Association of echocardiography-related parameters with the prognosis of decompensated cirrhosis: a retrospective cohort study.

BACKGROUND: Cardiac morphology and function, which are conventionally evaluated by echocardiography, are often abnormal in decompensated cirrhosis. We aimed to evaluate the association of echocardiography-related parameters with prognosis in cirrhosis. METHODS: This retrospective study included 104 decompensated cirrhotic patients, in whom cardiac structure and function were measured by echocardiography, including mitral inflow early diastolic velocity/mitral inflow late diastolic velocity (E/A), left atrium diameter, left ventricular end-diastolic dimension, interventricular septal thickness, left ventricular posterior wall thickness, right atrial transverse diameter, right atrial longitudinal diameter, right ventricular dimension (RVD), stroke volume, cardiac output, left ventricular ejection fraction, and fractional shortening. Cox regression and competing risk analyses and Kaplan-Meier and Nelson-Aalen cumulative risk curves were used to evaluate their associations with further decompensation and death in cirrhotic patients, if appropriate. RESULTS: Lower RVD was a predictor of further decompensation in Cox regression (adjusted by Child-Pugh score: p = 0.138; adjusted by MELD score: p = 0.034) and competing risk analyses (p = 0.003), and RVD ≤17 mm was significantly associated with higher cumulative incidence of further decompensation in Kaplan-Meier (p = 0.002) and Nelson-Aalen cumulative risk curves (p = 0.002). E/A ≤ 0.8 was a significant predictor of death in Cox regression (adjusted by Child-Pugh score: p = 0.041; adjusted by MELD score: p = 0.045) and competing risk analyses (p = 0.024), and E/A ≤ 0.8 was significantly associated with higher cumulative incidence of death in Kaplan-Meier (p = 0.023) and Nelson-Aalen cumulative risk curves (p = 0.024). Other echocardiography-related parameters were not significantly associated with further decompensation or death. CONCLUSION: RVD and E/A may be considered for the prognostic assessment of decompensated cirrhosis.

Artificial Intelligence in Liver Diseases: Recent Advances.

Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.