"Beyond MELD" - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation

Advancements in MELD Score and Its Impact on Hepatology

There continues to be an ongoing need for fair and equitable organ allocation. The Model for End-Stage Liver Disease (MELD) score has evolved as a calculated framework to evaluate and allocate patients for liver transplantation objectively. The original MELD score has undergone multiple modifications as it is continuously scrutinized for its accuracy in objectively representing the clinical context of patients with liver disease. Several refinements and iterations of the score have been developed, including the widely accepted MELD-Na score. In addition, the most recent updated iteration, MELD 3.0, has been created. The MELD 3.0 calculator incorporates new variables such as patient sex and serum albumin levels and assigns new weights for serum sodium, bilirubin, international normalized ratio, and creatinine levels. It is anticipated that the use of MELD 3.0 scores will reduce overall waitlist mortality and enhance access for female liver transplant candidates. However, despite the emergence of the MELD score as one of the most objective measures for fair organ allocation, various countries and healthcare systems employ alternative methods for stratification and organ allocation. This review article will highlight the origins of the MELD score, its iterations, the current MELD 3.0, and future directions for managing liver transplantation organ allocation. LAY SUMMARY: Organ donation is crucial for the management of patients unwell with liver disease, but organs must be allocated fairly and equitably. One method used for this is the Model for End-Stage Liver Disease (MELD) score, which helps objectively decide which patient is a candidate for liver transplant. Over time, the MELD score has been refined to better reflect patients' needs. For example, the latest version, MELD 3.0, now considers factors like nutrition and gender. This should ensure that more patients, especially females, are candidates and receive appropriate access to liver transplantation. However, not every country uses the MELD score. Some countries have created their own scoring systems based on local research. This review will explain where the MELD score came from, how it has changed, the current characteristics of the MELD 3.0 score, and what the future might hold for organ allocation in liver transplants.

Prognostic Modeling for Liver Cirrhosis Mortality Prediction and Real-Time Health Monitoring from Electronic Health Data

Liver cirrhosis stands as a prominent contributor to mortality, impacting millions across the United States. Enabling health care providers to predict early mortality among patients with cirrhosis holds the potential to enhance treatment efficacy significantly. Our hypothesis centers on the correlation between mortality and laboratory test results along with relevant diagnoses in this patient cohort. Additionally, we posit that a deep learning model could surpass the predictive capabilities of the existing Model for End-Stage Liver Disease score. This research seeks to advance prognostic accuracy and refine approaches to address the critical challenges posed by cirrhosis-related mortality. This study evaluates the performance of an artificial neural network model for liver disease classification using various training dataset sizes. Through meticulous experimentation, three distinct training proportions were analyzed: 70%, 80%, and 90%. The model's efficacy was assessed using precision, recall, F1-score, accuracy, and support metrics, alongside receiver operating characteristic (ROC) and precision-recall (PR) curves. The ROC curves were quantified using the area under the curve (AUC) metric. Results indicated that the model's performance improved with an increased size of the training dataset. Specifically, the 80% training data model achieved the highest AUC, suggesting superior classification ability over the models trained with 70% and 90% data. PR analysis revealed a steep trade-off between precision and recall across all datasets, with 80% training data again demonstrating a slightly better balance. This is indicative of the challenges faced in achieving high precision with a concurrently high recall, a common issue in imbalanced datasets such as those found in medical diagnostics.

Inconsistent values and algorithmic fairness: a review of organ allocation priority systems in the United States

BACKGROUND

The Organ Procurement and Transplant Network (OPTN) Final Rule guides national organ transplantation policies, mandating equitable organ allocation and organ-specific priority stratification systems. Current allocation scores rely on mortality predictions.

METHODS

We examined the alignment between the ethical priorities across organ prioritization systems and the statistical design of the risk models in question. We searched PubMed for literature on organ allocation history, policy, and ethics in the United States.

RESULTS

We identified 127 relevant articles, covering kidney (19), liver (60), lung (24), and heart transplants (23), and transplant accessibility (1). Current risk scores emphasize model performance and overlook ethical concerns in variable selection. The inclusion of race, sex, and geographical limits as categorical variables lacks biological basis; therefore, blurring the line between evidence-based models and discrimination. Comprehensive ethical and equity evaluation of risk scores is lacking, with only limited discussion of the algorithmic fairness of the Model for End-Stage Liver Disease (MELD) and the Kidney Donor Risk Index (KDRI) in some literature. We uncovered the inconsistent ethical standards underlying organ allocation scores in the United States. Specifically, we highlighted the exception points in MELD, the inclusion of race in KDRI, the geographical limit in the Lung Allocation Score, and the inadequacy of risk stratification in the Heart Tier system, creating obstacles for medically underserved populations.

