Derivation and Validation of a Model to Predict Clinically Significant Portal Hypertension Using Transient...

Area of Science:

• Hepatology
• Non-invasive diagnostics
• Liver disease biomarkers

Background:

• Liver biopsy and HVPG are invasive, costly, and time-consuming for assessing advanced fibrosis and CSPH.
• There is a need for less invasive methods to diagnose these conditions.

Purpose of the Study:

• To evaluate the combination of FIB-4 and LSM for identifying advanced fibrosis (AF) and CSPH.
• To develop and validate a novel predictive model for CSPH.

Main Methods:

• Retrospective analysis of 142 chronic liver disease patients.
• Comparison of FIB-4 and LSM with liver histology for AF diagnosis.
• Comparison of FIB-4, LSM, and platelet count with HVPG for CSPH diagnosis.
• Development and validation of a predictive model (PHAT) using logistic regression.

Main Results:

• LSM plus FIB-4 showed the highest AUROC (0.8155) for predicting CSPH.
• The novel Portal Hypertension Assessment Tool (PHAT) model, using LSM, FIB-4, and gender, achieved AUROCs of 0.8293 (internal) and 0.7899 (external) for CSPH prediction.
• PHAT score ≥-2.76 predicted CSPH with 94% sensitivity and 99% negative predictive value.

Conclusions:

• A model combining FIB-4, LSM, and gender can effectively identify CSPH in patients with chronic liver disease.
• This non-invasive approach offers a promising alternative to traditional diagnostic methods.