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Poster

A Machine Learning Model Based on Liver Histology Predicts the Hepatic Venous Pressure Gradient in Patients With Compensated Cirrhosis Due to Nonalcoholic Steatohepatitis

AASLD 2020

Study Background

♦ The hepatic venous pressure gradient (HVPG) reliably measures portal pressure, including the presence of clinically significant portal hypertension (CSPH; HVPG ≥10 mm Hg)

♦ CSPH is associated with an increased risk of hepatic decompensation and mortality

♦ Measurement and interpretation of HVPG require expertise, with up to 30% of HVPG readings deemed inaccurate

♦ We previously utilized a machine learning (ML) research platform (PathAI, Inc., Boston, MA) to quantify fibrosis and other histologic features of nonalcoholic steatohepatitis (NASH), and characterize the heterogeneity of fibrosis in patients with cirrhosis
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Authors

Bosch et al.