Machine learning-derived clinical phenotypes of ascites in cirrhosis: A multi-center latent class analysis with external validation
Giuseppe Cullaro, Jennifer C. Lai, Taryn Liu, Miguel E. Gomez, Douglas A. Simonetto, Sean Lee, Brian P. Lee, Andrew S. Allegretti, Nikhilesh R. Mazumder, Kavish R. Patidar, Elizabeth C. Verna
Background: Ascites development in cirrhosis reduces five-year survival from 80% to 30%, yet substantial heterogeneity exists among patients with comparable ascites severity. Methods: This multicenter retrospective cohort study included adult liver transplant candidates with ascites at four United States centers (2015-2024). Latent class analysis was performed using seven clinical variables: ascites grade, bilirubin, albumin, platelet count, estimated glomerular filtration rate, systolic blood pressure, and portal vein thrombosis. The derivation cohort (n=625) from University of California San Francisco and Columbia University was randomly split 80/20 for model development and internal validation. External validation was performed at University of Southern California (n=59) and Mayo Clinic (n=93). Primary outcomes were acute kidney injury (AKI) and waitlist mortality over 365 days. Results: Three phenotypes emerged: “CKD-Metabolic” (30.4%, lowest eGFR 76.5 mL/min/1.73m²), “Vasodilatory-Synthetic Dysfunction” (38.9%, highest bilirubin 7.03 mg/dL, lowest blood pressure 117.5 mmHg), and “PVT-Intermediate” (30.7%, severe thrombocytopenia 68.1×10³/μL, 24.0% PVT). In validation cohorts (n=305), phenotypes demonstrated robust classification (mean posterior probability 0.823-0.855). Vasodilatory-Synthetic Dysfunction showed increased AKI risk versus CKD-Metabolic in derivation (MELD 3.0-adjusted HR 2.95, 95% CI 2.07-4.22,