Enhancing ACLF prediction by integrating sarcopenia assessment and frailty in liver transplant candidates on the waiting list
Bibliographic record
Abstract
Background & Aims: 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. Methods: 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. Results: 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. Conclusions: 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. Impact and implications: The 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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".