant Recipients
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».