A Model Based on Computed Tomography Imaging Parameters to Predict Poor Survival in Patients With Liver Cirrhosis

• Conditions: Liver Cirrhosis

• Registration Number: NCT05208736
• Lead Sponsor: First Affiliated Hospital Xi'an Jiaotong University

Brief Summary

Cirrhosis, as the end stage of most chronic liver diseases, is an important clinical landmark portending high risk of death. Early identification and accurate prognostic scores is critical issue to improve survival rate. Loss of muscle mass and other body features, which can be determined from CT, have been associated with mortality in cirrhosis or hepatic carcinoma. In this study, we sought to investigate serial changes of CT imaging parameters, such as the skeletal muscle index (SMI), liver volume, adiposity density and so forth, to develop a new prognostic model for long-term motality in patients with liver cirrhosis. The final predictive model was developed under the Cox regression framework with MELD, Child-Pugh score, baseline and serial changes of CT imaging parameters. The discrimination of the new risk score was assessed by the overall C index.

Detailed Description

Cirrosis, which is always manifesting as ascites, hepatic encephalopathy, upper gastrointestinal bleeding, severe jaundice and coagulopathy, deteriorates rapidly with a high mortality. Early identification and accurate predicted model may be the key to make duly clinical decision and improve prognosis.

When hepatic disorders accur, cirrhosis is often accompanied by varying degrees of malnutrition, which is accurately reflexed by the cross-sectioned areas of the muscles at the third lumbar vertebra level normalized by the square of the height （L3-SMI）. In addition, previous studies have shown that lower density of adiposity and altered liver volume were often associated with adverse clinical events.

These changes in body composition can be accurately assessed and measured by using CT scans. However, there is no ideas whether these imaging patameters are reliable indicators to predict the mortality of cirrhosis. In this study, we aimed to evaluate the difference of serial changes of L3-SMI, the density of adiposity and liver volume between 5-year survival and non-survival group and develop a new prognostic model based on these CT imaging data. This study highlighted the clinical significance of serial changes of CT imaging data in predicting the long-term outcome of patients with cirrhosis for the first time.

Study Timeline

• Study Start: 2021-03-01
• Study First Submitted: 2022-01-17
• Study First Submitted QC: 2022-01-17
• Study First Posted: 2022-01-26
• Status Verified: 2022-04
• Last Update Submitted: 2022-04-14
• Last Update Posted: 2022-04-18
• Primary Completion: 2022-12-15
• Study Completion: 2023-03-15

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 400

Inclusion Criteria

1. Age 18-75 years
2. The diagnosis of liver cirrhosis based on imaging, laboratory and/or histological patameters from a board-certified hepatologist.
3. Patients who underwent at least two abdominal CT examinations, and the interval between the first and the second check was more than one year.
4. CT examination included the cross-sectional areas of muscle at L3 level.
5. Basic patient data included age, gender, weight, height, biochemical examination, complications and 5-year survival.
6. Biochemical examinations were available within 7 days before or after CT scan.

Exclusion Criteria

1. patients with liver cancer and other malignant tumors.
2. Patients accompanied by severe respiratory and circulatory diseases.
3. Patients had a history of previous transplantation.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Overall survival of subjects with cirrhosis	5 years	Overall survival of patients with liver cirrhosis will be summarized and compared with control subject within 5 years.

Trial Locations

Locations (2)

First Affiliated Hospital Xi'an Jiaotong University; 🇨🇳 Xi'an, Shannxi, China

First Affiliated Hospital, Xi'an Jiaotong University; 🇨🇳 Xi'an, Shannxi, China