Impact of Community Factors on the Donor Quality Score in Liver Transplantation

An increasing prevalence of metabolic syndrome and obesity has been linked to the rise in transplant indication for cryptogenic cirrhosis and nonalcoholic fatty liver disease (NAFLD), creating a growing challenge to public health. NAFLD liver transplant (LT) candidates are listed with low priority, and their waiting mortality is high. The impact of community/geographic factors on donor risk models is unknown. The purpose of this study was to develop a parsimonious donor risk-adjusted model tailored to NAFLD recipients by assessing the impact of donor, recipient, transplant, and external factors on graft survival. The theoretical framework was the social ecological model. Secondary data were collected from 3,165 consecutive recipients from the Scientific Registry of Transplant Recipients and Community Health Scores, a proxy of community health disparities derived from the Robert Wood Johnson Foundation\u27s community health rankings. Data were examined using univariate and multivariate analyses. The donor risk-adjusted model was developed using donor-only factors and supplemented with recipient and transplant factors, classifying donors as low, medium, and high risk. NAFLD residents in high-risk counties had increased likelihood of liver graft failure. Findings may be used to allocate high-risk donors to a subset of NAFLD with excellent outcomes, increasing the donor pool and decreasing mortality on the wait list

Artificial Intelligence and Liver Transplant:Predicting Survival of Individual Grafts

The demand for liver transplantation far outstrips the supply of deceased donor organs, and so, listing and allocation decisions aim to maximize utility. Most existing methods for predicting transplant outcomes use basic methods, such as regression modeling, but newer artificial intelligence (AI) techniques have the potential to improve predictive accuracy. The aim was to perform a systematic review of studies predicting graft outcomes following deceased donor liver transplantation using AI techniques and to compare these findings to linear regression and standard predictive modeling: donor risk index (DRI), Model for End‐Stage Liver Disease (MELD), and Survival Outcome Following Liver Transplantation (SOFT). After reviewing available article databases, a total of 52 articles were reviewed for inclusion. Of these articles, 9 met the inclusion criteria, which reported outcomes from 18,771 liver transplants. Artificial neural networks (ANNs) were the most commonly used methodology, being reported in 7 studies. Only 2 studies directly compared machine learning (ML) techniques to liver scoring modalities (i.e., DRI, SOFT, and balance of risk [BAR]). Both studies showed better prediction of individual organ survival with the optimal ANN model, reporting an area under the receiver operating characteristic curve (AUROC) 0.82 compared with BAR (0.62) and SOFT (0.57), and the other ANN model gave an AUC ROC of 0.84 compared with a DRI (0.68) and SOFT (0.64). AI techniques can provide high accuracy in predicting graft survival based on donors and recipient variables. When compared with the standard techniques, AI methods are dynamic and are able to be trained and validated within every population. However, the high accuracy of AI may come at a cost of losing explainability (to patients and clinicians) on how the technology works

Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching?

Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered “unbalanced.” In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D–R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution

Development of a Korean Liver Allocation System using Model for End Stage Liver Disease Scores: A Nationwide, Multicenter study

The previous Korean liver allocation system was based on Child-Turcotte-Pugh scores, but increasing numbers of deceased donors created a pressing need to develop an equitable, objective allocation system based on model for end-stage liver disease scores (MELD scores). A nationwide, multicenter, retrospective cohort study of candidates registered for livertransplantation from January 2009 to December 2011 was conducted at 11 transplant centers. Classification and regression tree (CART) analysis was used to stratify MELD score ranges according to waitlist survival. Of the 2702 patients that registered for liver transplantation, 2248 chronic liver disease patients were eligible. CART analysis indicated several MELD scores significantly predicted waitlist survival. The 90-day waitlist survival rates of patients with MELD scores of 31-40, 21-30, and ≤20 were 16.2%, 64.1%, and 95.9%, respectively (P  20, presence of HCC did not affect waitlist survival (P = 0.405). Considering the lack of donor organs and geographic disparities in Korea, we proposed the use of a national broader sharing of liver for the sickest patients (MELD ≥ 38) to reduce waitlist mortality. HCC patients with MELD ≤ 20 need additional MELD points to allow them equitable access to transplantation. Based on these results, the KoreanNetwork for Organ Sharing implemented the MELD allocation system in 2016.ope

Outcomes of liver re-transplantation and validation of predictive models of graft failure on single high-volume center

Outcomes of liver re-transplantation and validation of predictive models of graft failure on single high-volume cente

Extended-criteria donors in liver transplantation Part II: reviewing the impact of extended-criteria donors on the complications and outcomes of liver transplantation

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Development of an organ failure score in acute liver failure for transplant selection and identification of patients at high risk of futility.

INTRODUCTION: King's College Hospital criteria are currently used to select liver transplant candidates in acetaminophen-related acute liver failure (ALF). Although widely accepted, they show a poor sensitivity in predicting pre-transplant mortality and cannot predict the outcome after surgery. In this study we aimed to develop a new prognostic score that can allow patient selection for liver transplantation more appropriately and identify patients at high risk of futile transplantation. METHODS: We analysed consecutive patients admitted to the Royal Free and Beaujon Hospitals between 1990 and 2015. Clinical and laboratory data at admission were collected. Predictors of 3-month mortality in the non-transplanted patients admitted to the Royal Free Hospital were used to develop the new score, which was then validated against the Beaujon cohort. The Beaujon-transplanted group was also used to assess the ability of the new score in identifying patients at high risk of transplant futility. RESULTS: 152 patients were included of who 44 were transplanted. SOFA, CLIF-C OF and CLIF-ACLF scores were the best predictors of 3-month mortality among non-transplanted patients. CLIF-C OF score and high dosages of norepinephrine requirement were the only significant predictors of 3-month mortality in the non-transplanted patients, and therefore were included in the ALF-OFs score. In non-transplanted patients, ALF-OFs showed good performance in both exploratory (AUC = 0.89; sensitivity = 82.6%; specificity = 89.5%) and the validation cohort (AUC = 0.988; sensitivity = 100%; specificity = 92.3%). ALF-OFs score was also able to identify patients at high risk of transplant futility (AUC = 0.917; sensitivity = 100%; specificity = 79.2%). CONCLUSION: ALF-OFs is a new prognostic score in acetaminophen-related ALF that can predict both the need for liver transplant and high risk of transplant futility, improving candidate selection for liver transplantation