Arrese, Marco

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: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2. METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) formed an ensemble model. RESULTS: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% confidence interval [CI]: 0.764–0.819), outperformed VCTE alone (0.745, 95% CI 0.717–0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679–0.748, P < 0.0001), and Agile-3 model (0.740, 0.710–0.770, P < 0.0001) regarding the AUC, decision curve analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668–0.749) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores. DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE (https://aihepatology.shinyapps.io/ALADDIN1) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab (https://aihepatology.shinyapps.io/ALADDIN2) offers an effective alternative when VCTE is unavailable.

Background: Severe alcohol-associated hepatitis (sAH) is a well-characterized disease with high short-term mortality. However, there is limited research on those with a “less severe condition” (moderate AH). This study aims to characterize in-depth patients with moderate AH (mAH), including the performance of mortality scoring systems, key prognostic factors, and survival over time. Methods: A multicenter retrospective cohort study (2009–2019) included patients with mAH (MELD score ≤20 at admission). Cox regression and receiver operating characteristic curves with AUC were used for analysis. Results: We included 1845 patients with AH (20 centers, 8 countries) between 2009 and 2019. mAH was defined as a MELD score ≤20 at admission. Twenty-four percent met the criteria for an mAH episode. Patients with mAH tend to be older and have a higher proportion of females, with a median MELD of 17 (15–19), Maddrey discriminant function (mDF) of 33 (22–40), the trajectory of serum bilirubin of 0.83 (0.60–1.21), and neutrophil-to-lymphocyte ratio (NLR) of 5 (2.96–8.60). The primary causes of death in mAH included multiple organ failure (34.1%) and infections (16.6%). The cumulative survival rates at 30, 90, and 180 days were 94.3%, 90.4%, and 88.2%, respectively. In multivariable analysis, age was the only significant predictor of 30-day mortality (HR 1.49, 95% CI: 1.27–1.76, p <0.001). Mortality prediction models showed poor performance, with AUC for MELD (0.671), mDF (0.726), trajectory of serum bilirubin (0.733), and NLR (0.697). Conclusions: Patients with moderate AH exhibited a mortality of 11.8% at 6 months, primarily driven by multiple organ failure and infections. These patients also exhibit a different clinical profile compared to those with sAH. Tailored models and therapeutic strategies are needed to improve long-term outcomes in mAH.

ABSTRACTSeveral dynamic models predict mortality and corticosteroid response in alcohol‐associated hepatitis (AH), yet no consensus exists on the most effective model. This study aimed to assess predictive models for corticosteroid response and short‐term mortality in severe AH within a global cohort. We conducted a multi‐national study of patients with severe AH treated with corticosteroids for at least 7 days, enrolled between 2009 and 2019. Dynamic models—Lille‐4, Lille‐7, trajectory of serum bilirubin (TSB), and neutrophil‐to‐lymphocyte ratio (NLR)—were used to estimate 30‐ and 90‐day mortality. Lille‐7 demonstrated the highest accuracy for both 30‐ and 90‐day mortality.

Neurogastroenterology and motility disorders are complex gastrointestinal conditions that are prevalent worldwide, particularly affecting women and younger individuals. These conditions significantly impact the quality of life of people suffering from them. There is increasing evidence linking these disorders to cirrhosis, with a higher prevalence compared to the general population. However, the link between neurogastroenterology and motility disorders and cirrhosis remains unclear due to undefined mechanisms. In addition, managing these conditions in cirrhosis is often limited by the adverse effects of drugs commonly used for these disorders, presenting a significant clinical challenge in the routine management of patients with cirrhosis. This review delves into this connection, exploring potential pathophysiological links and clinical interventions between neurogastroenterology disorders and cirrhosis.

LINKED CONTENTThis article is linked to Idalsoaga et al papers. To view these articles, visithttps://doi.org/10.1111/apt.18193andhttps://doi.org/10.1111/apt.18220

Abstract Background Outcomes in alcohol‐associated liver disease (ALD) are influenced by several race and ethnic factors, yet its natural history across the continuum of patients in different stages of the disease is unknown. Methods We conducted a retrospective cohort study of U.S. adults from 2011 to 2018, using three nationally representative databases to examine potential disparities in relevant outcomes among racial and ethnic groups. Our analysis included logistic and linear regressions, along with competing risk analysis. Results Black individuals had the highest daily alcohol consumption (12.6 g/day) while Hispanic participants had the largest prevalence of heavy episodic drinking (33.5%). In a multivariable‐adjusted model, Hispanic and Asian participants were independently associated with a higher ALD prevalence compared to Non‐Hispanic White interviewees (OR: 1.4, 95% CI: 1.1–1.8 and OR: 1.5 95% CI:1.1–2.0, respectively), while Blacks participants had a lower ALD prevalence (OR: .7 95% CI: .6–.9), and a lower risk of mortality during hospitalization due to ALD (OR: .83 95% CI: .73–.94). Finally, a multivariate competing‐risk analysis showed that Hispanic ethnicity had a decreased probability of liver transplantation if waitlisted for ALD (SHR: .7, 95% CI: .6–.8) along with female Asian population (HR: .40, 95% CI: .26–.62). Conclusions After accounting for key social and biological health determinants, the Hispanic population showed an increased risk of ALD prevalence, even with lower alcohol consumption. Additionally, Hispanic and Asian female patients had reduced access to liver transplantation compared to other enlisted patients.

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/.