Building of Prognosis Model for Patients With Cirrhosis Based on Sarcopenia Assessed by Deep Learning

Официальное название

Building of Prognosis Model for Patients With Cirrhosis Based on Sarcopenia in Assessment With the Technology of Deep Learning

Обзор

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.

Подробное описание

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.

Первичные конечные точки

• Liver-related mortality [Срок оценки: As of December 31, 2025]

Вторичные конечные точки (1)

• All-cause Mortality [Срок оценки: As of December 31, 2025]

Критерии участия

Критерии включения

• 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

Критерии исключения

• 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Публикации

• 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

Идентификаторы

NCT: NCT06531200 · 2023PHB214-001

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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