Inequity in end-of-life care for patients with chronic liver disease in England.

BACKGROUND AND AIMS: The World Health Assembly recommends integration of palliative care into treatment of patients with any life-limiting condition, yet patients with non-malignant disease are less likely to receive specialist palliative care (SPC). This study compares SPC offered to patients with hepatocellular carcinoma (HCC) versus patients with chronic liver disease without HCC (CLD without HCC). METHODS: Patients who died from CLD or HCC over 5 years (2013-2017) in England were identified using a dataset linking national data on all hospital admissions (Hospital Episode Statistics - HES) with national mortality data from the Office for National Statistics (HES - ONS). The primary outcome was the proportion of patients who received inpatient SPC in their last year of life (LYOL). Secondary outcomes were (1) early inpatient SPC input and (2) the proportion dying in a hospice. The outcomes were compared between patients with HCC and CLD without HCC. RESULTS: 29 669 patients were identified, 8143 of whom had HCC. Patients with HCC were significantly more likely to receive inpatient SPC input-adjusted OR 3.74 (95% CI 3.52-3.97) and early inpatient SPC input-adjusted OR 7.26 (95% CI 6.38-8.25) and die in a hospice OR 8.23 (95% CI 7.33-9.24) than patients with CLD without HCC. CONCLUSIONS: These data highlight the stark inequity in access to SPC services between patients with HCC and patients with CLD without HCC in England. Addressing these inequities will improve end-of-life care for patients with CLD.

Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation.

Background and Aims: Liver fibrosis is a key risk factor for cirrhosis, hepatocellular carcinoma and end stage liver failure. The National Institute for Health and Care Excellence guidelines for assessment for advanced (≥F3) liver fibrosis in people with nonalcoholic fatty liver disease recommend the use of enhanced liver fibrosis (ELF) test, followed by vibration-controlled transient elastography (VCTE). Performance of ELF at predicting significant (≥F2) fibrosis in real-world practice is uncertain. To assess the accuracy of ELF using VCTE; investigate the optimum ELF cutoff value to identify ≥F2 and ≥F3; and develop a simple algorithm, with and without ELF score, for detecting ≥F2. Methods: Retrospective evaluation of patients referred to a Community Liver Service for VCTE, Jan-Dec 2020. Assessment included: body mass index (BMI), diabetes status, alanine aminotransferase (ALT) levels, ELF score and biopsy-validated fibrosis stages according to VCTE. Results: Data from 273 patients were available. n=110 patients had diabetes. ELF showed fair performance for ≥F2 and ≥F3, area under the curve (AUC) = 0.70, 95% confidence interval (CI) 0.64-0.76 and AUC=0.72, 95% CI: 0.65-0.79 respectively. For ≥F2 Youden's index for ELF=9.85 and for ≥F3, ELF=9.95. Combining ALT, BMI, and HbA1c (ALBA algorithm) to predict ≥F2 showed good performance (AUC=0.80, 95% CI: 0.69-0.92), adding ALBA to ELF improved performance (AUC=0.82, 95% CI: 0.77-0.88). Results were independently validated. Conclusions: Optimal ELF cutoff for ≥F2 is 9.85 and 9.95 for ≥F3. ALT, BMI, and HbA1c (ALBA algorithm) can stratify patients at risk of ≥F2. ELF performance is improved by adding ALBA.

The role of natural experiments in hepatology research: filling the gap between clinical trials and service evaluations.

Research developing and testing interventions that address the social determinants of liver disease are urgently needed; however, this cannot be achieved using conventional clinical research designs. A different approach is needed to conduct widely applicable, inclusive, and community-based research that addresses upstream factors driving liver morbidity. Natural experimental studies encompass a well-established field of research methodology that is less familiar to clinical hepatologists than conventional research methods such as the randomized control trial. The key strength of natural experiments is that, when robustly designed, they can be used to imply causality from routinely collected data. As such, they are well placed to test the impact of community interventions that aim to address social determinants of liver disease that cannot feasibly be assessed in a randomized control trial. In this review, we define natural experiments and their potential utility. We then work through examples of where they have already been used in clinical hepatology to highlight a range of research designs, analytical approaches, and best practices regarding their conduct and reporting. In doing so, we hope to equip clinical hepatologists with another tool to ensure the hepatology community can meet the global liver disease epidemic with evidence-based interventions.

