Acute liver failure is a rare and dynamic condition, with a broad aetiology and an incompletely understood pathophysiology. Management of this life-threatening disease requires critical care and organ support and frequently early liver transplantation. Proper identification, prevention and treatment of complications such as intracranial hypertension and sepsis are critical to optimising outcomes. The identification of the cause of acute liver failure and the prompt initiation of the aetiological treatment can also improve prognosis. Survival has progressively improved in parallel to advances in medical treatment. Intracranial hypertension complicating hepatic encephalopathy is less frequent than in the past and intracranial pressure monitoring now relies on non-invasive techniques. Current prognostic models have good accuracy to identify patients who will die without liver transplantation but are not able to identify those in whom transplantation is futile. New prognostic markers to select patients for transplantation are still in the pipeline. Therapeutic plasma exchange and, in some centers, early renal replacement therapy are well established treatments for the disease. The use of other artificial liver devices in clinical practice is not supported by evidence. This review is intended to provide a clinical update on the management of acute liver failure, incorporating the most recent advances in the field.

It remains unclear whether rectal colonization with multidrug-resistant organisms (MDROs) is prevalent and predisposes to infections by the same pathogens in patients with cirrhosis. Two series of critically ill patients were evaluated. In the Barcelona cohort, 486 consecutive patients were prospectively evaluated, 129 with and 357 without cirrhosis (2015-2016). Rectal swabs were performed at admission and weekly thereafter (until intensive care unit [ICU] discharge) to detect MDRO colonization. Risk factors for colonization and infection by MDROs were evaluated. A retrospective cohort from Frankfurt (421 patients with cirrhosis; 2010-2018) was investigated to evaluate MDRO rectal colonization in another epidemiological scenario. In the Barcelona cohort, 159 patients were colonized by MDROs (32.7%), 102 (64.2%) at admission and 57 (35.8%) during follow-up. Patients with cirrhosis showed higher rates of rectal colonization at admission than those without cirrhosis (28.7% vs. 18.2%, p = 0.01) but similar colonization rates during ICU stay. Extended-spectrum beta-lactamase-Enterobacterales were the most frequent MDROs isolated in both groups. Colonization by MDROs independently increased the risk of infection by MDROs at admission and during follow-up. Risk of new infection by the colonizing strain was also significantly increased in patients with (hazard ratio [HR] 7.41) and without (HR 5.65) cirrhosis. Rectal colonization by MDROs was also highly prevalent in Frankfurt (n = 198; 47%; 131 at admission [66.2%] and 67 [33.8%] during follow-up), with vancomycin-resistant enterococci being the most frequent colonizing organism. Rectal colonization by MDROs was also associated with an increased risk of infection by MDROs in this cohort. Infections occurring in MDR carriers were mainly caused by the colonizing strain. Rectal colonization by MDROs is extremely frequent in critically ill patients with cirrhosis. Colonization increases the risk of infection by the colonizing resistant strain. Rectal colonization by multidrug-resistant organisms (MDROs) is a prevalent problem in patients with cirrhosis requiring critical care. The pattern of colonizing bacteria is heterogeneous with relevant differences between centers. Colonization by MDROs is associated with increased risk of infection by the colonizing bacteria in the short term. This finding suggests that colonization data could be used to guide empirical antibiotic therapy and de-escalation policies in patients with cirrhosis.

Artificial intelligence (AI) methods in hepatology have proliferated since the mid-2010s, with numerous publications and some regulatory approvals. Yet, adoption of AI methods in real-world clinical practice and clinical research remains limited. Despite clear benefits of using AI to analyse complex data types in hepatology, such as histopathology, radiology images, multi-omics and more recently, natural language patient data, there are still substantial barriers and challenges to its integration into routine clinical workflows. In this position paper, we assess limitations and propose a set of clear recommendations aimed at both the development of AI systems and the broader hepatology environment to facilitate the transition of AI-based diagnostic, prognostic, and predictive tools into clinical care. In particular, we argue that the use of AI in clinical trials, seamless integration into hospital information systems and building AI literacy among clinicians will ultimately drive clinical adoption. We validate this perspective through a Delphi consensus involving 34 international experts from hepatology, AI, and data science, ensuring a comprehensive and consensus-driven evaluation of our recommendations.