A machine learning approach to non-invasive prediction of hepatic decompensation in compensated advanced chronic liver disease: the CIRI model.

BACKGROUND & AIMS: Hepatic venous pressure gradient (HVPG) is the gold standard for assessment of prognosis in compensated advanced chronic liver disease (cACLD), yet its invasiveness limits broad clinical use. We developed and validated the Cirrhosis Risk Identifier (CIRI) model, a machine-learning tool using 11 demographic and routine laboratory parameters trained to prognosticate first decompensation; and benchmarked its performance against HVPG, liver stiffness measurement (LSM), FIB-4 and MELD. METHODS: cACLD patients were identified in the U.S. Optum Clinformatics Data Mart (Optum CDM) for model development and internal validation and externally validated in a prospective European tertiary care cohort. The primary endpoint was first decompensation (ascites, encephalopathy, variceal bleeding) with HCC and death as competing events. RESULTS: CIRI was trained on 112,618 and internally validated on 18,852 cACLD patients in Optum CDM, with 210 HVPG-characterized cACLD patients (European cohort) included for external validation. In both Optum CDM and Europe, steatotic liver disease was the leading etiology (54%; 44%), with median follow-up of 18.5 months (IQR 7.2-40.4) and 27.5 months (21.2-34.8), respectively. In Optum CDM, CIRI showed higher 1- and 2-year time-dependent AUROCs (0.816, 0.815) than MELD and FIB-4 (both p<0.001). In Europe, AUROCs (0.836, 0.769) were comparable to HVPG (both p>0.900) and superior to LSM (both p<0.05). CIRI predicted decompensation independently (Optum CDM: adjusted subdistribution hazard ratio [aSHR]: 1.67, p<0.001; Europe: aSHR: 1.66, p=0.017) with a cutoff of ≥-8.25 identifying patients at comparable decompensation risk as HVPG ≥10mmHg (CSPH). CONCLUSION: The machine-learning-based CIRI model yielded robust prognostic performance and accurate risk stratification in cACLD patients. CIRI's discrimination of decompensation risk was comparable to that of HVPG, highlighting its potential future utility to non-invasively identify "at-risk" cACLD patients. CLINICAL TRIAL NUMBER: NCT03267615 IMPACT AND IMPLICATIONS: Hepatic decompensation marks the key clinical transition from compensated to decompensated advanced chronic liver disease and is associated with substantially worse prognosis. However, current risk assessment remains limited by the invasiveness of the gold-standard hepatic venous pressure gradient (HVPG) measurement and the infrastructure required for established non-invasive tests such as liver stiffness measurement (LSM). In this study, we developed and validated "Cirrhosis Risk Identifier" (CIRI), a machine-learning model based solely on routine laboratory and demographic data, which predicted first hepatic decompensation with performance comparable to HVPG and superior to widely used non-invasive tests including LSM, MELD and FIB-4. These findings are important for clinicians and researchers aiming to identify patients with cACLD who are at increased short-term risk of hepatic decompensation. Pending further prospective validation, CIRI could support scalable and repeatable risk stratification and help guide surveillance intensity and targeted strategies intended to prevent hepatic decompensation for high-risk patients.