Blood-based Vienna 3P/5P risk models accurately predict first hepatic decompensation in compensated advanced chronic...

Invasive measurement of hepatic venous pressure gradient (HVPG) is the gold standard for diagnosing clinically significant portal hypertension (CSPH, i.e. HVPG ≥10 mmHg), which indicates an increased risk of decompensation. We evaluated the blood-based Vienna 3P/5P models for non-invasive assessment of portal hypertension (PH) severity and their prognostic value. Their performance was compared to HVPG, liver stiffness measurement (LSM) and the ANTICIPATE±NASH model.

Patients with compensated advanced chronic liver disease (cACLD) who underwent HVPG measurement and LSM within the prospective VICIS (Vienna Cirrhosis Study) were included. We assessed the ability of each model to detect CSPH and severe PH (HVPG ≥16 mmHg), predict decompensation, and stratify risk. Outcome prediction was further validated in an external cohort.

Among 266 patients with diverse etiologies of cACLD, median HVPG was 11 (8-16) mmHg with a CSPH and severe PH prevalence of 62.8% and 25.6%, respectively. The 3P/5P models correlated with HVPG (both p <0.001), achieving AUROCs of 0.704 (5P) for CSPH and 0.800 (5P) for severe PH prediction. During a median follow-up of 23.9 (15.3-32.6) months, 48 (18%) patients decompensated. HVPG and the 5P model showed similar time-dependent AUROCs (at 0.5 and 1 year: 0.753-0.822), superior to ANTICIPATE±NASH (AUROCs: 0.689-0.691) and LSM (AUROCs: 0.621-0.636). The 5P (adjusted subdistribution hazard ratio [aSHR]: 1.32, p <0.001) and 3P (aSHR: 1.15, p = 0.010) models predicted decompensation independent from age, sex, LSM, etiological cure and non-selective beta blocker use. Proposed cut-offs for the 3P/5P models distinguished between patients at low and high risk of decompensation (Grays test p <0.001).

The blood-based 3P/5P models demonstrated significant prognostic value for predicting hepatic decompensation and identifying patients with cACLD at high risk. Importantly, the 5P model performed comparably to HVPG.

This study addresses the clinical need for accessible, reliable, and cost-effective non-invasive tools to predict hepatic decompensation in patients with compensated advanced chronic liver disease, given the limited availability of hepatic venous pressure gradient and liver stiffness measurement. By demonstrating that the Vienna 3P/5P models - machine learning tools based solely on routine laboratory parameters - achieve comparable prognostic accuracy to hepatic venous pressure gradient and outperform other non-invasive tools, such as liver stiffness measurement or the ANTICIPATE±NASH model, these findings have significant implications for clinicians providing care for patients with compensated advanced chronic liver disease. The models' simplicity, repeatability and wide availability could facilitate timely risk stratification and improved clinical management across diverse healthcare settings.

NCT03267615.