Impact of First and Further Decompensation in Metabolic-Dysfunction Associated Compensated Advanced Chronic Liver Disease
Notice bibliographique
Résumé
BACKGROUND Metabolic dysfunction-associated steatotic liver disease (MASLD) currently stands as one of the foremost global health challenges, with a prevalence of 38% worldwide according to the most recent estimates [1] and with a concerning upward trend due to the parallel anticipated increasing of Diabetes and Obesity epidemic in the coming years [2]. There is a long-standing agreement that the first decompensation - defined as ascites, hepatic encephalopathy (HE), variceal bleeding, and jaundice- appears the pivotal event for patients’ prognosis and marks the transition from the compensated, also known as compensated advanced chronic liver disease (cACLD), to the decompensated stage of cirrhosis [3]. Although only a small fraction of patients dies following the first decompensation episode, the risk of developing further decompensation increases and the median survival dramatically decreases [4]. The occurrence of a further decompensation event - defined according to the Baveno VII Consensus [5] as either the recurrence of the initial event or the development of a second decompensation event - represents a crucial turning point in the natural history of the liver disease, markedly increasing the risk of liver-related death (LR-D) in those patients. AIM We assessed the cumulative incidence of first and further (acute and non-acute) decompensation and evaluated their impact on LR-D in patients with compensated advanced chronic liver disease (cACLD) due to metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS International multicenter retrospective study (17 centers) on 6,061 consecutive patients with clinical (LSM>10 kPa) or biopsy-proven (F3-F4 fibrosis) diagnosis of cACLD due to MASLD. First and further decompensation were defined according to Baveno VII criteria. Competing risk analyses estimated the cumulative incidence of first and further decompensations, treating liver-related death (LR-D), extra-hepatic death (EH-D), and liver transplantation (LT) as competing events. Cumulative Incidence Functions (CIFs) were compared using Gray’s test and stratified by decompensation type and cause of death. Time-to-event analyses were anchored at cACLD diagnosis (first decompensation) and at first decompensation (subsequent events), with 5-year CIFs reported. Cause-specific Cox models with time-dependent covariates assessed the impact of decompensations and HCC on LR-D. Multivariable models included age, sex, diabetes, and liver function markers when available. A seven-state multistate model estimated transitions from cACLD to better assess the clinical course of cACLD due to MASLD. Analyses were conducted in R (v4.3.3) using cmprsk, mstate, and related packages. RESULTS The cumulative incidence of the first decompensation was 3.5% (95% C.I 3.0-4.1) at 5 years, increasing 19-fold the risk of LR-D using Cox analysis (Figure 1A); the cumulative incidence of further decompensation was 43.9% (95% C.I 37.2-50.2) at 5 years among patients with first decompensation (Figure 1A), additionally increasing 1.5-times the risk of LR-D. Ascites, followed by variceal bleeding, were the most common events in both first and further decompensation. Hepatocellular carcinoma (HCC) further independently increased the risk of LR-D by 3- and 1.4-fold in the whole cohort of cACLD due to MASLD and in those who experienced first decompensation, respectively. CONCLUSIONS The first and further decompensations represent tipping points in the clinical course of patients with cACLD due to MASLD, increasing 19-times and additionally 1.5-times the risk of LR-D. HCC is an independent predictor of LR-D in patients with cACLD due to MASLD, resulting in an additional risk of LR-D when associated with both first and further decompensation.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 0,000 |
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 tête enseignante, 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 ».