Jothimani, Dinesh

Purpose of review Hepatorenal syndrome (HRS) is the extreme manifestation of acute renal dysfunction in patients with cirrhosis, resulting from systemic vasodilation and renal vasoconstriction, leading to a reduction in the glomerular filtration rate. Recent findings The diagnostic criteria redefined diagnostic criteria for HRS-AKI (Hepatorenal syndrome -Acute kidney injury) as our understanding of disease progression has improved. This understanding of the HRS disease process guides current management, with the combination of intravascular volume expansion and vasoconstrictor therapy being the cornerstone of treatment. In addition to treatment selection, timing of treatment initiation, duration, dosage, and monitoring during treatment are decisive factors in HRS outcomes. Pathogenesis of HRS-AKI may be different in patients with acute-on-chronic liver failure (ACLF) in comparison to decompensated cirrhosis. Unfortunately, outcomes remain poor with medical therapies alone and a high risk of disease recurrence exists for HRS-AKI with persistence of the cirrhotic milieu. Liver transplantation remains the only definitive treatment of HRS-AKI. Given the critical nature of HRS-AKI, a multidisciplinary approach involving hepatologists, nephrologists, and intensive care physicians is essential for optimal clinical management. Summary Patients with HRS-AKI should be managed ideally in tertiary care centers and referred for liver transplantation promptly when eligible.

INTRODUCTION: Nosocomial infections (NIs) in cirrhosis are associated with high mortality but could be preventable. Logistic regression (LR) models have failed to identify high-risk patients. We aimed to develop machine learning (ML) models to predict NI. METHODS: The CLEARED consortium consists of prospectively enrolled cirrhosis inpatients from >120 centers. Using day-of-admission clinical data, 3 ML approaches (random forest [RF], extreme gradient boosting, and neural networks [NNs]) were used to predict NI. Data were split 80:20 for training and testing stratified by the outcome. Models were compared using area under the receiver operating characteristic curve (AUC). RESULTS: In total, 8,263 patients (55.90 ± 13.34 years; 64.1% men) from 127 centers in 37 countries were included. NI developed in 869 (10.5%), a median of 6 (4–11) days of postadmission. Major NIs were respiratory (29.6%) and urinary tract infection (15.7%), and spontaneous bacterial peritonitis (13.5%). NIs occurred more frequently in patients from low/low-middle income countries and those with severe liver disease, alcohol etiology, and admission infections. NIs were associated with inpatient mortality (31.9% vs 8.1%, P < 0.001) and liver transplantation (4.8% vs 1.9%, P < 0.001). Although the RF model (AUC 0.69) showed good calibration (Brier score 0.09), outperforming extreme gradient boosting, neural network, and LR models (AUC 0.66 for all; LR comparison P = 0.043), no model achieved AUC ≥0.80 for clinical utility. At 10% predicted probability threshold, the RF model demonstrated only 75.4% sensitivity, 52.9% specificity, and 15.9% positive predictive value (PPV). DISCUSSION: NIs cannot be accurately predicted from day-of-admission data using ML models, even in a large, prospective, global cirrhosis cohort. Every hospitalized patient with cirrhosis should receive protocolized infection control measures.

Small-for-size syndrome (SFSS) is a well-recognized complication following liver transplantation (LT), with up to 20% developing this following living donor LT (LDLT). Preventing SFSS involves consideration of factors before the surgical procedure, including donor and recipient selection, and factors during the surgical procedure, including adequate outflow reconstruction, graft portal inflow modulation, and management of portosystemic shunts. International Liver Transplantation Society, International Living Donor Liver Transplantation Group, and Liver Transplant Society of India Consensus Conference was convened in January 2023 to develop recommendations for the prediction and management of SFSS in LDLT. The format of the conference was based on the Grading of Recommendations, Assessment, Development, and Evaluation system. International experts in this field were allocated to 4 working groups (diagnosis, prevention, anesthesia, and critical care considerations, and management of established SFSS). The working groups prepared evidence-based recommendations to answer-specific questions considering the currently available literature. The working group members, independent panel, and conference attendees served as jury to edit and confirm the final recommendations presented at the end of the conference by each working group separately. This report presents the final statements and evidence-based recommendations provided by working group 2 that can be implemented to prevent SFSS in LDLT patients.