Enhancing ACLF prediction by integrating sarcopenia assessment and frailty in liver transplant candidates on the waiting list
Notice bibliographique
Résumé
•The correlations between the assessment tools for sarcopenia and frailty were poor.•MELD-Na, sarcopenia by SMI, and LFI were independent predictors of ACLF in patients on the WL.•The predictive model MELD-Na-sarcopenia-LFI achieved a C-statistic for the prediction of ACLF of 0.85.•The CLIF-C ACLF score correlated with WL mortality, whereas baseline parameters for physiological functional reserve did not. Background & AimsMalnutrition, sarcopenia, and frailty are prevalent in cirrhosis. We aimed to assess the correlation between assessment tools for malnutrition, sarcopenia, and frailty in patients on the liver transplant (LT) waiting list (WL), and to identify a predictive model for acute-on-chronic liver failure (ACLF) development.MethodsThis prospective single-center study enrolled consecutive patients with cirrhosis on the WL for LT (May 2019-November 2021). Assessments included subjective global assessment, CT body composition, skeletal muscle index (SMI), ultrasound thigh muscle thickness, sarcopenia HIBA score, liver frailty index (LFI), hand grip strength, and 6-minute walk test at enrollment. Correlations were analyzed using Pearson's correlation. Competing risk regression analysis was used to assess the predictive ability of the liver- and functional physiological reserve-related variables for ACLF.ResultsA total of 132 patients, predominantly with decompensated cirrhosis (87%), were included. Our study revealed a high prevalence of malnutrition (61%), sarcopenia (61%), visceral obesity (20%), sarcopenic visceral obesity (17%), and frailty (10%) among participants. Correlations between the assessment tools for sarcopenia and frailty were poor. Sarcopenia by SMI remained prevalent when frailty assessments were not usable. After a median follow-up of 10 months, 39% of the patients developed ACLF on WL, while 28% experienced dropouts without ACLF. Multivariate analysis identified MELD-Na, SMI, and LFI as independent predictors of ACLF on the WL. The predictive model MELD-Na-sarcopenia-LFI had a C-statistic of 0.85.ConclusionsThe poor correlation between sarcopenia assessment tools and frailty underscores the importance of a comprehensive evaluation. The SMI, LFI, and MELD-Na independently predicted ACLF development in WL. These findings enhance our understanding of the relationship between sarcopenia, frailty, and ACLF in patients awaiting LT, emphasizing the need for early detection and intervention to improve WL outcomes.Impact and implicationsThe relationship between sarcopenia and frailty assessment tools, as well as their ability to predict acute-on-chronic liver failure (ACLF) in patients on the liver transplant (LT) waiting list (WL), remains poorly understood. Existing objective frailty screening tests have limitations when applied to critically ill patients. The correlation between sarcopenia and frailty assessment tools was weak, suggesting that they may capture different phenotypes. Sarcopenia assessed by skeletal muscle index, frailty evaluated using the liver frailty index, and the model for end-stage liver disease-Na score independently predicted the development of ACLF in patients on the WL. Our findings support the integration of liver frailty index and skeletal muscle index assessments at the time of inclusion on the WL for LT. This combined approach allows for the identification of a specific patient subgroup with an increased susceptibility to ACLF, underscoring the importance of early implementation of targeted treatment strategies to improve outcomes for patients awaiting LT. Malnutrition, sarcopenia, and frailty are prevalent in cirrhosis. We aimed to assess the correlation between assessment tools for malnutrition, sarcopenia, and frailty in patients on the liver transplant (LT) waiting list (WL), and to identify a predictive model for acute-on-chronic liver failure (ACLF) development. This prospective single-center study enrolled consecutive patients with cirrhosis on the WL for LT (May 2019-November 2021). Assessments included subjective global assessment, CT body composition, skeletal muscle index (SMI), ultrasound thigh muscle thickness, sarcopenia HIBA score, liver frailty index (LFI), hand grip strength, and 6-minute walk test at enrollment. Correlations were analyzed using Pearson's correlation. Competing risk regression analysis was used to assess the predictive ability of the liver- and functional physiological reserve-related variables for ACLF. A total of 132 patients, predominantly with decompensated cirrhosis (87%), were included. Our study revealed a high prevalence of malnutrition (61%), sarcopenia (61%), visceral obesity (20%), sarcopenic visceral obesity (17%), and frailty (10%) among participants. Correlations between the assessment tools for sarcopenia and frailty were poor. Sarcopenia by SMI remained prevalent when frailty assessments were not usable. After a median follow-up of 10 months, 39% of the patients developed ACLF on WL, while 28% experienced dropouts without ACLF. Multivariate analysis identified MELD-Na, SMI, and LFI as independent predictors of ACLF on the WL. The predictive model MELD-Na-sarcopenia-LFI had a C-statistic of 0.85. The poor correlation between sarcopenia assessment tools and frailty underscores the importance of a comprehensive evaluation. The SMI, LFI, and MELD-Na independently predicted ACLF development in WL. These findings enhance our understanding of the relationship between sarcopenia, frailty, and ACLF in patients awaiting LT, emphasizing the need for early detection and intervention to improve WL outcomes.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».