CONCLUSIONS

We encourage efforts to address statistical and ethical concerns in organ allocation models and urge standardization and transparency in policy development to ensure fairness, equitability, and evidence-based risk predictions.

Artificial intelligence-based evaluation of prognosis in cirrhosis

Cirrhosis represents a significant global health challenge, characterized by high morbidity and mortality rates that severely impact human health. Timely and precise prognostic assessments of liver cirrhosis are crucial for improving patient outcomes and reducing mortality rates as they enable physicians to identify high-risk patients and implement early interventions. This paper features a thorough literature review on the prognostic assessment of liver cirrhosis, aiming to summarize and delineate the present status and constraints associated with the application of traditional prognostic tools in clinical settings. Among these tools, the Child-Pugh and Model for End-Stage Liver Disease (MELD) scoring systems are predominantly utilized. However, their accuracy varies significantly. These systems are generally suitable for broad assessments but lack condition-specific applicability and fail to capture the risks associated with dynamic changes in patient conditions. Future research in this field is poised for deep exploration into the integration of artificial intelligence (AI) with routine clinical and multi-omics data in patients with cirrhosis. The goal is to transition from static, unimodal assessment models to dynamic, multimodal frameworks. Such advancements will not only improve the precision of prognostic tools but also facilitate personalized medicine approaches, potentially revolutionizing clinical outcomes.

Common definitions and variables are needed for the United States to join the conversation on acute-on-chronic liver failure

Acute-on-chronic liver failure (ACLF) is a variably defined syndrome characterized by acute decompensation of cirrhosis with organ failures. At least 13 different definitions and diagnostic criteria for ACLF have been proposed, and there is increasing recognition that patients with ACLF may face disadvantages in the current United States liver allocation system. There is a need, therefore, for more standardized data collection and consensus to improve study design and outcome assessment in ACLF. In this article, we discuss the current landscape of transplantation for patients with ACLF, strategies to optimize organ utility, and data opportunities based on emerging technologies to facilitate improved data collection.

Sarcopenia evaluated by EASL/AASLD computed tomography-based criteria predicts mortality in patients with cirrhosis: A systematic review and meta-analysis

Background & Aims

Sarcopenia is associated with increased morbidity and mortality in patients with cirrhosis, but its definition in current literature is very heterogeneous. We performed a systematic review and meta-analysis to assess the association between mortality and sarcopenia evaluated by computed tomography (CT) in patients with cirrhosis, both overall and stratified for the criteria used to define sarcopenia.

Methods

Medline, Embase, Scopus, and Cochrane Library were searched up to January 2023. We included studies assessing sarcopenia presence with CT scans and providing data on the risk of mortality. Adjusted hazard ratios (HRs) and 95% CIs were pooled using a random-effects model.

Results

Thirty-nine studies comprising 12,827 patients were included in the meta-analysis. The summary prevalence of sarcopenia was 44% (95% CI 38-50%). The presence of sarcopenia (any definition) was an independent predictor of mortality with an adjusted HR of 2.07 (95% CI 1.81-2.36), and the result was consistent in all subgroup analyses. The prognostic role of the EASL/AASLD criteria was confirmed for the first time with an HR of 1.86 (95% CI 1.53-2.26) (n = 14 studies). The cut-offs used to define sarcopenia based on psoas muscle parameters varied among studies, thus, a subgroup analysis was not feasible. There was no substantial heterogeneity for the main estimates and no significant risk of publication bias.

Conclusions

Sarcopenia on CT is associated with a 2-fold higher risk of mortality in patients with cirrhosis. The cut-offs proposed by EASL/AASLD are prognostically relevant and should be the recommended criteria used to define sarcopenia in clinical practice.

Impact and implications

Sarcopenia assessed by the reference standard (computed tomography scan) is an independent predictor of mortality in patients with cirrhosis, with a 2-fold increase in the risk of death in all sensitivity analyses. This finding is particularly valid in patients from Europe and North America, and in transplant candidates. Stratifying for the parameters and cut-offs used, we confirmed for the first time the prognostic impact of the definition proposed by EASL/AASLD, supporting their use in clinical practice. Psoas muscle assessment is promising, but data are still limited and too heterogeneous to recommend its routine use at present.

