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Related Experiment Video

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Optimized Analysis of In Vivo and In Vitro Hepatic Steatosis

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Risk Prediction in Patients With Metabolic Dysfunction-Associated Steatohepatitis Using Natural Language Processing.

Jordan Guillot¹, Christopher Y K Williams¹, Shadera Azzam¹

¹Bakar Computational Health Sciences Institute, University of California, San Francisco, California.

Gastro Hep Advances

|July 21, 2025

Summary

This summary is machine-generated.

A novel natural language processing algorithm identified a real-world cohort of patients with metabolic dysfunction-associated steatohepatitis (MASH). This study highlights key comorbidities and lab values predicting MASH progression and mortality.

Keywords:

Cirrhosis Metabolic Dysfunction–Associated Steatotic Liver Disease Natural Language Processing Real-World Evidence

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Area of Science:

Hepatology
Medical Informatics
Public Health

Background:

Metabolic dysfunction-associated steatohepatitis (MASH) is a heterogeneous liver disease with significant implications for end-stage liver disease.
Understanding MASH progression in real-world populations is crucial for effective management and treatment development.
Current real-world data on MASH progression and associated mortality remains limited.

Purpose of the Study:

To define a real-world MASH cohort using natural language processing (NLP).
To identify significant associations between MASH and all-cause mortality.
To investigate predictors of progression to cirrhosis and liver transplantation.

Main Methods:

Development and validation of a novel NLP algorithm, "NASHDetection," to identify MASH patients.
Application of the algorithm to a patient cohort diagnosed between 2012 and 2022.
Utilized Cox regression with stepwise variable selection to analyze outcomes.

Main Results:

The NASHDetection algorithm achieved 86% accuracy in identifying 2695 MASH patients.
At diagnosis, 55.4% had cirrhosis, 34.0% decompensation, and 10.8% hepatocellular carcinoma.
Comorbidities like type 2 diabetes mellitus, heart failure, and peripheral artery disease, along with elevated LDL cholesterol and alkaline phosphatase, were associated with increased all-cause mortality.

Conclusions:

A real-world MASH cohort was characterized using a novel NLP approach.
Several comorbidities and laboratory markers were identified as potential predictors of MASH progression and mortality.
NLP-driven patient characterization can facilitate future interventional trials for MASH.