An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study.

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/ .

Performance of AI-Enabled Electrocardiogram in the Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease.

BACKGROUND AND AIMS: Accessible noninvasive screening tools for metabolic dysfunction-associated steatotic liver disease (MASLD) are needed. We aim to explore the performance of a deep learning-based artificial intelligence (AI) model in distinguishing the presence of MASLD using 12-lead electrocardiogram (ECG). METHODS: This is a retrospective study of adults diagnosed with MASLD in Olmsted County, Minnesota, between 1996 and 2019. Both cases and controls had ECGs performed within 6 years before and 1 year after study entry. An AI-based ECG model using a convolutional neural network was trained, validated, and tested in 70%, 10%, and 20% of the cohort, respectively. External validation was performed in an independent cohort from Mayo Clinic Enterprise. The primary outcome was the performance of ECG to identify MASLD, alone or when added to clinical parameters. RESULTS: A total of 3468 MASLD cases and 25,407 controls were identified. The AI-ECG model predicted the presence of MASLD with an area under the curve (AUC) of 0.69 (original cohort) and 0.62 (validation cohort). The performance was similar or superior to age- and sex-adjusted models using body mass index (AUC, 0.71), presence of diabetes, hypertension or hyperlipidemia (AUC, 0.68), or diabetes alone (AUC, 0.66). The model combining ECG, age, sex, body mass index, diabetes, and alanine aminotransferase had the highest AUC: 0.76 (original) and 0.72 (validation). CONCLUSIONS: This is a proof-of-concept study that an AI-based ECG model can detect MASLD with a comparable or superior performance as compared with the models using a single clinical parameter but not superior to the combination of clinical parameters. ECG can serve as another screening tool for MASLD in the nonhepatology space.

Racial and ethnic disparities in early treatment with immunotherapy for advanced HCC in the United States.

BACKGROUND AND AIMS: Immunotherapy has emerged as an effective treatment for patients with advanced-stage HCC. We aimed to investigate the efficacy of immunotherapy for advanced HCC in a nationwide cohort and racial and ethnic disparities in access to immunotherapy. APPROACH AND RESULTS: We used the US National Cancer Database to identify patients with tumor-node-metastasis stage 3 or 4 HCC between 2017 and 2018. We performed multivariable Cox regression to identify factors associated with overall survival (OS) and logistic regression to identify factors associated with receipt of immunotherapy. Of the 3,990 patients treated for advanced HCC, 3,248 (81.4%) patients received chemotherapy and 742 (18.6%) patients received immunotherapy as a first-line treatment. Immunotherapy was associated with improved OS compared with chemotherapy (adjusted HR: 0.76, 95% CI: 0.65-0.88) after adjusting for covariates. There were racial and ethnic disparities in access to immunotherapy, with Hispanic (adjusted OR [aOR]: 0.63, 95% CI: 0.46-0.83) and Black patients (aOR: 0.71, 95% CI: 0.54-0.89) less likely to receive immunotherapy compared with White patients. There was a significant interaction between race-ethnicity and facility type, with higher disparity observed in nonacademic centers (interaction p = 0.004). CONCLUSIONS: Immunotherapy was associated with improved OS compared with chemotherapy in advanced HCC. There are significant disparities in early access to immunotherapy, likely due to differential access to clinical trials and experimental therapies. A comprehensive approach to monitoring and eliminating racial-ethnic disparities in the management of advanced HCC is urgently needed.

Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis.

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.

Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases.

Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.

Detection of Circulating Tumor Cells and Their Implications as a Biomarker for Diagnosis, Prognostication, and Therapeutic Monitoring in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is among the leading causes of worldwide cancer-related morbidity and mortality. Poor prognosis of HCC is attributed primarily to tumor presentation at an advanced stage when there is no effective treatment to achieve the long term survival of patients. Currently available tests such as alpha-fetoprotein have limited accuracy as a diagnostic or prognostic biomarker for HCC. Liver biopsy provides tissue that can reveal tumor biology but it is not used routinely due to its invasiveness and risk of tumor seeding, especially in early-stage patients. Liver biopsy is also limited in revealing comprehensive tumor biology due to intratumoral heterogeneity. There is a clear need for new biomarkers to improve HCC detection, prognostication, prediction of treatment response, and disease monitoring with treatment. Liquid biopsy could be an effective method of early detection and management of HCC. Circulating tumor cells (CTCs) are cancer cells in circulation derived from the original tumor or metastatic foci, and their measurement by liquid biopsy represents a great potential in facilitating the implementation of precision medicine in patients with HCC. CTCs can be detected by a simple peripheral blood draw and potentially show global features of tumor characteristics. Various CTC detection platforms using immunoaffinity and biophysical properties have been developed to identify and capture CTCs with high efficiency. Quantitative abundance of CTCs, as well as biological characteristics and genomic heterogeneity among the CTCs, can predict disease prognosis and response to therapy in patients with HCC. This review article will discuss the currently available technologies for CTC detection and isolation, their utility in the clinical management of HCC patients, their limitations, and future directions of research.