GEMA-Na and MELD 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study

Background

The Gender-Equity Model for liver Allocation corrected by serum sodium (GEMA-Na) and the Model for End-stage Liver Disease 3.0 (MELD 3.0) could amend sex disparities for accessing liver transplantation (LT). We aimed to assess these inequities in Spain and to compare the performance of GEMA-Na and MELD 3.0.

Methods

Nationwide cohort study including adult patients listed for a first elective LT (January 2016-December 2021). The primary outcome was mortality or delisting for sickness within the first 90 days. Independent predictors of the primary outcome were evaluated using multivariate Cox's regression with adjusted relative risks (RR) and 95% confidence intervals (95% CI). The discrimination of GEMA-Na and MELD 3.0was assessed using Harrell c-statistics (Hc).

Findings

The study included 6071 patients (4697 men and 1374 women). Mortality or delisting for clinical deterioration occurred in 286 patients at 90 days (4.7%). Women had reduced access to LT (83.7% vs. 85.9%; p = 0.037) and increased risk of mortality or delisting for sickness at 90 days (adjusted RR = 1.57 [95% CI 1.09-2.28]; p = 0.017). Female sex remained as an independent risk factor when using MELD or MELD-Na but lost its significance in the presence of GEMA-Na or MELD 3.0. Among patients included for reasons other than tumours (n = 3606; 59.4%), GEMA-Na had Hc = 0.753 (95% CI 0.715-0.792), which was higher than MELD 3.0 (Hc = 0.726 [95% CI 0.686-0.767; p = 0.001), showing both models adequate calibration.

Interpretation

GEMA-Na and MELD 3.0 might correct sex disparities for accessing LT, but GEMA-Na provides more accurate predictions of waiting list outcomes and could be considered the standard of care for waiting list prioritization.

Funding

Instituto de Salud Carlos III, Agencia Estatal de Investigación (Spain), and European Union.

Serious Illness Communication in Cirrhosis Care: Tools to Improve Illness Understanding, Prognostic Understanding, and Care Planning

Patients with cirrhosis frequently experience an unpredictable illness trajectory, with frequent hospitalizations and complications. Along with the uncertain nature of the disease, the possibility of a lifesaving and curative transplant often makes prognostic discussions and future care decisions challenging. Serious illness communication (SIC) refers to supportive communication whereby clinicians assess patients' illness understanding, share prognostic information according to patients' preferences, explore patients' goals, and make recommendations for care that align with these goals. SIC includes 3 key components: (1) illness understanding; (2) prognostic understanding; and (3) care planning. In this piece, we explore current barriers to early implementation of SIC in cirrhosis care and share possible solutions, including adopting a multidisciplinary approach, delivering culturally competent care, and training clinicians in SIC core skills. By use of a case example, we aim to demonstrate SIC in action and to provide clinicians with tools and skills that can be used in practice.

Prognosis algorithms for acute decompensation of cirrhosis and ACLF

Accurate prediction of survival in patients with cirrhosis is crucial, as patients who are unlikely to survive in the short-term need to be oriented to liver transplantation and to novel therapeutic approaches. Patients with acute decompensation of cirrhosis without or with organ dysfunction/failure, the so-called acute-on-chronic liver failure (ACLF), have a particularly high short-term mortality. Recognizing the specificity of this clinical situation, dedicated classifications and scores have been developed over the last 15 years, including variables (e.g. organ failures and systemic inflammation) not part of the formerly available cirrhosis severity scores, namely Child-Pugh score or MELD. For patients with acute decompensation of cirrhosis, it led to the development of a dedicated score, the Clif-C-AD score, independently validated. For more severe patients, three different scoring systems have been proposed, by European, Asian and North American societies namely Clif-C-ACLF, AARC score and NASCELD-ACLF respectively. These scores have been validated, and are widely used across the world. The differences and similarities between these scores, as well as their validation and limitations are discussed here. Even if these scores and classifications have been a step forward in favouring homogeneity between studies, and in helping making decisions for individual patients, their predictive value for mortality can still be improved as their area under the ROC curve does not exceed .8. Novel scores including biomarkers reflecting the pathophysiology of acute decompensation of cirrhosis might help reach that goal.

Gd-EOB-DTPA enhanced MRI based radiomics combined with clinical variables in stratifying hepatic functional reserve in HBV infected patients

PURPOSES

To evaluate radiomics from Gd-EOB-DTPA enhanced MR combined with clinical variables for stratifying hepatic functional reserve in hepatitis B virus (HBV) patients.

