Background: The study compared the readability, grade level, understandability, actionability, and accuracy of standard patient educational material against artificial intelligence chatbot-derived patient educational material regarding cirrhosis. Methods: An identical standardized phrase was used to generate patient educational materials on cirrhosis from 4 large language model-derived chatbots (ChatGPT, DocsGPT, Google Bard, and Bing Chat), and the outputs were compared against a pre-existing human-derived educational material (Epic). Objective scores for readability and grade level were determined using Flesch-Kincaid and Simple Measure of Gobbledygook scoring systems. 14 patients/caregivers and 8 transplant hepatologists were blinded and independently scored the materials on understandability and actionability and indicated whether they believed the material was human or artificial intelligence-generated. Understandability and actionability were determined using the Patient Education Materials Assessment Tool for Printable Materials. Transplant hepatologists also provided medical accuracy scores. Results: Most educational materials scored similarly in readability and grade level but were above the desired sixth-grade reading level. All educational materials were deemed understandable by both groups, while only the human-derived educational material (Epic) was considered actionable by both groups. No significant difference in perceived actionability or understandability among the educational materials was identified. Both groups poorly identified which materials were human-derived versus artificial intelligence-derived. Conclusions: Chatbot-derived patient educational materials have comparable readability, grade level, understandability, and accuracy to human-derived materials. Readability, grade level, and actionability may be appropriate targets for improvement across educational materials on cirrhosis. Chatbot-derived patient educational materials show promise, and further studies should assess their usefulness in clinical practice. Export

Background. Hepatocellular carcinoma (HCC) is a leading cause of cancer morbidity and mortality. Findings of microvascular invasion (MVI) in patients with HCC have emerged as an important prognostic factor for poor survival after tumor resection. Aim. This study evaluated the relation between MVI and HCC within various anatomical Couinaud’s segments of the liver. Method. A multicenter retrospective review of HCC records was conducted from 2012 to 2017. HCC cases were identified using ICD-9 and 10 codes 155, C22.0, and C22.8. HCC patients who underwent liver transplants were included in this study. Liver segment of the location of HCC was obtained from radiographic records, and MVI information was obtained from pathology reports. Segmental distributions of HCC in MVI versus non-MVI groups were compared using Wilcoxon rank sum tests. value was set at <0.05. Results. We analyzed 120 HCC patients who underwent liver transplantation. The mean age of our cohort was 57 years, and the most common etiology of liver disease was hepatitis C at 58.3%. The median HCC size was 3.1 cm, and MVI was present in 23.3% of the explanted specimens. MVI was 2 to 3 times significantly higher in patients with HCC affecting segments 2 and 3 and segments 4b and 5 ( ). Moreover, median survival was significantly lower in patients with MVI versus those without MVI (50 vs. 137 months, ). Conclusion. MVI was significantly higher in HCC tumors located in liver segments 2 and 3 and 4b and 5, and survival was lower in patients with MVI compared with those without.

Hepatobiliary diseases and their complications cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may exacerbate the underlying condition and lead to unfavorable prognosis. To develop and validate prognostic biomarkers for the prediction of complications of cholestatic liver disease based on urinary BA indices, liquid chromatography-tandem mass spectrometry was used to analyze urine samples from 257 patients with cholestatic liver diseases during a 7-year follow-up period. The urinary BA profile and non-BA parameters were monitored, and logistic regression models were used to predict the prognosis of hepatobiliary disease-related complications. Urinary BA indices were applied to quantify the composition, metabolism, hydrophilicity, and toxicity of the BA profile. We have developed and validated the bile-acid liver disease complication (BALDC) model based on BA indices using logistic regression model, to predict the prognosis of cholestatic liver disease complications including ascites. The mixed BA and non-BA model was the most accurate and provided higher area under the receiver operating characteristic (ROC) and smaller akaike information criterion (AIC) values compared to both non-BA and MELD (models for end stage liver disease) models. Therefore, the mixed BA and non-BA model could be used to predict the development of ascites in patients diagnosed with liver disease at early stages of intervention. This will help physicians to make a better decision when treating hepatobiliary disease-related ascites.