The Addition of C-Reactive Protein and von Willebrand Factor to Model for End-Stage Liver Disease-Sodium Improves Prediction of Waitlist Mortality.

BACKGROUND AND AIMS: Patients with cirrhosis on the liver transplant (LT) waiting list may die or be removed because of complications of portal hypertension (PH) or infections. von Willebrand factor antigen (vWF-Ag) and C-reactive protein (CRP) are simple, broadly available markers of these processes. APPROACH AND RESULTS: We determined whether addition of vWF-Ag and CRP to the Model for End-Stage Liver Disease-Sodium (MELD-Na) score improves risk stratification of patients awaiting LT. CRP and vWF-Ag at LT listing were assessed in two independent cohorts (Medical University of Vienna [exploration cohort] and Mayo Clinic Rochester [validation cohort]). Clinical characteristics, MELD-Na, and mortality on the waiting list were recorded. Prediction of 3-month waiting list mortality was assessed by receiver operating characteristics curve (ROC-AUC). In order to explore potential mechanisms underlying the prognostic utility of vWF-Ag and CRP in this setting, we evaluated their association with PH, bacterial translocation, systemic inflammation, and circulatory dysfunction. In the exploration cohort (n = 269) vWF-Ag and CRP both improved the predictive value of MELD-Na for 3-month waitlist mortality and showed the highest predictive value when combined (AUC: MELD-Na, 0.764; MELD-Na + CRP, 0.790; MELD-Na + vWF, 0.803; MELD-Na + CRP + vWF-Ag, 0.824). Results were confirmed in an independent validation cohort (n = 129; AUC: MELD-Na, 0.677; MELD-Na + CRP + vWF-Ag, 0.882). vWF-Ag was independently associated with PH and inflammatory biomarkers, whereas CRP closely, and MELD independently, correlated with biomarkers of bacterial translocation/inflammation. CONCLUSIONS: The addition of vWF-Ag and CRP-reflecting central pathophysiological mechanisms of PH, bacterial translocation, and inflammation, that are all drivers of mortality on the waiting list for LT-to the MELD-Na score improves prediction of waitlist mortality. Using the vWFAg-CRP-MELD-Na model for prioritizing organ allocation may improve prediction of waitlist mortality and decrease waitlist mortality.

Identification of optimal therapeutic window for steroid use in severe alcohol-associated hepatitis: A worldwide study.

BACKGROUND & AIMS: Corticosteroids are the only effective therapy for severe alcohol-associated hepatitis (AH), defined by a model for end-stage liver disease (MELD) score >20. However, there are patients who may be too sick to benefit from therapy. Herein, we aimed to identify the range of MELD scores within which steroids are effective for AH. METHODS: We performed a retrospective, international multicenter cohort study across 4 continents, including 3,380 adults with a clinical and/or histological diagnosis of AH. The main outcome was mortality at 30 days. We used a discrete-time survival analysis model, and MELD cut-offs were established using the transform-the-endpoints method. RESULTS: In our cohort, median age was 49 (40-56) years, 76.5% were male, and 79% had underlying cirrhosis. Median MELD at admission was 24 (19-29). Survival was 88% (87-89) at 30 days, 77% (76-78) at 90 days, and 72% (72-74) at 180 days. A total of 1,225 patients received corticosteroids. In an adjusted-survival-model, corticosteroid use decreased 30-day mortality by 41% (hazard ratio [HR] 0.59; 0.47-0.74; p <0.001). Steroids only improved survival in patients with MELD scores between 21 (HR 0.61; 0.39-0.95; p = 0.027) and 51 (HR 0.72; 0.52-0.99; p = 0.041). The maximum effect of corticosteroid treatment (21-30% survival benefit) was observed with MELD scores between 25 (HR 0.58; 0.42-0.77; p <0.001) and 39 (HR 0.57; 0.41-0.79; p <0.001). No corticosteroid benefit was seen in patients with MELD >51. The type of corticosteroids used (prednisone, prednisolone, or methylprednisolone) was not associated with survival benefit (p = 0.247). CONCLUSION: Corticosteroids improve 30-day survival only among patients with severe AH, especially with MELD scores between 25 and 39. LAY SUMMARY: Alcohol-associated hepatitis is a condition where the liver is severely inflamed as a result of excess alcohol use. It is associated with high mortality and it is not clear whether the most commonly used treatments (corticosteroids) are effective, particularly in patients with very severe liver disease. In this worldwide study, the use of corticosteroids was associated with increased 30-day, but not 90- or 180-day, survival. The maximal benefit was observed in patients with an MELD score (a marker of severity of liver disease; higher scores signify worse disease) between 25-39. However, this benefit was lost in patients with the most severe liver disease (MELD score higher than 51).

