Kulkarni, Anand V

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.

ABSTRACTBackgroundMetabolic dysfunction‐associated steatotic liver disease (MASLD) affects over one third of the global population and is closely linked to gastrointestinal, oncological, and cardiovascular diseases. As the most common cause of steatotic liver disease (SLD), MASLD is often referred to as “fatty liver” in general health information searches. With the Internet becoming a primary source of healthcare information, this study aimed to evaluate global and regional public interest in steatotic liver disease using Google Trends data from January 2004 to December 2024.MethodsA comprehensive analysis was performed using Google Trends with the search term “fatty liver disease.” Interest levels were quantified using Google Trends indices, and temporal patterns were evaluated using average annual percent change (AAPC). Regional variations were assessed to identify geographic hotspots of public interest.ResultsGlobal interest in MASLD demonstrated significant growth (AAPC: 6.77%, 95% Confidence interval [CI]: 5.33–8.23), with a 264% increase from 2004 to 2024. Peak interest was observed in August 2024 (index: 100) compared to the lowest point in June 2007. Iran showed the highest country‐specific search volume (index: 100), while within the United States, West Virginia demonstrated the greatest relative interest (index: 100). Key topics associated with MASLD included “liver,” “symptoms,” “disease,” and “therapy.”ConclusionsSubstantial growth in global public interest in MASLD, with distinct temporal and geographical patterns, provides insights to develop targeted awareness campaigns and educational interventions, particularly in regions with low search volumes. Future research should examine the relationship between online search patterns and clinical outcomes in MASLD.

Background and Aims: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence in a global cohort, we sought to derive and validate an enhanced prognostic model. Approach and Results: The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled patients with AH per National Institute for Alcohol Abuse and Alcoholism criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day postadmission mortality, 3 artificial intelligence algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined through Bayesian updating, integrating the derivation cohort’s average 90-day mortality with each center’s approximate mortality rate to produce posttest probabilities. The ALCoholic Hepatitis Artificial INtelligence Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30 d) and 27.9% (90 d) in the derivation cohort versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779–0.844) and 0.799 (0.769–0.830), significantly surpassing legacy models like Maddrey’s Discriminant Function, Model for End-Stage Liver Disease variations, age-serum bilirubin-international normalized ratio-serum Creatinine score, Glasgow, and modified Glasgow Scores (p < 0.001). ALCoholic Hepatitis Artificial INtelligence Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCoholic Hepatitis Artificial INtelligence Ensemble score > 0.20 in both derivation and validation cohorts. Conclusions: Harnessing artificial intelligence within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it’s accessible at: https://aihepatology.shinyapps.io/ALCHAIN/.

AbstractVasoactive drugs form the mainstay of therapy for two of the most important complications of liver disease: hepatorenal syndrome (HRS) and acute variceal bleed (AVB). With cumulative evidence supporting the use in cirrhosis, terlipressin has been recommended for the management of HRS and AVB. However, owing to the safety concerns, terlipressin was not approved by food and drug administration (FDA) until now. In this review, we discuss the pharmacology and the major practice‐changing studies on the safety and efficacy of terlipressin in patients with cirrhosis particularly focusing on existing indications like AVB and HRS and reviewing new data on the expanding indications in liver disease. The references for this review were identified from PUBMED with MeSH terms such as “terlipressin,” “hepatorenal syndrome,” “varices, esophagal and gastric,” “ascites” and “cirrhosis.” Terlipressin, a synthetic analogue of vasopressin, was introduced in 1975 to overcome the adverse effects of vasopressin. Terlipressin is an effective drug for HRS reversal in patients with liver cirrhosis and acute‐on‐chronic liver failure. There is documented mortality benefit with terlipressin therapy in HRS and AVB. Adverse effects are common with terlipressin and need to be monitored strictly. There is some evidence to support the use of this drug in refractory ascites, hepatic hydrothorax, paracentesis‐induced circulatory dysfunction and perioperatively during liver transplantation. However, terlipressin is not yet recommended for such indications. In conclusion, terlipressin has stood the test of time with expanding indications and clear prerequisites for clinical use. Our review warrants a fresh perspective on the efficacy and safety of terlipressin.