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Prognostic and diagnostic scoring models in acute alcohol-associated hepatitis: A review comparing the performance of
Jad Mitri1, Mohammad Almeqdadi2, Raffi Karagozian3
1Department of Medicine, Saint Elizabeth's Medical Center, Boston, MA 02135, United States.
Insights
Accurately predicting prognosis in alcohol-associated hepatitis (AAH) is crucial due to its high mortality. This review examines various scoring models, including Maddrey discriminant function (MDF) and MELD, to assess AAH outcomes.
Area of Science:
- Hepatology
- Internal Medicine
- Clinical Prognostication
Background:
- Alcohol-associated hepatitis (AAH) is a severe liver disease requiring accurate prognostic assessment.
- Existing scoring systems like Maddrey discriminant function (MDF) and Model for End-Stage Liver Disease (MELD) are used for prognostication.
- AAH management necessitates reliable tools to predict outcomes and guide treatment, including corticosteroids.
Purpose of the Study:
- To review and compare the performance and limitations of various scoring models for predicting alcohol-associated hepatitis mortality.
- To highlight the most effective prognostic tools for AAH.
- To discuss predictors of alcohol relapse and complications like acute kidney injury (AKI).
Main Methods:
- Review of established and emerging prognostic scoring systems for AAH.
- Analysis of clinical features, laboratory markers, and histologic data in AAH prognostication.
- Evaluation of artificial intelligence (AI) models for AAH outcome prediction.
Main Results:
- Maddrey discriminant function (MDF) and Model for End-Stage Liver Disease (MELD) are key prognostic scores, with MELD potentially offering higher accuracy.
- The Lille score assesses treatment response to corticosteroids.
- Clinical assessment and laboratory markers are often sufficient for diagnosis and severity grading, reducing the need for liver biopsy in prognosis.
Conclusions:
- Accurate prognostication in AAH is vital for guiding treatment decisions and improving patient outcomes.
- A combination of established scores (MDF, MELD, Lille) and emerging AI models aids in predicting AAH mortality.
- Predicting complications like AKI and alcohol relapse is essential for comprehensive AAH management.
Abstract:
Alcohol-associated hepatitis (AAH) is a severe form of liver disease caused by alcohol consumption. In the absence of confounding factors, clinical features and laboratory markers are sufficient to diagnose AAH, rule out alternative causes of liver injury and assess disease severity. Due to the elevated mortality of AAH, assessing the prognosis is a radical step in management. The Maddrey discriminant function (MDF) is the first established clinical prognostic score for AAH and was commonly used in the earliest AAH clinical trials. A MDF > 32 indicates a poor prognosis and a potential benefit of initiating corticosteroids. The model for end stage liver disease (MELD) score has been studied for AAH prognostication and new evidence suggests MELD may predict mortality more accurately than MDF. The Lille score is usually combined to MDF or MELD score after corticosteroid initiation and offers the advantage of assessing response to treatment a 4-7 d into the course. Other commonly used scores include the Glasgow Alcoholic Hepatitis Score and the Age Bilirubin international normalized ratio Creatinine model. Clinical AAH correlate adequately with histologic severity scores and leave little indication for liver biopsy in assessing AAH prognosis. AAH presenting as acute on chronic liver failure (ACLF) is so far prognosticated with ACLF-specific scoring systems. New artificial intelligence-generated prognostic models have emerged and are being studied for use in AAH. Acute kidney injury (AKI) is one possible complication of AAH and is significantly associated with increased AAH mortality. Predicting AKI and alcohol relapse are important steps in the management of AAH. The aim of this review is to discuss the performance and limitations of different scoring models for AAH mortality, emphasize the most useful tools in prognostication and review predictors of recurrence.