Class III obesity is a risk factor for the development of acute-on-chronic liver failure in patients with decompensated cirrhosis.

BACKGROUND & AIMS: Acute-on-chronic liver failure (ACLF) is a syndrome of systemic inflammation and organ failures. Obesity, also characterized by chronic inflammation, is a risk factor among patients with cirrhosis for decompensation, infection, and mortality. Our aim was to test the hypothesis that obesity predisposes patients with decompensated cirrhosis to the development of ACLF. METHODS: We examined the United Network for Organ Sharing (UNOS) database, from 2005-2016, characterizing patients at wait-listing as non-obese (body mass index [BMI] <30), obese class I-II (BMI 30-39.9) and obese class III (BMI ≥40). ACLF was determined based on the CANONIC study definition. We used Cox proportional hazards regression to assess the association between obesity and ACLF development at liver transplantation (LT). We confirmed our findings using the Nationwide Inpatient Sample (NIS), years 2009-2013, using validated diagnostic coding algorithms to identify obesity, hepatic decompensation and ACLF. Logistic regression evaluated the association between obesity and ACLF occurrence. RESULTS: Among 387,884 patient records of decompensated cirrhosis, 116,704 (30.1%) were identified as having ACLF in both databases. Multivariable modeling from the UNOS database revealed class III obesity to be an independent risk factor for ACLF at LT (hazard ratio 1.24; 95% CI 1.09-1.41; p <0.001). This finding was confirmed using the NIS (odds ratio 1.30; 95% CI 1.25-1.35; p <0.001). Regarding specific organ failures, analysis of both registries demonstrated patients with class I-II and class III obesity had a greater prevalence of renal failure. CONCLUSION: Class III obesity is a newly identified risk factor for ACLF development in patients with decompensated cirrhosis. Obese patients have a particularly high prevalence of renal failure as a component of ACLF. These findings have important implications regarding stratifying risk and preventing the occurrence of ACLF. LAY SUMMARY: In this study, we identify that among patients with decompensated cirrhosis, class III obesity (severe/morbid obesity) is a modifiable risk factor for the development of acute-on-chronic liver failure (ACLF). We further demonstrate that regarding the specific organ failures associated with ACLF, renal failure is significantly more prevalent in obese patients, particularly those with class III obesity. These findings underscore the importance of weight management in cirrhosis, to reduce the risk of ACLF. Patients with class III obesity should be monitored closely for the development of renal failure.

Case Report: When Arteries Betray: Massive Post-transplantation Hemorrhage.

Hepatic artery pseudoaneurysm is a rare complication that may occur in the setting of liver transplantation and other traumatic instrumentation of the hepatobiliary system. This condition poses a significant morbidity and mortality risk and must be diagnosed and intervened upon emergently. Herein we present a case of hepatic artery pseudoaneurysm complicated by hemorrhagic shock in the setting of a post-liver transplant biliary leak. In this case, the patient was ultimately diagnosed via hepatic angiogram, treated with hepatic artery embolization, and required subsequent retransplantation. The objective of this case was to demonstrate the importance of maintaining a high clinical suspicion for hepatic artery pseudoaneurysm in the post-transplant setting, emphasize the use of computed tomography angiography as a primary diagnostic tool, and involving interventional radiology early in the treatment course.

Antibiotic exposure and depression incidence: A cohort study of the Korean population.

Recent research highlights the crucial role of the gut-brain axis in understanding depression etiologies. While burgeoning studies suggest an association between disruptions in gut microbiota and the development of depression, limited longitudinal studies have investigated this link. To address this gap, we conducted a retrospective cohort study using National Health Insurance Service-Health Screening Cohort (NHIS-HEALS) data in South Korea, involving 199,144 individuals aged 40-79. We examined the impact of cumulative antibiotic exposure (2004-2008) on subsequent depression incidence (2009-2013) by conducting Cox proportional hazards regressions. Our findings show an increasing depression risk with extended antibiotic exposure after adjusting for comorbidities and behavioral covariates. A broader antibiotic spectrum was associated with a higher depression risk. These trends persisted after adjusting for the original antibiotic indications. In conclusion, our study highlights the duration-dependent association between antibiotic exposure and increased depression risk, offering insights into depression etiologies and relevant novel therapeutic tools, and advocating for heightened antibiotic stewardship considering their impact on mental health.

Evolutionary changes in metabolic dysfunction-associated steatotic liver disease and risk of hepatocellular carcinoma: A nationwide cohort study.