METHODS

Our study included 279 chronic HBV patients divided 8:2 for training and test cohorts. Radiomics features were extracted from the hepatobiliary phase (HBP) MR images. Radiomics features were selected to construct a Rad-score which was combined with clinical parameters in two models differentiating hepatitis vs. Child-Pugh A and Child-Pugh A vs. B/C. Performances of these stratifying models were compared using area under curve (AUC).

RESULTS

Rad-score alone discriminated hepatitis vs. Child-Pugh A with AUC = 0.890, 0.914 and Child-Pugh A vs. B/C with AUC = 0.862, 0.865 for the training and test cohorts, respectively. Model 1 [Rad-score + clinical parameters for hepatitis vs. Child-Pugh A] showed AUC = 0.978 for the test cohort, which was higher than ALBI [albumin-bilirubin] and MELD [model for end-stage liver disease], with AUCs of 0.716, 0.799, respectively (p < 0.001, < 0.001). Model 2 [Rad-score + clinical parameters for Child-Pugh A vs. B/C] showed AUC of 0.890 in the test cohort, which was similar to ALBI (AUC = 0.908, p = 0.760), and higher than MELD (AUC = 0.709, p = 0.018).

CONCLUSION

Rad-score combined with clinical variables stratifies hepatic functional reserve in HBV patients.

A model including standardized weight improved predicting waiting list mortality in adolescent liver transplant candidates: A US national study

The Model for End-Stage Liver Disease (MELD) score has been employed to identify adolescents eligible for liver transplantation since 2004. However, the optimal model for prioritizing adolescent candidates is uncertain. In our study, we aimed at evaluating the value of adding anthropometric variables to liver transplantation allocation models among adolescents. We conducted a retrospective cohort study using the data from the Organ Procurement and Transplantation Network Standard Transplant Analysis and Research to identify adolescent patients registered on the liver transplant waiting list in the United States between January 1, 2003, and December 31, 2022. Adolescents (12-17 y) who were listed for their first liver transplantation were included. We evaluated the performance of different models including pediatric end-stage liver disease with Na and creatinine, MELD, and MELD 3.0. Furthermore, we evaluated whether adding anthropometric variables ( z -score for weight and height) would improve the models' performance for our primary outcome (mortality at 90 days after listing). We identified 1421 eligible adolescent patients. Adding a z -score of weight (MELD-TEEN) improved the performance and discrimination of the MELD score. The final model including weight z -score (MELD-TEEN) had better discriminative power compared to MELD 3.0 and pediatric end-stage liver disease with Na and creatinine in the overall cohort and in different age groups (ages 12-14 and 15-17). MELD-TEEN could improve the accuracy of allocation of liver transplants among adolescents by incorporating the weight z -score compared to MELD 3.0 and pediatric end-stage liver disease with Na and creatinine.

Prevalence, Characteristics, and Outcomes of Infective Endocarditis Readmissions in Patients With Variables Associated With Liver Disease in the United States

Infective endocarditis (IE) is common in patients with liver disease. Outcomes of IE in patients with liver disease are limited. We aimed to investigate IE outcomes in patients with variables associated with liver disease in the USA. We used the 2017 National Readmission Database to identify index admission of adults with IE, based on the International Classification of Disease, 10th revision codes. The primary outcome was 30-day readmission. Secondary outcomes were mortality and predictors of hospital readmission. We identified 40,413 IE admissions. Patients who were readmitted were more likely to have a history of HCV (19.4 vs 12.3%, P < .001), hyponatremia (25 vs 21%, P < .001), and thrombocytopenia (20.3 vs 16.3%, P < .001). After adjusting for age, hypertension, heart failure, diabetes mellitus, and end stage renal disease, hyponatremia (odds ratio (OR) 1.25; 95% confidence intervals [CI]: 1.17-1.35; P < .001) and thrombocytopenia (OR 1.16; 95% CI: 1.08-1.24; P < .001) correlated with higher odds of 30-day readmission. Mortality was higher among patients with hyponatremia (29 vs 22%, P < .001), thrombocytopenia (29 vs 17%, P < .001), coagulopathy (12 vs 5%, P < .001), cirrhosis (6 vs 4%, P < .001), ascites (7 vs 3%, P < .001), liver failure (18 vs 3%, P < .001), and portal hypertension (3 vs 1.5%, P < .001).

Portal hypertension in common variable immunodeficiency disorders - a single center analysis on clinical and immunological parameter in 196 patients

Background

Liver manifestations and in particular portal hypertension (PH) contribute significantly to morbidity and mortality of patients with common variable immunodeficiency disorders (CVID). Screening strategies and early detection are limited due to the lack of specific diagnostic tools.

