Building of Prognosis Model for Patients With Cirrhosis Based on Sarcopenia Assessed by Deep Learning
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Cirrhosis, Liver, Sarcopenia. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Building of Prognosis Model for Patients With Cirrhosis Based on Sarcopenia in Assessment With the Technology of Deep Learning
Overview
The goal of this observational study is to develop and validate a fully automated imaging deep learning platform for the evaluation of sarcopenia in liver cirrhosis. Based on this model, a new prognostic model for liver cirrhosis incorporating imaging biomarkers such as sarcopenia will be constructed, and its predictive performance will be validated.
Detailed description
The goal of this observational study is to collect clinical and abdominal imaging data of patients with liver cirrhosis. The collected imaging data will be used as a model development set to develop, test, and internally validate a fully automated imaging deep learning platform for the evaluation of sarcopenia in liver cirrhosis. Subsequently, relevant data from patients with liver cirrhosis at other centers will be collected and used as an external validation dataset. The model will be externally validated by abdominal radiology experts. Furthermore, we will include sociodemographic information, clinical data, imaging data, and clinical outcomes of the aforementioned liver cirrhosis patients to predict the prognosis of these patients using the established model. This model will be used to construct a new prognostic model for liver cirrhosis incorporating imaging biomarkers such as sarcopenia, and its predictive performance will be validated.
Primary outcome measures
- Liver-related mortality [Time frame: As of December 31, 2025]
Secondary outcome measures (1)
- All-cause Mortality [Time frame: As of December 31, 2025]
Eligibility criteria
Inclusion criteria
- Age ≥18 years
- Diagnosis of liver cirrhosis, meeting at least one of the following criteria:
- Clinical diagnosis: ICD-10-CM codes K74.100 and K74.607 from our hospital's electronic medical record system
- Liver biopsy pathology or a combination of clinical, laboratory, and imaging examinations confirming liver cirrhosis: Pathological biopsy criteria: fibrosis bridging between lobules leading to lobular structural disarray, nodular regeneration of hepatocytes, formation of pseudo-lobules
- Laboratory tests: the presence of at least 2 of the following 4 abnormal indicators suggesting liver cirrhosis:
- a) Platelet count < 100×10\^9/L, with no other explainable cause;
- b) Serum albumin < 35g/L, excluding malnutrition or kidney disease as other causes;
- c) International normalized ratio (INR) > 1.3 or prolonged prothrombin time (PT) (after discontinuation of thrombolytic or anticoagulant drugs for more than 7 days);
- d)Aspartate aminotransferase to platelet ratio index (APRI) > 2.
- Availability of high-quality L3-level CT images
Exclusion criteria
- Incomplete sociodemographic, laboratory, or imaging data
- Diagnosed or highly suspected malignancy
- Severe chronic kidney disease, respiratory insufficiency, cardiovascular diseases, etc.
- Neurological diseases and muscular degenerative diseases
- Hyperthyroidism, hypothyroidism, tuberculosis, or any other diseases that may affect basal metabolism
- Diseases or conditions causing malabsorption of intestinal nutrients, such as inflammatory bowel disease or gastrointestinal surgery
- Treatment with glucocorticoids or immunosuppressants
- Pregnancy or lactation
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Publications
- Huang DQ, Terrault NA, Tacke F, Gluud LL, Arrese M, Bugianesi E, Loomba R. Global epidemiology of cirrhosis - aetiology, trends and predictions. Nat Rev Gastroenterol Hepatol. 2023 Jun;20(6):388-398. doi: 10.1038/s41575-023-00759-2. Epub 2023 Mar 28. PMID 36977794
- Zeng X, Shi ZW, Yu JJ, Wang LF, Luo YY, Jin SM, Zhang LY, Tan W, Shi PM, Yu H, Zhang CQ, Xie WF. Sarcopenia as a prognostic predictor of liver cirrhosis: a multicentre study in China. J Cachexia Sarcopenia Muscle. 2021 Dec;12(6):1948-1958. doi: 10.1002/jcsm.12797. Epub 2021 Sep 14. PMID 34520115
- Desai AP, Mohan P, Nokes B, Sheth D, Knapp S, Boustani M, Chalasani N, Fallon MB, Calhoun EA. Increasing Economic Burden in Hospitalized Patients With Cirrhosis: Analysis of a National Database. Clin Transl Gastroenterol. 2019 Jul;10(7):e00062. doi: 10.14309/ctg.0000000000000062. PMID 31343469
- Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019 Jun 29;393(10191):2636-2646. doi: 10.1016/S0140-6736(19)31138-9. Epub 2019 Jun 3. PMID 31171417
- Damluji AA, Alfaraidhy M, AlHajri N, Rohant NN, Kumar M, Al Malouf C, Bahrainy S, Ji Kwak M, Batchelor WB, Forman DE, Rich MW, Kirkpatrick J, Krishnaswami A, Alexander KP, Gerstenblith G, Cawthon P, deFilippi CR, Goyal P. Sarcopenia and Cardiovascular Diseases. Circulation. 2023 May 16;147(20):1534-1553. doi: 10.1161/CIRCULATIONAHA.123.064071. Epub 2023 May 15. PMID 37186680
- Sinn DH, Kang D, Kang M, Guallar E, Hong YS, Lee KH, Park J, Cho J, Gwak GY. Nonalcoholic fatty liver disease and accelerated loss of skeletal muscle mass: A longitudinal cohort study. Hepatology. 2022 Dec;76(6):1746-1754. doi: 10.1002/hep.32578. Epub 2022 Jun 10. PMID 35588190
- Han E, Lee YH, Kim BK, Park JY, Kim DY, Ahn SH, Lee BW, Kang ES, Cha BS, Han KH, Kim SU. Sarcopenia is associated with the risk of significant liver fibrosis in metabolically unhealthy subjects with chronic hepatitis B. Aliment Pharmacol Ther. 2018 Aug;48(3):300-312. doi: 10.1111/apt.14843. Epub 2018 Jun 19. PMID 29920701
- Davuluri G, Welch N, Sekar J, Gangadhariah M, Alsabbagh Alchirazi K, Mohan ML, Kumar A, Kant S, Thapaliya S, Stine M, McMullen MR, McCullough RL, Stark GR, Nagy LE, Naga Prasad SV, Dasarathy S. Activated Protein Phosphatase 2A Disrupts Nutrient Sensing Balance Between Mechanistic Target of Rapamycin Complex 1 and Adenosine Monophosphate-Activated Protein Kinase, Causing Sarcopenia in Alcohol-Assoc PMID 32799332
Identifiers
NCT: NCT06531200 · 2023PHB214-001