Bristol Stool Scale as a Determinant of Hepatic Encephalopathy Management in Patients With Cirrhosis.

INTRODUCTION: Bowel movement (BM) frequency is used to titrate lactulose for hepatic encephalopathy (HE). However, stool consistency using the Bristol stool scale (BSS, 0-7) is often ignored. METHODS: The study included pre-BSS and post-BSS cohorts. BSS was incorporated into decision-making after training in outpatients with cirrhosis. Two to 3 BMs/d and BSS 3-4 were considered normal, whereas the rest were considered high or low; concordance between the metrics was evaluated. Medication changes and 6-month admissions were compared between this group (post-BSS) and a comparable previous group (pre-BSS). Concordance and regression analyses for all-cause admissions and HE-related admissions were performed, and comparisons were made for HE-related medication stability. In the longitudinal analysis, an outpatient group seen twice was analyzed for BSS and BMs. RESULTS: In the post-BSS cohort, 112 patients were included with only 46% BSS and BMs concordance and modest BSS/BMs correlation (r = 0.27, P = 0.005). Compared with a pre-BSS cohort (N = 114), there was a lower 6-month total (4% vs 0.36%, P < 0.001) or HE-related admission (1% vs 0.12%, P = 0.002). Regression showed model for end-stage liver disease (odds ratio [OR]: 1.10, P = 0.003) and pre-BSS/post-BSS (OR: 0.04, P < 0.001) for all-cause admissions and HE (OR: 3.59, P = 0.04) and preera/postera (OR: 0.16, P = 0.02) for HE-related admissions as significant. HE medication regimens were more stable post-BSS vs pre-BSS (32% vs 20%, P = 0.04), which was due to patients with BSS > BMs (P = 0.02). In the longitudinal analysis, 33 patients without medication changes or underlying clinical status changes were tested 36 ± 24 days apart. No changes in BSS (P = 0.73) or BMs (P = 0.19) were found. DISCUSSION: BSS is complementary and additive to BM frequency, can modulate the risk of readmissions and stabilize HE-related therapy changes in outpatients with cirrhosis, and could help personalize HE management.

Gastric intestinal metaplasia: when to treat? How to treat?

PURPOSE OF REVIEW: Gastric intestinal metaplasia (GIM) is an attractive target for surveillance and treatment as it can progress to gastric adenocarcinoma (GAC). Yet, GIM remains a challenging area for clinicians as most patients do not progress to cancer, and there are conflicting data regarding the benefits of surveillance and therapy. This review aims to summarize recently published GIM surveillance guidelines, to discuss, which patients with GIM may benefit from treatment, and to review pivotal and recent literature on GIM therapy. RECENT FINDINGS: Guidelines published by American, British, and European gastroenterology societies do not recommend universal surveillance, but do suggest endoscopic surveillance in patients with risk factors for progression to GAC. Although light examination for at least 7 min and mapping biopsies may increase yield for dysplasia and GAC. In randomized trials, Helicobacter pylori eradication reduced risk of dysplasia and cancer. In GIM with visible dysplasia and early-stage GAC, endoscopic resection improves quality of life without reducing survival compared with surgery. Endoscopic ablation therapies have shown promise for invisible or extensive dysplasia. SUMMARY: Endoscopic resection is appropriate for visible dysplasia and early-stage GAC without high-risk features that persists despite H. pylori eradication therapy. Prospective studies are needed to assess the utility of endoscopic ablation in GIM.

Clinical outcomes and financial impacts of malnutrition in sepsis.

BACKGROUND: In the United States in 2014 approximately 1.7 million adults were hospitalized with sepsis, resulting in about 270,000 deaths. Malnutrition in hospitalized patients contributes to increased morbidity, mortality, and costs, especially in the critically ill population. AIM: Our goal was to investigate the prevalence of malnutrition in sepsis and the impact it has on clinical and financial outcomes in our most critically ill patients. METHODS: We implemented nutritional screening by a registered dietitian of 1000 patients admitted with sepsis to specialized care units. We calculated the prevalence of malnutrition, and compared outcomes including mortality, length of stay, and financial costs. RESULTS: About 10% of patients with sepsis admitted to our specialized care units were diagnosed with malnutrition on admission after implementation of mandatory assessment. CONCLUSIONS: Although mortality did not reach statistical significance, these patients had more comorbidities, longer hospital stays, and higher total costs.