Methods

We evaluated clinical, immunological, histological, and imaging parameters in CVID patients with clinical manifestation of portal hypertension (CVID+PH).

Results

Portal hypertension was present in 5.6% of CVID patients and was associated with high clinical burden and increased mortality (18%). Longitudinal data on clinical and immunological parameters in patients before and during clinically manifest portal hypertension revealed a growing splenomegaly and increasing gamma-glutamyl transferase (GGT) and soluble interleukin 2 receptor (SIL-2R) levels with decreasing platelets over time. While ultrasound of the liver failed to detect signs of portal hypertension in most affected patients, transient elastography was elevated in all patients. All CVID+PH patients had reduced naïve CD45RA+CD4+ T-cells (mean of 6,2%). The frequency of severe B-lymphocytopenia (Euroclass B-) was higher in CVID+PH patients. The main histological findings included lymphocytic infiltration, nodular regenerative hyperplasia-like changes (NRH-LC), and porto(-septal) fibrosis.

Conclusion

CVID patients with lower naïve CD45RA+CD4+ T-cells or severely reduced B-cells might be at higher risk for portal hypertension. The combination of biochemical (increasing sIL-2R, GGT, and decreasing platelets) and imaging parameters (increasing splenomegaly) should raise suspicion of the beginning of portal hypertension.

Prognostic models in end stage liver disease

Cirrhosis is a major cause of death worldwide, and is associated with significant health care costs. Even if milestones have been recently reached in understanding and managing end-stage liver disease (ESLD), the disease course remains somewhat difficult to prognosticate. These difficulties have already been acknowledged already in the past, when scores instead of single parameters have been proposed as valuable tools for short-term prognosis. These standard scores, like Child Turcotte Pugh (CTP) and model for end-stage liver disease (MELD) score, relying on biochemical and clinical parameters, are still widely used in clinical practice to predict short- and medium-term prognosis. The MELD score, which remains an accurate, easy-to-use, objective predictive score, has received significant modifications over time, in order to improve its performance especially in the liver transplant (LT) setting, where it is widely used as prioritization tool. Although many attempts to improve prognostic accuracy have failed because of lack of replicability or poor benefit with the comparator (often the MELD score or its variants), few scores have been recently proposed and validated especially for subgroups of patients with ESLD, as those with acute-on-chronic liver failure. Artificial intelligence will probably help hepatologists in the near future to fill the current gaps in predicting disease course and long-term prognosis of such patients.

The Application of Artificial Intelligence (AI)-Based Ultrasound for the Diagnosis of Fatty Liver Disease: A Systematic Review

Fatty liver disease, also known as hepatic steatosis, poses a significant global health concern due to the excessive accumulation of fat within the liver. If left untreated, this condition can give rise to severe complications. Recent advances in artificial intelligence (AI, specifically AI-based ultrasound imaging) offer promising tools for diagnosing this condition. This review endeavors to explore the current state of research concerning AI's role in diagnosing fatty liver disease, with a particular emphasis on imaging methods. To this end, a comprehensive search was conducted across electronic databases, including Google Scholar and Embase, to identify relevant studies published between January 2010 and May 2023, with keywords such as "fatty liver disease" and "artificial intelligence (AI)." The article selection process adhered to the PRISMA framework, ultimately resulting in the inclusion of 13 studies. These studies leveraged AI-assisted ultrasound due to its accessibility and cost-effectiveness, and they hailed from diverse countries, including India, China, Singapore, the United States, Egypt, Iran, Poland, Malaysia, and Korea. These studies employed a variety of AI classifiers, such as support vector machines, convolutional neural networks, multilayer perceptron, fuzzy Sugeno, and probabilistic neural networks, all of which demonstrated a remarkable level of precision. Notably, one study even achieved a diagnostic accuracy rate of 100%, underscoring AI's potential in diagnosing fatty liver disease. Nevertheless, the review acknowledged certain limitations within the included studies, with the majority featuring relatively small sample sizes, often encompassing fewer than 100 patients. Additionally, the variability in AI algorithms and imaging techniques added complexity to the comparative analysis. In conclusion, this review emphasizes the potential of AI in enhancing the diagnosis and management of fatty liver disease through advanced imaging techniques. Future research endeavors should prioritize the execution of large-scale studies that employ standardized AI algorithms and imaging techniques to validate AI's utility in diagnosing this prevalent health condition.

