Chaudhary, Ashok

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.

INTRODUCTION: A Model for End-Stage Liver Disease-lactate (MELD-LA) score is an independent predictor of inpatient mortality and organ failure validated in more than 8,600 patients across diverse settings. However, its utility in sepsis, particularly when combined with lactate clearance (LA clearance), has not been fully evaluated. In this study, we examined the dynamic role of MELD-LA with 24-hour LA clearance as a predictor of 30-day mortality in critically ill patients with cirrhosis and sepsis. METHODS: We analyzed cirrhosis patients with sepsis admitted to the intensive care unit (ICU) in a large US healthcare system (20 hospitals) from 2014 to 2022, examining MELD-LA and LA clearance within 24 hours as predictors of 30-day mortality. We externally validated our findings in the Asian Pacific Association for the Study of the Liver-acute-on-chronic liver failure Research Consortium, a multinational Asian cohort (31 centers) from 2009 to 2019. RESULTS: A total of 3,879 cirrhosis patients with sepsis were admitted to the ICU (43.2% female, 20.5% Hispanic, 12.4% on dialysis; mean initial LA 4.5 mmol/L, MELD 20.8, MELD-LA 17.6). Survivors showed a median lactate change of −1.04 (35% clearance) compared with −0.4 (12%) for nonsurvivors. LA clearance within 24 hours was influenced by the presenting MELD-LA. In the North American cohort, ≥35% LA clearance was associated with improved 30-day survival (odds ratio 0.41, 95% CI 0.31–0.53; P < 0.001). Similarly, in the Asian Pacific Association for the Study of the Liver cohort (n = 1,259), ≥35% clearance was linked to a 60% reduction in mortality (odds ratio 0.42, 95% CI 0.28–0.62; P < 0.001). When stratified by LA clearance, patients with LA clearance of ≥35% had a higher percentage of early administration of resuscitative measures than those with LA clearance of < 10% especially regarding early antibiotic administration (82.5% vs 67.6%; P value <0.001). DISCUSSION: Early reduction in initial LA by one-third predicts 30-day mortality and may indicate treatment response in cirrhosis patients with sepsis in the ICU. Combining MELD-LA and LA clearance early enables better risk stratification and informs management.