Idalsoaga, Francisco

Summary Background Oesophageal disorders and chronic liver disease are common worldwide and significantly impact quality of life. The intricate link between these conditions, including how oesophageal disorders like GERD, Barrett's oesophagus and oesophageal cancer affect and are affected by chronic liver disease, remains poorly understood. Aims To review the relationship between oesophageal disorders and chronic liver disease, evaluating epidemiology, pathophysiology and therapeutic factors. Methods We reviewed the literature on the relationship between oesophageal disorders and chronic liver disease, including cirrhosis, using the PubMed database Results Oesophageal disorders such as gastroesophageal reflux disease, Barrett's oesophagus, oesophageal cancer, oesophageal motor disorders and oesophageal candidiasis are prevalent among individuals with cirrhosis, exacerbating the burden of liver disease. These diseases have a multifaceted symptomatology and pathogenic basis, posing a significant challenge in cirrhotic patients that necessitates careful diagnosis and management. Additionally, therapies frequently used for these diseases, such as proton pump inhibitors, require careful consideration in cirrhotic patients due to potential adverse effects and altered pharmacokinetics. Managing oesophageal disorders in cirrhotic patients requires a cautious approach due to possible interactions with medications and the risk of adverse effects. Furthermore, symptoms associated with these conditions are often exacerbated by common interventions in patients with cirrhosis, such as band ligation for oesophageal varices. Conclusions Oesophageal disorders are common in cirrhosis and increase the disease burden. These conditions require careful management due to complex symptoms and treatment risks. Proton pump inhibitors and other therapies must be used cautiously, as cirrhosis interventions can worsen symptoms.

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/.