Evaluation of the Easy Albumin-Bilirubin Score as a Prognostic Tool for Mortality in Adult Trauma Patients in the Intensive Care Unit: A Retrospective Study

The easy albumin-bilirubin (EZ-ALBI) score is derived using the following equation: total bilirubin (mg/dL) - 9 × albumin (g/dL). This study aimed to determine whether the EZ-ALBI score predicted mortality risk in adult trauma patients in an intensive care unit (ICU). Data from a hospital's trauma database were retrospectively evaluated for 1083 adult trauma ICU patients (139 deaths and 944 survivors) between 1 January 2016 and 31 December 2021. Patients were classified based on the ideal EZ-ALBI cut-off of -26.5, which was determined via receiver operating characteristic curve analysis. The deceased patients' EZ-ALBI scores were higher than those of the surviving patients (-26.8 ± 6.5 vs. -30.3 ± 5.9, p = 0.001). Multivariate logistic analysis revealed that, in addition to age, the presence of end-stage renal disease, Glasgow Coma Scale scores, and injury severity scores, the EZ-ALBI score is an independent risk factor for mortality (odds ratio (OR), 1.10; 95% confidence interval (CI): 1.06-1.14; p = 0.001)). Compared with patients with EZ-ALBI scores < -26.5, those with scores ≥ -26.5 had a 2.1-fold higher adjusted mortality rate (adjusted OR, 2.14; 95% CI: 1.43-3.19, p = 0.001). In conclusion, the EZ-ALBI score is a substantial and independent predictor of mortality and can be screened to stratify mortality risk in adult trauma ICU patients.

The Effect of Model for End-Stage Liver Disease 3.0 on Disparities between Patients with and without Hepatocellular Carcinoma in Korea

PURPOSE

The model for end-stage liver disease (MELD) 3.0 has recently been suggested for determining liver allocation. We aimed to apply MELD 3.0 to a Korean population and to discover differences between patients with and without hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This study is a retrospective study of 2203 patients diagnosed with liver cirrhosis at Severance Hospital between 2016-2022. Harrell's concordance index was used to validate the ability of MELD scores to predict 90-day survival.

RESULTS

During a mean follow-up of 12.9 months, 90-day survival was 61.9% in all patients, 50.4% in the HCC patients, and 74.8% in the non-HCC patients. Within the HCC patients, the concordance index for patients on the waitlist was 0.653 using MELD, which increased to 0.753 using MELD 3.0. Among waitlisted patients, the 90-day survival of HCC patients was worse than that of non-HCC patients with MELD scores of 31-37 only (69.7% vs. 30.0%, p=0.001). Applying MELD 3.0, the 90-day survival of HCC patients was worse than that of non-HCC patients across a wider range of MELD 3.0 scores, compared to MELD, with MELD 3.0 scores of 21-30 and 31-37 (82.0% vs. 72.5% and 72.3% vs. 24.3%, p=0.02 and p<0.001, respectively).

CONCLUSION

MELD 3.0 predicted 90-day survival of the HCC patients more accurately than original MELD score; however, the disparity between HCC and non-HCC patients increased, particularly in patients with MELD scores of 21-30. Therefore, a novel exception score is needed or the current exception score system should be modified.

Biliary atresia and liver transplantation in the United States: A contemporary analysis

BACKGROUND

Biliary atresia (BA) remains the number one indication for paediatric liver transplantation (LT) worldwide but is an uncommon indication for older LT recipients. The impact of recent donor allocation changes, pervasive organ shortage and evolving LT practices on the BA LT population is unknown.

METHODS

We identified patients who underwent LT between January 2010 and December 2021 using the UNOS database. We compared clinical outcomes between patients with BA and those with non-BA cholestatic liver disease. Groups were stratified by age, <12 years (allocated via PELD system) and ≥12 years (allocated via MELD system). Waitlist outcomes were compared using competing-risk regression analysis, graft survival rates were compared using Kaplan-Meier time-to-event analysis and Cox proportional hazards modelling provided adjusted estimates.

RESULTS

There were 2754 BA LT waitlist additions and 2206 BA LTs (1937 <12 years [younger], 269 ≥12 years [older]). There were no differences in waitlist mortality between BA and non-BA cholestatic patients. Among BA LT recipients, there were 441 (20.0%) living-donor liver transplantations (LDLT) and 611 (27.7%) split deceased-donor LTs. Five-year graft survival was significantly higher among BA versus non-BA cholestatic patients in the older group (88.3% vs. 79.5%, p < .01) but not younger group (89.3% vs. 89.5%). Among BA LT recipients, improved graft outcomes were associated with LDLT (vs. split LT: HR: 2, 95% CI: 1.03-3.91) and higher transplant volume (volume >100 vs. <40 BA LTs: HR: 3.41, 95% CI: 1.87-6.2).

