George, Jacob

ABSTRACT Background and Aims It remains unclear whether different factors influence 30‐ and 90‐day readmission rates. We identified predictors of 90‐day mortality in a global cohort of hospitalised patients with cirrhosis. Methods Variables associated with 30‐ and 90‐day post‐discharge readmission, liver transplantation (LT), and mortality were compared in prospectively enrolled, non‐electively hospitalised adult patients with cirrhosis. Results We enrolled 4208 patients from 125 centres across 37 countries. Alcohol‐related liver disease was the most common aetiology of cirrhosis (40%), followed by hepatitis B virus infection (21%); 47% were readmitted, 8% received liver transplantation (LT), and 23% died within 90 days post‐discharge. In multivariable analysis, country income was significantly associated with both 30‐ and 90‐day readmission rates, the receipt of LT, and mortality within 30 and 90 days post‐discharge (all p ‐values < 0.001). Independent predictors of higher rates of 30‐ and 90‐day readmissions, respectively, were disease severity ( p < 0.001, p = 0.003), prior ascites ( p = 0.025, p = 0.016), prior hospitalisation ( p < 0.001), hyponatremia ( p < 0.001, p = 0.008), and in‐hospital vasopressor use ( p = 0.002, p = 0.011). Prior variceal bleeding independently predicted higher 90‐day readmission rates, whereas age, disease severity, mechanical ventilation, and in‐hospital vasopressor use (all, p < 0.001) increased odds of 30‐ and 90‐day post‐discharge death. Additionally, admission ( p = 0.011) and nosocomial ( p = 0.022) infections increased the odds of 90‐day post‐discharge death. Conclusion In a global cohort of hospitalised patients with cirrhosis, patients in high‐income countries exhibited the highest rates of 30‐ and 90‐day readmission, alongside lower mortality rates and a higher incidence of LT. Variables that predict 30‐day readmission also predicted 90‐day readmission. Understanding these disparities is essential for achieving health equity.

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.

Background and Aims: Fatty liver disease is a major public health threat due to its very high prevalence and related morbidity and mortality. Focused and dedicated interventions are urgently needed to target disease prevention, treatment, and care. Approach and Results: We developed an aligned, prioritized action agenda for the global fatty liver disease community of practice. Following a Delphi methodology over 2 rounds, a large panel (R1 n = 344, R2 n = 288) reviewed the action priorities using Qualtrics XM, indicating agreement using a 4-point Likert-scale and providing written feedback. Priorities were revised between rounds, and in R2, panelists also ranked the priorities within 6 domains: epidemiology, treatment and care, models of care, education and awareness, patient and community perspectives, and leadership and public health policy. The consensus fatty liver disease action agenda encompasses 29 priorities. In R2, the mean percentage of “agree” responses was 82.4%, with all individual priorities having at least a super-majority of agreement (> 66.7% “agree”). The highest-ranked action priorities included collaboration between liver specialists and primary care doctors on early diagnosis, action to address the needs of people living with multiple morbidities, and the incorporation of fatty liver disease into relevant non-communicable disease strategies and guidance. Conclusions: This consensus-driven multidisciplinary fatty liver disease action agenda developed by care providers, clinical researchers, and public health and policy experts provides a path to reduce the prevalence of fatty liver disease and improve health outcomes. To implement this agenda, concerted efforts will be needed at the global, regional, and national levels.