Citations

Background: Alcohol-associated hepatitis (AH) is among the deadliest liver diseases, but its incidence is poorly defined. The aim of our study was to define the incidence of AH meeting the National Institute on Alcohol Abuse and Alcoholism criteria and to identify risk factors for AH. Methods: We conducted a retrospective cohort study using the Rochester epidemiology project database on adult patients hospitalized with AH between January 1, 2000 and December 31, 2018. Patients were screened using ICD-9 codes and then included if they met the National Institute on Alcohol Abuse and Alcoholism criteria on manual chart review. Baseline demographics, comorbidities, access to care, liver-related complications, and outcomes were obtained. The HOUsing-based index of SocioEconomic status index was used to measure socioeconomic status. Incidence rates were calculated in cases per 100,000 person-years of follow-up. Results: Among 204 patients, the cumulative AH incidence was 6.8 per 100,000 person-years. Between 2000–2004 and 2015–2018, AH incidence among males increased from 8.4 to 14.7 per 100,000 py, whereas AH incidence among females increased by 7-fold from 0.8 to 5.9 per 100,000 py. Such increases among females were accompanied by increases in comorbid depression and anxiety. The proportion of patients with AH in the lower socioeconomic status quartiles increased from 62.9% between 2000 and 2004 to 73.3% between 2015 and 2019. Conclusions: The incidence of AH is increasing rapidly, especially among females and individuals of lower socioeconomic status. There are areas of unmet need in preventative measures and treatments for comorbid psychiatric disorders in patients at high risk of AH.

Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.

Patients with cirrhosis frequently require admission to the intensive care unit as complications arise in the course of their disease. These admissions are associated with high short- and long-term morbidity and mortality. Thus, understanding and characterizing complications and unique needs of patients with cirrhosis and acute-on-chronic liver failure helps providers identify appropriate level of care and evidence-based treatments. While there is no widely accepted critical care admission criteria for patients with cirrhosis, the presence of organ failure and primary or nosocomial infections are associated with particularly high in-hospital mortality. Optimal management of patients with cirrhosis in the critical care setting requires a system-based approach that acknowledges deviations from canonical pathophysiology. In this review, we discuss appropriate considerations and evidence-based practices for the general care of patients with cirrhosis and critical illness.

INTRODUCTION: The triglyceride (TG) threshold for diagnosis of chylous ascites in patients with portal hypertension remains uncertain. METHODS: Retrospective analysis of lipoprotein electrophoresis was conducted in 286 consecutive ascites samples. RESULTS: Ascitic TG ≥ 81 mg/dL is 95.4% sensitive and 94.6% specific for chylous ascites diagnosed by the presence of significant chylomicron population. DISCUSSION: The cutoff for chylous ascites diagnosis should be TG ≥ 81 mg/dL.

AbstractBackgroundNodular regenerative hyperplasia (NRH) is a rare condition characterized clinically by the development of non‐cirrhotic portal hypertension. NRH is the histopathological result in the liver of various systemic disease processes including autoimmune disorders, haematological malignancies and medications. However, natural history of this condition has been limited to small case series while patient outcomes pertaining to different aetiologies of NRH are largely unknown.MethodsA retrospective cohort of consecutive patients diagnosed with pathology‐confirmed NRH at Mayo Clinic between 2002 and 2017 was identified. The histological diagnosis of NRH was determined by expert liver pathologists. Patients with metastatic liver disease, history of liver transplantation or younger than 18 were excluded. Potential aetiologies of NRH were classified as haematological, rheumatological, drug‐associated, miscellaneous or idiopathic. Long‐term mortality was analysed using Kaplan‐Meier estimation and Cox regression models.ResultsOne hundred and sixty‐seven consecutive patients with pathology‐confirmed NRH were analysed over a 15‐year period and followed for a median time of 50 months (1–306 months). The mean age at diagnosis was 53 years. No aetiology or risk factor for NRH was identified in the majority of patients (94, 56.3%), whereas an associated, possibly causal, condition was found in 73 patients (secondary NRH). The most common presenting feature was elevated liver tests (80%), but no significant differences in laboratory tests were seen based on aetiology of NRH. Compared to idiopathic NRH, those with an identified cause had a higher rate of splenomegaly at presentation (54% vs. 27%, p = 0.002). Portal hypertension‐related complications at diagnosis were common, with ascites present in one‐third of patients. Overall transplant‐free survival was 63% at 5 years. Median survival in idiopathic NRH was 9.4 years compared to 7.3 years in secondary NRH. Age, renal function and volume status at presentation were significantly associated with survival; however, MELD score was not.ConclusionsThe rates of liver‐related complications and mortality in NRH are low, and only a small number of patients ultimately require liver transplantation. Most patients do not have an identified risk factor or aetiology for NRH, and liver‐related outcomes do not appear to differ based on associated, possibly causal, conditions.

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity. METHODS: A review of Mayo Clinic's electronic health records identified 5,212 patients with advanced cirrhosis ≥18 years who underwent liver transplantation at the 3 Mayo Clinic transplant centers between 1988 and 2019. The patients were matched by age and sex in a 1:4 ratio to controls without liver disease and then divided into training, validation, and test sets using a 70%-10%-20% split. The primary outcome was the performance of the model in distinguishing patients with cirrhosis from controls using their ECGs. In addition, the association between the ACE score and the severity of patients' liver disease was assessed. RESULTS: The model's area under the curve in the test set was 0.908 with 84.9% sensitivity and 83.2% specificity, and this performance remained consistent after additional matching for medical comorbidities. Significant elevations in the ACE scores were seen with increasing model for end-stage liver disease-sodium score. Longitudinal trends in the ACE scores before and after liver transplantation mirrored the progression and resolution of liver disease. DISCUSSION: The ACE score, a deep learning model, can accurately discriminate ECGs from patients with and without cirrhosis. This novel relationship between AI-enabled ECG analysis and cirrhosis holds promise as the basis for future low-cost tools and applications in the care of patients with liver disease.

Abstract The rise in innovative digital health technologies has led a paradigm shift in health care toward personalized, patient‐centric medicine that is reaching beyond traditional brick‐and‐mortar facilities into patients’ homes and everyday lives. Digital solutions can monitor and detect early changes in physiological data, predict disease progression and health‐related outcomes based on individual risk factors, and manage disease intervention with a range of accessible telemedicine and mobile health options. In this review, we discuss the unique transformation underway in the care of patients with liver disease, specifically examining the digital transformation of diagnostics, prediction and clinical decision‐making, and management. Additionally, we discuss the general considerations needed to confirm validity and oversight of new technologies, usability and acceptability of digital solutions, and equity and inclusivity of vulnerable populations.