Background and Aims Patients with primary biliary cholangitis (PBC) often suffer with pruritus. We describe the impact of pruritus on quality of life and how it is managed in a real-world cohort. Methods TARGET-PBC is a longitudinal observational cohort of patients with PBC across the USA. Data include information from medical records for three years prior to the date of consent up to 5 years of follow-up. Enrolled patients were asked to complete patient-reported outcome surveys: PBC-40, 5-D itch, and the PROMIS fatigue survey. Kruskal–Wallis tests were used to compare differences in symptoms between groups. Results A total of 211 patients with completed PRO surveys were included in the current study. PRO respondents were compared with non-respondents in the TARGET-PBC population and were broadly similar. Pruritus was reported in 170 patients (81%), with those reporting clinically significant pruritus (30%) scoring worse across each domain of the PBC-40 and 5-D itch, more frequently having cirrhosis, and having significantly greater levels of fatigue. Patients reporting clinically significant pruritus were more likely to receive treatment, but 33% had never received treatment (no itch = 43.9%, mild itch = 38.3%). Conclusions The prevalence of pruritus was high in this population, and those reporting clinically significant pruritus had a higher likelihood of having advanced disease and worse quality of life. However, this study found that pruritus in PBC is under-treated. This may be due in part to ineffectiveness of current treatments, poor tolerance, or the lack of FDA-approved medications for pruritus.

Dysphagia (swallowing difficulties) is relatively common in the general population, but the prevalence increases with age and poses particular problems in the older patient, potentially compromising nutritional status, complicating the administration of solid medications, increasing the risk of aspiration pneumonia and undermining the quality of life. The repercussions of dysphagia are not only physical but also emotional, affecting patient morale and leading to feelings of social isolation. There are various causes, including carcinoma, stroke and advanced Alzheimer's disease. The diverse range of causes may manifest in a number of different ways, but should always act as a warning sign, which requires further investigation. Management is multidisciplinary, depending on the underlying cause, extent of dysphagia and likely prognosis. This article examines the incidence, causes and management of dysphagia, based on a review of recent literature. The focus is on the nurse's role in the management of this challenging disorder with particular emphasis on the care of the elderly patient.

INTRODUCTION: This study investigates the applicability of the new metabolic dysfunction-associated steatotic liver disease (MASLD) nomenclature to the real-world TARGET-NASH US adult cohort. METHODS: The new MASLD/metabolic steatohepatitis nomenclature was applied to patients enrolled with pragmatic diagnoses of nonalcoholic fatty liver and nonalcoholic steatohepatitis (NASH), and NASH cirrhosis and concordance were determined between the definitions. RESULTS: Approximately 99% of TARGET-NASH participants met the new MASLD diagnostic criteria. Approximately 1,484/1,541 (96.3%, kappa 0.974) nonalcoholic fatty liver patients (metabolic dysfunction-associated steatotic liver), 2,195/2,201 (99.7%, kappa 0.998) NASH patients (metabolic steatohepatitis), and 1,999/2,003 (99.8%, kappa 0.999) NASH cirrhosis patients met the new criteria. DISCUSSION: The new MASLD nomenclature is highly concordant with the previous TARGET-NASH pragmatic definitions.