Large language models: a primer and gastroenterology applications.

Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology.

Large language models in gastroenterology: a simplified overview for clinicians This text discusses the recent advancements in large language models (LLMs), like ChatGPT, which have significantly advanced artificial intelligence. These models can create specific, high-quality text responses without needing extensive training data or complex programming. They show great promise in transforming various aspects of clinical healthcare, particularly in improving patient care, medical education, and research. This article focuses on how LLMs can be applied in the field of gastroenterology. It explains the technical aspects of LLMs, their strengths and weaknesses, and how to use them effectively and safely. The text also explores how LLMs could be used in clinical practice, education, and research in gastroenterology. Finally, it discusses the challenges in implementing these models and the ongoing efforts to overcome them, aiming to provide gastroenterologists with the basic knowledge needed to engage more actively in the development and use of this emerging technology.

Novel insights on the effect of nicotine in a murine colitis model.

Studies showed that nicotine has a positive influence on symptoms of ulcerative colitis. In the present study, we explored the effect of nicotine treatment using different routes of administration in the dextran sodium sulfate (DSS) colitis mouse model. We also investigated the effects of cotinine, a major metabolite of nicotine, in the model. C57BL6 adult male mice were given DSS solution freely in the drinking water for seven consecutive days, and tap water was given thereafter. Disease severity, length of the colon, colon tissue histology, and inflammatory markers, including colonic myeloperoxidase activity and colonic tumor necrosis factor-α levels, were evaluated. The effect of nicotine and cotinine treatments via various different routes of administration were examined the DSS model. In addition, we measured the plasma levels of nicotine and cotinine in our treatment protocols. Administration of low, but not high, doses of oral nicotine in DSS-treated mice resulted in a significant decrease in disease severity, histologic damage scores, as well as colonic level of tumor necrosis factor-α. However, the anti-inflammatory effect of nicotine was not seen after chronic s.c. or minipump infusion of the drug. Differences in plasma levels of nicotine and cotinine do not seem to account for this lack of effect. Finally, oral cotinine alone failed to show a significant effect in the DSS model of colitis. These results highlight that dose and route of administration play a critical role in the protective effect of nicotine in the DSS mouse colitis model.

Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.

There is a rising interest in use of big data approaches to personalize treatment of inflammatory bowel diseases (IBDs) and to predict and prevent outcomes such as disease flares and therapeutic nonresponse. Machine learning (ML) provides an avenue to identify and quantify features across vast quantities of data to produce novel insights in disease management. In this review, we cover current approaches in ML-driven predictive outcomes modeling for IBD and relate how advances in other fields of medicine may be applied to improve future IBD predictive models. Numerous studies have incorporated clinical, laboratory, or omics data to predict significant outcomes in IBD, including hospitalizations, outpatient corticosteroid use, biologic response, and refractory disease after colectomy, among others, with considerable health care dollars saved as a result. Encouraging results in other fields of medicine support efforts to use ML image analysis-including analysis of histopathology, endoscopy, and radiology-to further advance outcome predictions in IBD. Though obstacles to clinical implementation include technical barriers, bias within data sets, and incongruence between limited data sets preventing model validation in larger cohorts, ML-predictive analytics have the potential to transform the clinical management of IBD. Future directions include the development of models that synthesize all aforementioned approaches to produce more robust predictive metrics.

Burden of Primary Biliary Cholangitis Among Inpatient Population in the United States.

