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Purpose of Review <p>Accurate prognostication in cirrhosis is essential for clinical management, transplant evaluation, and palliative planning. This review outlines traditional and emerging prognostic tools, emphasizing the roles of serum albumin, hepatic encephalopathy, and machine learning models.</p> Recent Findings <p>Serum albumin is an independent predictor of outcomes, reflecting both hepatic synthetic function and nutritional status. Traditional models such as MELD have been updated to incorporate albumin and sex, improving accuracy and addressing transplant disparities. Newer tools, including the VOCAL-Penn score, enable more precise risk stratification in specific clinical settings such as perioperative assessment. Although hepatic encephalopathy was historically included in the Child-Pugh score, its subjective nature limited use; newer methods now aim to quantify and integrate encephalopathy more reliably.</p> Summary <p>Incorporating specific complications of cirrhosis and biomarkers of multisystem dysfunction enhances prognostic precision. Emerging machine learning–based models offer promising opportunities for individualized risk prediction, potentially improving patient outcomes and equitable access to care.</p>

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Novel Approaches to Cirrhosis Prognostication – Recognizing the Role of Albumin, Hepatic Encephalopathy, and Machine-Learning Models

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Kaitlin Payne,
  • Ki Jung Lee,
  • Mohammad Almeqdadi

摘要

Purpose of Review

Accurate prognostication in cirrhosis is essential for clinical management, transplant evaluation, and palliative planning. This review outlines traditional and emerging prognostic tools, emphasizing the roles of serum albumin, hepatic encephalopathy, and machine learning models.

Recent Findings

Serum albumin is an independent predictor of outcomes, reflecting both hepatic synthetic function and nutritional status. Traditional models such as MELD have been updated to incorporate albumin and sex, improving accuracy and addressing transplant disparities. Newer tools, including the VOCAL-Penn score, enable more precise risk stratification in specific clinical settings such as perioperative assessment. Although hepatic encephalopathy was historically included in the Child-Pugh score, its subjective nature limited use; newer methods now aim to quantify and integrate encephalopathy more reliably.

Summary

Incorporating specific complications of cirrhosis and biomarkers of multisystem dysfunction enhances prognostic precision. Emerging machine learning–based models offer promising opportunities for individualized risk prediction, potentially improving patient outcomes and equitable access to care.