Background: Nonalcoholic steatohepatitis (NASH), advanced fibrosis, and subsequent cirrhosis and hepatocellular carcinoma are becoming the most common etiology for liver failure and liver transplantation; however, they can only be diagnosed at these potentially reversible stages with a liver biopsy, which is associated with various complications and high expenses. Knowing the difference between the more benign isolated steatosis and the more severe NASH and cirrhosis informs the physician regarding the need for more aggressive management. Objective: We intend to explore the feasibility of using machine learning methods for noninvasive diagnosis of NASH and advanced liver fibrosis and compare machine learning methods with existing quantitative risk scores. Methods: We conducted a retrospective analysis of clinical data from a cohort of 492 patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD), NASH, or advanced fibrosis. We systematically compared 5 widely used machine learning algorithms for the prediction of NAFLD, NASH, and fibrosis using 2 variable encoding strategies. Then, we compared the machine learning methods with 3 existing quantitative scores and identified the important features for prediction using the SHapley Additive exPlanations method. Results: The best machine learning method, gradient boosting (GB), achieved the best area under the curve scores of 0.9043, 0.8166, and 0.8360 for NAFLD, NASH, and advanced fibrosis, respectively. GB also outperformed 3 existing risk scores for fibrosis. Among the variables, alanine aminotransferase (ALT), triglyceride (TG), and BMI were the important risk factors for the prediction of NAFLD, whereas aspartate transaminase (AST), ALT, and TG were the important variables for the prediction of NASH, and AST, hyperglycemia (A1c), and high-density lipoprotein were the important variables for predicting advanced fibrosis. Conclusions: It is feasible to use machine learning methods for predicting NAFLD, NASH, and advanced fibrosis using routine clinical data, which potentially can be used to better identify patients who still need liver biopsy. Additionally, understanding the relative importance and differences in predictors could lead to improved understanding of the disease process as well as support for identifying novel treatment options.

Background: Patient reports of collaborative goal setting have repeatedly been associated with improved health outcomes, and the American Diabetes Association specifically encourages collaborative goal setting as a component of high quality care. Current limitations in our understanding of what needs to transpire for patients to denote goal setting as “collaborative”, remain a barrier to fostering collaborative goal setting in practice. Methods: Four focus groups were conducted among 19 patients with diabetes. A semi-structured focus group guide was used to explore patient perceptions of collaborative goal setting and what needed to happen for goals to be considered collaboratively set. Focus group transcripts were coded using thematic analysis. Results: Collaborative goal setting was described by patients as occurring within the context of a caring relationship where patients and health care providers: (1) listen and learn from each other; (2) share ideas; (3) agree on a measurable objective; and (4) support goal achievement. Patients also articulated clear responsibilities for themselves and clinicians and described collaborative goal setting as a process that occurs over time. Conclusions: Patients perceived collaborative goal setting as a multidimensional process that occurs over time within the context of a caring relationship and encompasses distinct patient and clinician responsibilities.

INTRODUCTION: Risk of cardiovascular (CV) events is estimated by the Framingham Risk Score (FRS), Pooled Cohort Equation (PCE), and the American Heart Association Predicting Risk of cardiovascular disease (CVD) EVENTs (PREVENT) equation. The applicability of these risk tools in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) is uncertain. This study sought to determine the accuracy of the FRS, PCE, and PREVENT in a real-world cohort of patients with MASLD. METHODS: This analysis included US adults 30 years and older with MASLD in the TARGET-NASH study. Five to ten-year CV risk was estimated using original and recalibrated versions of the FRS and PCE, and original PREVENT equation. Discrimination among prediction models was assessed using the Harrell concordance statistic, and calibration was evaluated using a modified Hosmer Lemeshow test comparing observed and predicted CV events. Logistic regression was used to assess the contribution of liver disease to observed CV events. RESULTS: Overall, 1,090 patients were included. There was an increase in observed CV events from metabolic dysfunction-associated steatotic livers to cirrhosis. FRS demonstrated a weak ability to identify patients who went on to experience a CV event (C-statistic 0.58, 95% confidence interval 0.52–0.63) as did the American Heart Association PREVENT model (C-statistic 0.60, 95% confidence interval 0.54–0.65). Both the FRS and PCE demonstrated a significant lack of calibration ( P < 0.01), with overestimation in the highest deciles and underestimation in the lowest deciles of predicted risk. DISCUSSION: Commonly used tools to identify CV risk performed poorly in a cohort of patients with MASLD. Because CV related death is the greatest source of mortality among patients with MASLD, better risk assessment tools are required.