CONCLUSION

Liver transplant outcomes among BA patients are excellent, with LDLT and higher transplant centre volume associated with optimal graft outcomes.

The novel SALT-M score predicts 1-year post-transplant mortality in patients with severe acute-on-chronic liver failure

BACKGROUND & AIMS

Twenty-eight-day mortality ranges from 30-90% in patients with acute-on-chronic liver failure grades 2/3 (severe ACLF). Though liver transplantation (LT) has demonstrated a survival benefit, the scarcity of donor organs and uncertainty regarding post-LT mortality among patients with severe ACLF may cause hesitancy. We developed and externally validated a model to predict 1-year post-LT mortality in severe ACLF, called the Sundaram ACLF-LT-Mortality (SALT-M) score, and estimated the median length of stay (LoS) after LT (ACLF-LT-LoS).

METHODS

In 15 LT centers in the US, we retrospectively identified a cohort of patients with severe ACLF transplanted between 2014-2019, followed up to Jan'2022. Candidate predictors included demographics, clinical and laboratory values, and organ failures. We selected predictors in the final model using clinical criteria and externally validated them in two French cohorts. We provided measures of overall performance, discrimination, and calibration. We used multivariable median regression to estimate LoS after adjusting for clinically relevant factors.

RESULTS

We included 735 patients, of whom 521 (70.8%) had severe ACLF (120 ACLF-3, external cohort). The median age was 55 years, and 104 with severe ACLF (19.9%) died within 1-year post-LT. Our final model included age >50 years, use of 1/≥2 inotropes, presence of respiratory failure, diabetes mellitus, and BMI (continuous). The c-statistic was 0.72 (derivation) and 0.80 (validation), indicating adequate discrimination and calibration based on the observed/expected probability plots. Age, respiratory failure, BMI, and presence of infection independently predicted median LoS.

CONCLUSIONS

The SALT-M score predicts mortality within 1-year after LT in patients with ACLF. The ACLF-LT-LoS score predicted median post-LT stay. Future studies using these scores could assist in determining transplant benefits.

IMPACT AND IMPLICATIONS

Liver transplantation (LT) may be the only life-saving procedure available to patients with acute-on-chronic liver failure (ACLF), but clinically instability can augment the perceived risk of post-transplant mortality at 1 year. We developed a parsimonious score with clinically and readily available parameters to objectively assess 1-year post-LT survival and predict median length of stay after LT. We developed and externally validated a clinical model called the Sundaram ACLF-LT-Mortality score in 521 US patients with ACLF with 2 or ≥3 organ failure(s) and 120 French patients with ACLF grade 3. The c-statistic was 0.72 in the development cohort and 0.80 in the validation cohort. We also provided an estimation of the median length of stay after LT in these patients. Our models can be used in discussions on the risks/benefits of LT in patients listed with severe ACLF. Nevertheless, the score is far from perfect and other factors, such as patient's preference and center-specific factors, need to be considered when using these tools.

Clinical decision support and electronic interventions to improve care quality in chronic liver diseases and cirrhosis

Significant quality gaps exist in the management of chronic liver diseases and cirrhosis. Clinical decision support systems-information-driven tools based in and launched from the electronic health record-are attractive and potentially scalable prospective interventions that could help standardize clinical care in hepatology. Yet, clinical decision support systems have had a mixed record in clinical medicine due to issues with interoperability and compatibility with clinical workflows. In this review, we discuss the conceptual origins of clinical decision support systems, existing applications in liver diseases, issues and challenges with implementation, and emerging strategies to improve their integration in hepatology care.

Cellular therapies in liver and pancreatic diseases

Over the past two decades, developments in regenerative medicine in gastroenterology have been greatly enhanced by the application of stem cells, which can self-replicate and differentiate into any somatic cell. The discovery of induced pluripotent stem cells has opened remarkable perspectives on tissue regeneration, including their use as a bridge to transplantation or as supportive therapy in patients with organ failure. The improvements in DNA manipulation and gene editing strategies have also allowed to clarify the physiopathology and to correct the phenotype of several monogenic diseases, both in vivo and in vitro. Further progress has been made with the development of three-dimensional cultures, known as organoids, which have demonstrated morphological and functional complexity comparable to that of a miniature organ. Hence, owing to its protean applications and potential benefits, cell and organoid transplantation has become a hot topic for the management of gastrointestinal diseases. In this review, we describe current knowledge on cell therapies in hepatology and pancreatology, providing insight into their future applications in regenerative medicine.

