ArticleJHEP reports : innovation in hepatology2020
Body composition predicts mortality and decompensation in compensated cirrhosis patients: A prospective cohort study.
Article in JHEP reports : innovation in hepatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 6 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 6 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Effect of adipose-related parameters on mortality in patients with liver cirrhosis: a meta-analysis.Annals of medicine · 2025Pooled it
- The impact of sarcopenia and frailty on decompensation in compensated cirrhosis: A systematic review.Hepatology communications · 2025Pooled it
- Prognostic biomarkers for predicting decompensation in alcoholic and non-alcoholic patients with compensated cirrhosis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Systematic review and meta-analysis of biomarkers predicting decompensation in patients with compensated cirrhosis.BMJ open gastroenterology · 2024Pooled it
- Systematic review: development of a consensus code set to identify cirrhosis in electronic health records.Alimentary pharmacology & therapeutics · 2022Pooled it
- Malnutrition, Frailty, and Sarcopenia in Patients With Cirrhosis: 2021 Practice Guidance by the American Association for the Study of Liver Diseases.Hepatology (Baltimore, Md.) · 2021Guideline
- Changes in Muscle Mass and the Risk of Liver-Related Events: A Prospective Cohort Study.Diabetes, obesity & metabolism · 2026Article
- Subcutaneous Fat Density Is an Independent Predictor of Adverse Outcomes and Shows Early Improvement After Liver Transplantation.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Automated CT-derived visceral-to-subcutaneous fat ratio as a prognostic imaging biomarker for mortality in sepsis.European radiology · 2026Article
- Article
- Sarcopenia Is Associated With Mortality and Progression of Cirrhosis in Decompensated but Not Compensated Disease-A Multicentre Study.Alimentary pharmacology & therapeutics · 2026Article
- Noninvasive Tests for Predicting Decompensation in Compensated Advanced Chronic Liver Disease: A Comprehensive Review.Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- Review
- Bioelectrical impedance analysis-derived phase angle predicts osteosarcopenia and mortality in patients with chronic liver disease.Scientific reports · 2025Article
- MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients.BMC medical imaging · 2025Article
- Advances in the diagnosis and management of clinically significant portal hypertension in cirrhosis: A narrative review.World journal of hepatology · 2025Review
- Sina Score as a New Machine Learning-Derived Online Prediction Model of Mortality for Cirrhotic Patients Awaiting Liver Transplantation: A Prospective Cohort Study.Journal of clinical medicine · 2025Article
- Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.Clinical transplantation · 2025Article
- Role of tumor-specific and whole-body imaging biomarkers for prediction of recurrence in patients with stage III colorectal cancer.Abdominal radiology (New York) · 2025Observational
- Hepatic cirrhosis and decompensation: Key indicators for predicting mortality risk.World journal of hepatology · 2025Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
BACKGROUND &
aimsBody composition, particularly sarcopenia, is associated with mortality in patients with decompensated cirrhosis undergoing transplant evaluation. Similar data are limited for non-transplant eligible or compensated patients.
methodsA total of 274 patients with cirrhosis were followed prospectively for ≤5 years after a CT scan. We utilized Analytic Morphomics® to measure body composition (fat, muscle, and bone) which was rendered into relative values (percentiles) in relation to a reference population. The model for end-stage liver disease (MELD) score was used as a reference model for survival prediction. We validated our models in a separate cohort.
resultsOur cohort had a mean Child-Pugh score of 7.0 and a mean MELD of 11.3. The median follow-up time was 5.05 years. The proportion of patients alive at 1, 3 and 5 years was 86.5%, 68.0%, and 54.3%; 13 (4.6%) underwent liver transplantation. Child-Pugh B/C (
conclusionThe addition of body composition features to predictive models improves the prospective determination of prognosis in patients with cirrhosis, particularly those with compensated disease. Fat density, a novel feature, is associated with the risk of decompensation. LAY SUMMARY: Am I at high risk of getting sicker and dying? This is the key question on the mind of patients with cirrhosis. The problem is that we have very few tools to help guide our patients, particularly if they have early cirrhosis (without symptoms like confusion or fluid in the belly). We found that how much muscle and fat the patient has and what that muscle or fat looks like on a CT scan provide helpful information. This is important because many patients have CT scans and this information is hiding in plain sight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.