Related Experiment Video

Updated: Mar 19, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Risk stratification of decompensated cirrhosis patients by Chronic Liver Failure Consortium scores: Classification

Yu Shi1, Zheyue Shu2, Wenjie Sun3

  • 1State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Hepatology Research : the Official Journal of the Japan Society of Hepatology
|June 12, 2016
PubMed

Insights

The Chronic Liver Failure (CLIF) Consortium scores accurately predict mortality in decompensated cirrhosis patients. These scores offer better risk stratification than traditional methods like MELD and CP, aiding clinical decisions.

Area of Science:

  • Hepatology
  • Prognostic Biomarkers
  • Clinical Risk Stratification

Background:

  • Decompensated cirrhosis prognosis varies significantly.
  • Accurate risk stratification is crucial for patient management.
  • Existing scoring systems may lack precision.

Purpose of the Study:

  • To evaluate the prognostic performance of CLIF Consortium scores.
  • To stratify mortality risk in patients with and without acute-on-chronic liver failure (ACLF).
  • To compare CLIF scores against established prognostic models.

Main Methods:

  • Validated CLIF Consortium acute-on-chronic liver failure (CLIF-C ACLFs) and CLIF Consortium Acute Decompensation (CLIF-C ADs) scores.
  • Utilized a cohort of 209 ACLF and 1245 non-ACLF patients.
  • Employed classification and regression tree (CRT) analysis for risk stratification.

Main Results:

  • CLIF-C ACLFs and CLIF-C ADs demonstrated superior predictive accuracy over MELD, MELD-Na, and CP scores.
  • CRT analysis successfully stratified patients into distinct risk groups.
  • The CRT model showed improved prognostic prediction across all patient groups.

Conclusions:

  • CLIF-C ACLF and CLIF-C AD scores are more effective than MELD, MELD-Na, and CP for predicting mortality.
  • Combined use of CLIF scores aids in identifying high-risk cirrhosis patients.
  • These scores can assist in clinical decision-making for patient management.
Abstract

Keywords:
CLIF Consortium score prognosisclassification and regression tree analysisdecompensated cirrhosisrisk stratification

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

845

Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
544
Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test01:22

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test

In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess...
248
Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow01:26

Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow

Chronic liver disease significantly impacts drug metabolism due to alterations in hepatic blood flow and enzyme accessibility. This disruption affects the body's pharmacokinetics—the movement and processing of drugs within the system. Key enzymes crucial for metabolizing medications become less accessible, changing how drugs are processed and utilized. Furthermore, liver disease influences the synthesis of plasma proteins, such as albumin and globulins, which play critical roles in drug...
324
Esophageal Varices-II: Clinical Features and Management01:28

Esophageal Varices-II: Clinical Features and Management

Esophageal varices often manifest as gastrointestinal bleeding episodes, presenting symptoms like hematemesis (vomiting of blood), hematochezia (passing fresh blood via the rectum), and melena (black, tarry stools). Other signs can include weight loss, anorexia, abdominal discomfort, jaundice, pruritus, altered mental status, and muscle cramps.
In the initial assessment, a thorough review of the patient's medical history is vital to identify risk factors such as liver disease, alcohol...
731