Primary biliary cholangitis (PBC) is an autoimmune liver disease that can lead to cirrhosis and liver failure. Our aim was to assess the recent trends in the mortality rates and health care utilization of patients with PBC seen in the inpatient setting in the United States. We used the National (Nationwide) Inpatient Sample data (2005-2014). The study population included adults with PBC, using International Classification of Diseases, Ninth Revision codes. Trends in PBC-related discharges, total charges, length of stay (LoS), and in-hospital mortality were evaluated. Hierarchical generalized linear models were performed for determining predictors of mortality and total hospital charges. Between the study years of 2005 and 2014, a total of 22,665 hospitalized cases with PBC were identified (mean age 63 years; 84% female, 76% white). The number of PBC-related discharges increased from 3.24 per 100,000 in 2005 to 3.68 per 100,000 in 2014, with an average annual increase of 1.4% (95% confidence interval [CI]: 0.4%-2.4%). Fifty-seven percent had Medicare as their primary payer, 37% had cirrhosis, and 1.3% had hepatocellular carcinoma. Between 2005 and 2014, the average total charges for PBC increased from $53,901 to $57,613 (annual percent change [APC], 1.7%; 95% CI: -0.2%-3.5%), LoS decreased from 6.9 days to 5.4 days (APC, -2.2%; 95% CI: -3.2% to -1.1%), and mortality rate decreased from 3.8% to 2.8% (APC, -5.4%; 95% CI: -8.4% to -2.4%). Multivariable analysis revealed that ascites were independently associated with increased risk of in-hospital mortality (odds ratio: 1.77; 95% CI: 1.50-2.08), increased charge (percent change: 22.5%; 95% CI: 18.6%-26.7%), and increased LoS (percent change: 29.7%; 95% CI: 25.7%-33.9%). Conclusion: The number of PBC cases has increased in recent years. Mortality and LoS have decreased, and the total charges have remained the same.

Chronic kidney disease in patients with chronic hepatitis C virus infection.

Hepatitis C virus (HCV) infection affects many organs in the body, including the liver, kidneys, skin, joints and others. Although the hepatic manifestation of HCV has been widely studied, the extrahepatic manifestations of HCV have not been fully appreciated. Studies have shown that patients with HCV have a higher risk of chronic kidney disease and end-stage renal disease, as well as poorer outcomes after kidney transplantation. Given these findings, it is important to screen HCV patients for presence of renal impairment in a timely manner. Current guidelines recommend screening for kidney disease at the time of HCV diagnosis, along with annual urinalysis and creatinine checks. It is important to note that chronic HCV infection has been associated with a number of renal disorders, including cryoglobulinemic glomerulonephritis, membranoproliferative glomerulonephritis, membranous nephropathy, focal segmental glomerulosclerosis, IgA nephropathy, fibrillary and immunotactoid glomerulopathies, and hepatorenal syndrome. Of these, while HRS can be reversible post-transplantation, cryoglobulinemic glomerulonephritis is common and is primarily caused by mixed cryoglobulinemia. These patients may be asymptomatic or may present with hematuria, proteinuria, nephrotic syndrome, or impaired renal function only detected by laboratory data. In this review, we will provide an up-to-date assessment of these renal complications in patients with HCV infection.

Among Patients With NAFLD, Treatment of Dyslipidemia Does Not Reduce Cardiovascular Mortality.

Dyslipidemia is one of the common risk factors for NAFLD and is associated with cardiovascular (CV) mortality, which is the most common cause of death in NAFLD. Lipid-lowering agents (LLAs) are used to reduce CV events in the general population. Our aim was to assess whether the use of LLAs in patients with NAFLD can reduce the risk of CV mortality. We used the third National Health and Nutrition Examination Survey mortality linked files. Mortality was determined from the National Death Index records through 2011. NAFLD was diagnosed by ultrasound after exclusion of other causes of liver disease. After inclusion and exclusion, the cohort consisted of 2,566 patients with NAFLD (45.8% < 45 years of age, 52.8% male, 75.4% white). Those who were taking LLAs were more likely to be older, non-Hispanic white, and had significantly higher rates of diabetes mellitus (DM), hyperlipidemia, hypertension, metabolic syndrome, and history of CV disease (CVD) (all P< 0.01). In our multivariate analysis, DM was an independent predictor of overall mortality (adjusted hazard ratio [aHR]: 1.79 [95% confidence interval (CI): 1.40-2.30]) and CV mortality (aHR: 1.89 [95% CI: 1.08-3.30]). History of CVD was associated with both overall (aHR: 2.03 [95% CI: 1.57-2.63]) and CV mortality (aHR: 3.69 [95% CI: 2.23-6.08]). In contrast, the use of statins and other LLAs was not associated with reduction in overall (aHR = 0.95 [95% CI: 0.37-2.44] and aHR = 1.43 [95% CI: 0.99-2.07]) and CV mortality (aHR = 1.20 [95% CI: 0.26-5.54] and aHR = 1.63 [95% CI: 0.70-3.76]). Conclusion: The use of statins and other LLAs did not reduce the increased risk of overall or CV mortality in NAFLD.