BACKGROUND/AIMS: To determine the association between evolutionary changes in metabolic dysfunction-associated steatotic liver disease (MASLD) status and the risk of hepatocellular carcinoma (HCC) in a nationwide population-based cohort. METHODS: Information on study participants was derived from the Korea National Health Insurance Service database. The study population consisted of 5,080,410 participants who underwent two consecutive biennial health screenings between 2009 and 2012. All participants were followed up until HCC, death, or 31 December 2020. The association of evolutionary changes in MASLD status, as assessed by the fatty liver index and cardiometabolic risk factors, including persistent non-MASLD, resolved MASLD, incident MASLD, and persistent MASLD, with HCC risk was evaluated using multivariable-adjusted Cox proportional hazards regression. RESULTS: Among the 5,080,410 participants with 39,910,331 person-years of follow-up, 4,801 participants developed HCC. The incidence of HCC in participants with resolved, incident, and persistent MASLD was approximately 2.2-, 2.3-, and 4.7-fold higher, respectively, than that in those with persistent non-MASLD among the Korean adult population. When stratifying the participants according to the evolutionary change in MASLD status, persistent (adjusted hazard ratio [aHR], 2.94; 95% confidence interval [CI], 2.68-3.21; P<0.001), incident (aHR, 1.85; 95% CI, 1.63-2.10; P<0.001), and resolved MASLD (aHR, 1.33; 95% CI, 1.18-1.50; P<0.001) had an increased risk of HCC compared to persistent non-MASLD. CONCLUSION: The evolutionary changes in MASLD were associated with the differential risk of HCC independent of metabolic risk factors and concomitant medications, providing additional information on the risk of HCC stratification in patients with MASLD.

Associations of cholecystectomy with metabolic health changes and incident cardiovascular disease: a retrospective cohort study.

Although some studies conducted about the risk of cholecystectomy and cardiovascular disease, there was a limit to explaining the relationship. We investigated the short-term and long-term relationship between cholecystectomy and cardiovascular disease, and evidence using the elements of the metabolic index as an intermediate step. It was a retrospective cohort study and we used the National Health Insurance Service database of South Korea between 2002 and 2015. Finally, 5,210 patients who underwent cholecystectomy and 49,457 at 1:10 age and gender-matched controls of subjects were collected. The main results was estimated by Multivariate Cox proportional hazard regression to calculate the hazard ratio (HR) with 95% confidence interval (CI) for risk of cardiovascular disease after cholecystectomy. Regarding short-term effects of cholecystectomy, increased risk of cardiovascular disease (aHR 1.35, 95% CI 1.15-1.58) and coronary heart disease (aHR 1.77, 95% CI 1.44-2.16) were similarly seen within 2 years of surgery. When analyzing the change in metabolic risk factors, cholecystectomy was associated with a change in systolic blood pressure (adjusted mean [aMean]: 1.51, 95% CI: [- 1.50 to - 4.51]), total cholesterol (aMean - 14.14, [- 20.33 to 7.95]) and body mass index (aMean - 0.13, [- 0.37 to 0.11]). Cholecystectomy patients had elevated risk of cardiovascular disease in the short-term, possibly due to the characteristics of the patient before surgery. The association of cholecystectomy and cardiovascular disease has decreased after 2 years in patients who underwent cholecystectomy, suggesting that because of improvement of metabolic health, cholecystectomy-associated elevation of cardiovascular disease risk may be ameliorated 2 years after cholecystectomy.

Reversal of nonalcoholic fatty liver disease reduces the risk of cardiovascular disease among Korean.

Nonalcoholic fatty liver disease (NAFLD) is considered an independent risk factor for the development of cardiovascular disease. However, the association between changes in NAFLD status and the risk of cardiovascular disease (CVD) remains uncertain. Starting January 1, 2013, participants were followed until the occurrence of CVD event, death, or December 31, 2020. This was a population-based cohort study that included data from adults aged ≥ 20, who underwent 2 consecutive health screenings from 2009 to 2012. NAFLD was defined as a Fatty Liver Index ≥ 60 at each screening. The primary endpoint was a CVD event, which encompassed ischemic heart disease and cerebrovascular disease. The association between changes in NAFLD status and the risk of CVD was determined using multivariable Cox proportional hazards regression. This cohort comprised 4656,305 adults with a median age of 53 years. During 36,396,968 person-years of follow-up, 238,933 (5.1%) CVD events were observed. Compared to patients with no NAFLD at both screenings, patients who developed NAFLD at the second screening exhibited an increased risk of CVD (adjusted hazard ratio, 1.15; 95% confidence interval, 1.13-1.17). In contrast, individuals who recovered from NAFLD at the second screening demonstrated a reduced CVD risk compared to those with persistent NAFLD (adjusted hazard ratio, 0.91; 95% confidence interval, 0.90-0.92). The reversal of NAFLD is associated with a reduced risk of CVD. Therefore, focusing on NAFLD treatment could serve as a clinical target for lowering CVD risk.