Noninvasive serum biomarkers for liver fibrosis in NAFLD: current and future.

In the last 20 years, noninvasive serum biomarkers to identify liver fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) have been developed, validated against liver biopsy (the gold standard for determining the presence of liver fibrosis) and made available for clinicians to use to identify ≥F3 liver fibrosis. The aim of this review is firstly to focus on the current use of widely available biomarkers and their performance for identifying ≥F3. Secondly, we discuss whether noninvasive biomarkers have a role in identifying F2, a stage of fibrosis that is now known to be a risk factor for cirrhosis and overall mortality. We also consider whether machine learning algorithms offer a better alternative for identifying individuals with ≥F2 fibrosis. Thirdly, we summarise the utility of noninvasive serum biomarkers for predicting liver related outcomes (e.g., ascites and hepatocellular carcinoma) and non-liver related outcomes (e.g., cardiovascular-related mortality and extra hepatic cancers). Finally, we examine whether serial measurement of biomarkers can be used to monitor liver disease, and whether the use of noninvasive biomarkers in drug trials for non-alcoholic steatohepatitis can accurately, compared to liver histology, monitor liver fibrosis progression/regression. We conclude by offering our perspective on the future of serum biomarkers for the detection and monitoring of liver fibrosis in NAFLD.

Testing to sustain hepatitis C elimination targets in people who inject drugs: A network-based model.

Little is known about the level of testing required to sustain elimination of hepatitis C (HCV), once achieved. In this study, we model the testing coverage required to maintain HCV elimination in an injecting network of people who inject drugs (PWID). We test the hypothesis that network-based strategies are a superior approach to deliver testing. We created a dynamic injecting network structure connecting 689 PWID based on empirical data. The primary outcome was the testing coverage required per month to maintain prevalence at the elimination threshold over 5 years. We compared four testing strategies. Without any testing or treatment provision, the prevalence of HCV increased from the elimination threshold (11.68%) to a mean of 25.4% (SD 2.96%) over the 5-year period. To maintain elimination with random testing, on average, 4.96% (SD 0.83%) of the injecting network needs to be tested per month. However, with a 'bring your friends' strategy, this was reduced to 3.79% (SD 0.64%) of the network (p < .001). The addition of contact tracing improved the efficiency of both strategies. In conclusion, we report that network-based approaches to testing such as 'bring a friend' initiatives and contact tracing lower the level of testing coverage required to maintain elimination.

Injecting network structure determines the most efficient strategy to achieve Hepatitis C elimination in people who inject drugs.

Transmission of Hepatitis C (HCV) continues via sharing of injection equipment between people who inject drugs (PWID). Network-based modelling studies have produced conflicting results about whether random treatment is preferable to targeting treatment at PWID with multiple partners. We hypothesise that differences in the modelled injecting network structure produce this heterogeneity. The study aimed to test how changing network structure affects HCV transmission and treatment effects. We created three dynamic injecting network structures connecting 689 PWID (UK-net, AUS-net and USA-net) based on published empirical data. We modelled HCV in the networks and at 5 years compared prevalence of HCV 1) with no treatment, 2) with randomly targeted treatment and 3) with treatment targeted at PWID with the most injecting partnerships (degree-based treatment). HCV prevalence at 5 years without treatment differed significantly between the three networks (UK-net (42.8%) vs. AUS-net (38.2%), p < 0.0001 and vs. USA-net (54.0%), p < 0.0001). In the treatment scenarios UK-net and AUS-net showed a benefit of degree-based treatment with a 5-year prevalence of 1.0% vs. 9.6% p < 0.0001 and 0.15% vs. 0.44%, p < 0.0001. USA-net showed no significant difference (29.3% vs. 29.2%, p = 0.0681). Degree-based treatment was optimised with low prevalence, moderate treatment coverage conditions whereas random treatment was optimised in low treatment coverage, high prevalence conditions. In conclusion, injecting network structure determines the transmission rate of HCV and the most efficient treatment strategy. In real-world injecting network structures, the benefit of targeting HCV treatment at individuals with multiple injecting partnerships may have been underestimated.