Mortality scoring systems for liver transplant recipients: before and after model for end-stage liver disease score

The mortality scoring systems for patients with end-stage liver disease have evolved from the Child-Turcotte-Pugh score to the model for end-stage liver disease (MELD) score, affecting the wait list for liver allocation. There are inherent weaknesses in the MELD score, with the gradual decline in its accuracy owing to changes in patient demographics or treatment options. Continuous refinement of the MELD score is in progress; however, both advantages and disadvantages exist. Recently, attempts have been made to introduce artificial intelligence into mortality prediction; however, many challenges must still be overcome. More research is needed to improve the accuracy of mortality prediction in liver transplant recipients.

The potential role of machine learning in modelling advanced chronic liver disease

The use of artificial intelligence is rapidly increasing in medicine to support clinical decision making mostly through diagnostic and prediction models. Such models derive from huge databases (big data) including a large variety of health-related individual patient data (input) and the corresponding diagnosis and/or outcome (labels). Various types of algorithms (e.g. neural networks) based on powerful computational ability (machine), allow to detect the relationship between input and labels (learning). More complex algorithms, like recurrent neural network can learn from previous as well as actual input (deep learning) and are used for more complex tasks like imaging analysis and personalized (bespoke) medicine. The prompt availability of big data makes that artificial intelligence can provide rapid answers to questions that would require years of traditional clinical research. It may therefore be a key tool to overcome several major gaps in the model of advanced chronic liver disease, mostly transition from mild to clinically significant portal hypertension, the impact of acute decompensation and the role of further decompensation and treatment efficiency. However, several limitations of artificial intelligence should be overcome before its application in clinical practice. Assessment of the risk of bias, understandability of the black boxes developing the models and models' validation are the most important areas deserving clarification for artificial intelligence to be widely accepted from physicians and patients.

Awarding additional MELD points to the shortest waitlist candidates improves sex disparity in access to liver transplant in the United States

Since the introduction of the MELD-based allocation system, women are now 30% less likely than men to undergo liver transplant (LT) and have 20% higher waitlist mortality. These disparities are in large part due to height differences in men and women though no national policies have been implemented to reduce sex disparities. Patients were identified using the Scientific Registry of Transplant Recipients (SRTR) from 2014 to 2019. Patients were categorized into five groups by first dividing into thirds by height then dividing the shortest third into three groups to capture more granular differences in the most disadvantaged patients (<166 cm). We then used LSAM to model waitlist outcomes in five versions of awarding additional MELD points to shorter candidates compared to current policy. We identified two proposed policy changes LSAM scenarios that resulted in improvement in LT and death percentage for the shortest candidates with the least negative impact on taller candidates. In conclusion, awarding an additional 1-2 MELD points to the shortest 8% of LT candidates would improve waitlist outcomes for women. This strategy should be considered in national policy allocation to address sex-based disparities in LT.

Early prediction model for prognosis of patients with hepatitis-B-virus-related acute-on-chronic liver failure received glucocorticoid therapy

Background

Early prediction for short-term prognosis is essential for the management of hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF). In this study, we aim to establish a noninvasive model for predicting the 90-day mortality in patients with HBV–ACLF received glucocorticoid therapy.

Methods

Two hundred and eighty patients with HBV–ACLF were enrolled from July 2010 to June 2022. All patients received routine medicine treatment and 204 of them received additional glucocorticoid treatment. Then, the patients who received glucocorticoid treatment were randomly divided into a training cohort and a validation cohort. An early prediction model for 90-day mortality of HBV–ACLF was established in the training cohort and then validated in the validation cohort.

Results

HBV–ACLF patients received glucocorticoid treatment showed significantly better survival that those not (P < 0.01). In the training cohort, a noninvasive model was generated with hepatic encephalopathy grade, INR, total bilirubin, age and SIRS status, which was named HITAS score. It showed significantly better predictive value for 90-day mortality of HBV–ACLF than MELD score and Child–Turcotte–Pugh score in both the training cohort and validation cohort. Using the Kaplan–Meier analysis with cutoff points of 2.5 and 3.47, the HITAS score can classify HBV–ACLF patients into different groups with low, intermediate and high risk of death after glucocorticoid therapy.

Conclusions

We proposed a HITAS score, which was an early prediction model for the prognosis of HBV–ACLF. It might be used to identify HBV–ACLF patients with favorable responses to glucocorticoid treatment.