Markers of cardiac dysfunction associated with inflammation in a cohort of patients with acute decompensation of cirrhosis.

BACKGROUND AND AIMS: Inflammation underpinning acute decompensation (AD) of liver disease is an important driver for the development of acute-on-chronic liver failure or death. We aimed to investigate associations between inflammatory biomarkers and impaired cardiac function in patients admitted for AD of cirrhosis. METHODS: This is a retrospective analysis of a well-characterized prospective cohort of patients with AD of liver disease admitted to a tertiary referral center. All patients had echocardiographic assessment of cardiac function and serum samples at admission. We reclassified patients according to the CLIF-C AD score, measured inflammatory (IL-6, IL-8, TNF-ɑ, CD206) and cardiac-specific (NT-proBNP, troponin T) biomarkers and tested for associations with echocardiographic parameters of cardiac function. We explored the impact on outcome of these factors in multivariate analysis. RESULTS: We included 70 patients (58 ±â€…10 years, 28 women), with a mean CLIF-C AD score of 47 ±â€…7. Thirty-nine patients (56%) fulfilled the echocardiographic criteria for cardiac dysfunction. We found associations between parameters of diastolic dysfunction and serum concentrations of IL-6 and CD206. Echocardiographic parameters of cardiac function were not associated with markers of liver dysfunction such as the CLIF-C AD score. In multivariate analysis higher MELD, higher NT-proBNP, and IL-8 concentrations as well as the absence of echocardiographic criteria for cardiac dysfunction significantly associated with death during follow-up. CONCLUSION: We found evidence in favor of a clinically relevant link between serum biomarkers of inflammation (IL-6, CD206) and echocardiographic signals of cardiac dysfunction in patients with acutely decompensated cirrhosis.

The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now.

Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.