Survival prediction among patients with non-cancer-related end-stage liver disease

Median Meld at Transplant Minus 3 Reduces the Mortality of Non-Hepatocellular Carcinoma Patients on the Liver Transplant Waitlist

Liver transplants (LTs) are prioritized by mortality risk, which is estimated by MELD scores. Since hepatocellular carcinoma (HCC) patients present with lower MELD scores, they are allocated MELD exception points. Concerns persist that HCC recipients are over-prioritized, resulting in disproportionate waitlist mortality among non-HCC patients. We assessed whether the Median Meld at Transplant minus 3 (MMaT-3) scoring system would balance waitlist mortality and transplantation rates between HCC and non-HCC patients. We reviewed 266 patient charts listed for an LT from 2015 to 2023; 46.2% were listed in the MMaT-3 era. Amongst non-HCC patients, MMaT-3 implementation significantly increased 1-year transplant rate and reduced 1-year waitlist mortality among non-HCC patients (p = 0.003). Pre-MMaT-3 gaps in transplantation (p = 0.004) and waitlist dropout (p = 0.01) were eliminated post-implementation (p > 0.05). Amongst HCC patients, MMaT-3 implementation had no impact on the 1-year transplant rate (p = 0.92) or 1-year waitlist mortality (p = 0.66). Fine-gray proportional hazard multivariable analysis revealed that MMaT-3 significantly reduced waitlist mortality among non-HCC patients (asHR: 0.44, 95% CI [0.23, 0.83], p = 0.01) and limited impact on HCC patients (p = 0.31). MMaT-3 allocation did not significantly alter 2-year post-transplant survival for both populations. We show that the MMaT-3 system decreased the waitlist mortality of non-HCC patients with limited impacts on outcomes for HCC patients listed for an LT.

What is known about the care and support provided for an ageing population with lived experience of chronic viral hepatitis as they near end-of-life: A scoping review

Ageing with a chronic hepatitis B (HBV) or hepatitis C (HCV) infection is an emerging public health priority. For people living with chronic viral hepatitis, their disease progression into old age is both underpinned by their existing blood borne virus and the potential emergence of other infectious and non-infectious conditions. These twinned pathways bring additional challenges to the care and support for people as they near end of life. This scoping review sought to examine what is known about the experiences of the end-of-life phase of an increasing population ageing with HBV and HCV in studies conducted in high-income settings and published in peer reviewed literature (2010-2021). In interpreting this literature, we found that challenges in determining the end-of life phase for people with lived experience of HBV or HCV are exacerbated by the conflation of aetiologies into a singular diagnosis of end-stage liver disease. Studies overwhelmingly reported the clinical aspects of end-of-life care (i.e. prognosis assessment and symptom management) with less attention paid to educative aspects (i.e. advance care directives and surrogate decision makers, discussion of treatment options and determining goals of care). Psychosocial interventions (i.e. quality of life beyond symptom management, including emotional/spiritual support and family and bereavement support) received limited attention in the literature, though there was some recognition that psychosocial interventions should be part of end-of-life care provision. Given the focus on the prominent disease presentation of liver cirrhosis and/or end-stage liver disease, the social and cultural dimensions of these infections have received less attention in the literature on end-of-life in the context of chronic viral hepatitis.

MELD-Na Accurately Predicts 6-Month Mortality in Patients With Decompensated Cirrhosis: Potential Trigger for Hospice Referral

GOAL

The goal of this study was to determine the accuracy of Model of End-stage Liver Disease-Sodium (MELD-Na) in predicting 6-month mortality for patients listed for liver transplantation on the United Network of Organ Sharing (UNOS) waitlist.

BACKGROUND

End-stage liver disease patients underutilize hospice services despite significant morbidity and mortality associated with advanced liver disease. A well-known barrier to hospice referral is clinician uncertainty in identifying patients with an expected survival of <6 months, a requirement for a referral.

METHODS

Retrospective cross-sectional analysis was performed from UNOS data spanning February 27, 2002, to September 30, 2019. Inclusion criteria of patients aged 18 years and above, diagnosis of cirrhosis, liver transplant eligible, and listed in the UNOS database. Exclusion criteria included fulminant hepatic failure, prior history of liver transplantation, diagnosis of hepatocellular carcinoma, receipt of liver transplant in <180 days, or removal from waiting list <180 days for a reason other than death.

MEASUREMENT

Mortality by 180 days.

RESULTS

Of the 93,157 patients that met inclusion criteria, MELD-Na was calculated for all patients with sodium, total bilirubin, international normalized ratio, and creatinine available (N=79,611). The c -statistic with 95% confidence interval for MELD-Na for the predicted 6-month mortality was 0.83 (0.827-0.835). Mean MELD-Na of 28.2 was associated with ≤50% 6-month survival.

CONCLUSION

MELD-Na is an objective, quick measure that can aid providers in identifying patients with increased 6-month mortality in time-constrained settings, and a score of 28 can trigger the discussion for hospice as a means of improving value-based health care.

Anti-inflammatory Effects of Alcohol Are Associated with JNK-STAT3 Downregulation in an In Vitro Inflammation Model in HepG2 Cells

Background

In several preclinical and in vitro models of acute inflammation, alcohol (ethanol, EtOH) has been described as an immunomodulatory agent. Similarly, in different pathologies, clinical observations have confirmed either pro- or anti-inflammatory effects of EtOH. The liver plays an important role in immunity and alcohol metabolism; therefore, we analysed dose- and time-dependent effects of EtOH on the inflammatory response of human liver cells in an in vitro model of acute inflammation.

Methods

HepG2 cells were stimulated with IL-1β and subsequently exposed to EtOH in a low or high dose (85 mM, LoD or 170 mM, HiD) for 1 h (acute exposure) or 72 h (prolonged exposure). IL-6 and TNF-α release was determined by ELISA. Cell viability, adhesion of isolated neutrophils to HepG2 monolayers, their ICAM-1 expression, and the activation of stress-induced protein kinase/c-Jun N-terminal kinase (SAPK/JNK) or signal transducer and activator of transcription 3 (STAT3) were analysed.

Results

In this experimental design, EtOH did not markedly change the cell viability. Acute and prolonged exposure to EtOH significantly reduced dose-independent IL-1β-induced IL-6 and TNF-α release, as well as adhesion capacity to pretreated HepG2 cells. Acute exposure to EtOH significantly decreased the percentage of ICAM-1-expressing cells. IL-1β stimulation notably increased the activation of SAPK/JNK. However, low-dose EtOH exposure reduced this activation considerably, in contradiction to high-dose EtOH exposure. Acute exposure to LoD EtOH significantly diminished the IL-1β-induced STAT3 activation, whereas an acute exposure of cells to either HiD EtOH or in a prolonged setting showed no effects on STAT3 activation.

Conclusion

EtOH exerts anti-inflammatory potential in this in vitro model of hepatic inflammation. These effects are associated with the reduced activation of JNK/STAT3 by EtOH, particularly in the condition of acute exposure to low-dose EtOH.

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

Background

Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care.

Objective

We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed.

Methods

A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set.

Results

The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group.

Conclusions

Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.