Simonetto, Douglas A.

Outcomes of kidney-alone transplantation (KAT) in former simultaneous liver-kidney (SLK) transplant candidates after liver waitlist delisting are not well defined. Using Scientific Registry of Transplant Recipients (SRTR) data, we identified adult recipients who underwent KAT between 2005 and 2025 after prior SLK listing (RLD-KAT), which was matched 1:5 to primary KAT recipients without liver disease by age, transplant year, and diabetes status. Patient and graft survival were evaluated using the Kaplan-Meier method and multivariable Cox proportional hazards models. We identified 163 RLD-KAT recipients matched to 815 controls. Unadjusted Kaplan-Meier analyses demonstrated lower patient survival in RLD-KAT (p = 0.020), whereas death-censored and overall graft survival were similar (p > 0.05). In multivariable models, death-censored graft survival (HR 0.89; 95% CI 0.51–1.53), overall graft survival (HR 1.00; 95% CI 0.73–1.36), and patient survival (HR 1.07; 95% CI 0.76–1.5) did not differ significantly. Six RLD-KAT recipients (3.7%) subsequently underwent liver transplantation. Kidney-alone transplantation in prior SLK candidates was associated with acceptable patient and graft outcomes and a low incidence of subsequent liver transplantation.

Background: Telomeres are repetitive sequences at the end of linear chromosomes that preserve genetic information. Patients with telomere biology disorders (TBDs) are subject to multiorgan manifestations, which include the liver. However, due to the rare nature of the disorder, these manifestations are not fully characterized. Methods: Here, we describe the hepatic manifestations of patients with TBD at a large tertiary center within a specialized TBD clinic; 121 consecutive patients with TBD seen from 2017 to 2024 were included. The median age of the TBD diagnosis was 43 years old, with 60% male. The most common pathogenic/likely pathogenic variant was TERT (25%), followed by RTEL1 (10%) and TERC (7%). Results: Evidence of hepatic involvement in TBD was observed in 60% of patients (73 of 121), as determined by imaging studies or abnormal liver chemistries. Hepatic steatosis was the most prevalent imaging finding (56%), while a cholestatic pattern of liver injury was the predominant laboratory abnormality (73%). Biopsy-proven portosinusoidal vascular disorder was present in 4 patients. Findings of portal hypertension were observed in 25 patients (34%), with decompensation observed in 12 patients (16%). Traditional liver imaging modalities (computer tomography, ultrasound, and MRI) were compared with magnetic resonance elastography in 41 patients. Of these, 7 patients had abnormal magnetic resonance elastography with normal traditional imaging (17%), indicating higher sensitivity of magnetic resonance elastography to detect liver involvement. Conclusions: This study describes the liver manifestations within a cohort of patients with TBD with findings of portosinusoidal vascular disorder and hepatopulmonary syndrome similar to other cohorts and a noted higher prevalence of hepatic steatosis as compared with the general population. Liver stiffness measurements, integrated with imaging and laboratory testing, further improve characterization of hepatic manifestations in TBD.

Ascites development in cirrhosis reduces 5-year survival from 80% to 30%, yet substantial heterogeneity exists among patients with comparable ascites severity. This multicenter retrospective cohort study included adult liver transplant candidates with ascites at 4 United States centers (2015–2024). Latent class analysis was performed using 7 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 the University of California San Francisco and Columbia University was randomly split 80/20 for model development and internal validation. External validation was performed at the University of Southern California (n=59) and Mayo Clinic (n=93). Primary outcomes were acute kidney injury (AKI) and waitlist mortality over 365 days. Three phenotypes emerged: “CKD-Metabolic” (30.4%, lowest eGFR 76.5 mL/min/1.73 m 2 ), “Vasodilatory-Synthetic Dysfunction” (38.9%, highest bilirubin 7.03 mg/dL, lowest blood pressure 117.5 mmHg), and “PVT-Intermediate” (30.7%, severe thrombocytopenia 68.1×10 3 /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, p <0.001) and validation cohorts (HR 3.39, 95% CI 1.93–5.94, p <0.001). Competing risk analysis revealed no mortality differences, but Vasodilatory-Synthetic Dysfunction was associated with higher transplantation rates (validation: HR 1.63, 95% CI 1.19–2.22, p =0.002). Three reproducible clinical phenotypes of ascites were identified with distinct risk profiles. The Vasodilatory-Synthetic Dysfunction phenotype was associated with a 3-fold increase in the risk of AKI that persisted after adjustment for MELD 3.0. No differences in waitlist mortality were observed, likely reflecting effective MELD-based transplant allocation that preferentially removes the highest-acuity patients from the waitlist.

Artificial intelligence (AI) has emerged as a transformative force in liver transplantation (LT), spanning patient selection, donor–recipient matching, intraoperative management, and post-transplant care. Historically, applications have relied on traditional machine learning (ML) and deep learning (DL) techniques trained on structured clinical datasets to improve risk stratification and outcome prediction. Since 2023, however, advances in generative AI (GenAI) and large language models (LLMs) have expanded the scope of AI in LT, enabling multimodal reasoning and the extraction of clinically meaningful information from unstructured data. This review synthesizes recent AI-driven developments across the LT patient journey, from advanced chronic liver disease management to long-term post-transplant outcomes. Representative applications include ML-based models to optimize treatment selection and waitlist prioritization for hepatocellular carcinoma, random forest and gradient-boosting approaches to improve donor utilization and predict graft survival, and DL-based imaging models for graft volumetry and steatosis assessment. Post-transplant innovations encompass neural network models for graft injury classification, recurrent architectures for immunosuppression dosing and fibrosis prediction, multi-task frameworks for complication risk stratification, and integrative omics-based approaches to detect rejection and dysfunction. Emerging GenAI applications include LLM-enabled digital phenotyping of social determinants of health and multi-agent simulations of transplant selection committees. Despite rapid progress, translation into routine practice remains limited by challenges in local validation, workflow integration, and infrastructure requirements. Future opportunities lie in incorporating underutilized data domains, such as psychosocial factors, -omics, and intraoperative physiologic streams, and leveraging GenAI to enhance clinical decision support, research scalability, and patient–clinician communication. AI is poised not only to refine prediction but to reshape the conceptual and operational landscape of liver transplantation.