The postoperative Model for End stage Liver Disease score as a predictor of short-term outcome after transplantation of extended criteria donor livers

Advancements in Predictive Tools for Primary Graft Dysfunction in Liver Transplantation: A Comprehensive Review

Orthotopic liver transplantation stands as the sole curative solution for end-stage liver disease. Nevertheless, the discrepancy between the demand and supply of grafts in transplant medicine greatly limits the success of this treatment. The increasing global shortage of organs necessitates the utilization of extended criteria donors (ECD) for liver transplantation, thereby increasing the risk of primary graft dysfunction (PGD). Primary graft dysfunction (PGD) encompasses early allograft dysfunction (EAD) and the more severe primary nonfunction (PNF), both of which stem from ischemia-reperfusion injury (IRI) and mitochondrial damage. Currently, the only effective treatment for PNF is secondary transplantation within the initial post-transplant week, and the occurrence of EAD suggests an elevated, albeit still uncertain, likelihood of retransplantation urgency. Nonetheless, the ongoing exploration of novel IRI mitigation strategies offers hope for future improvements in PGD outcomes. Establishing an intuitive and reliable tool to predict upcoming graft dysfunction is vital for early identification of high-risk patients and for making informed retransplantation decisions. Accurate diagnostics for PNF and EAD constitute essential initial steps in implementing future mitigation strategies. Recently, novel methods for PNF prediction have been developed, and several models for EAD assessments have been introduced. Here, we provide an overview of the currently scrutinized predictive tools for PNF and EAD evaluation strategies, accompanied by recommendations for future studies.

Prognostic role of the Donor Risk Index, the Eurotransplant Donor Risk Index, and the Balance of Risk score on graft loss after liver transplantation

This study aimed to identify cutoff values for donor risk index (DRI), Eurotransplant (ET)-DRI, and balance of risk (BAR) scores that predict the risk of liver graft loss. MEDLINE and Web of Science databases were searched systematically and unrestrictedly. Graft loss odds ratios and 95% confidence intervals were assessed by meta-analyses using Mantel-Haenszel tests with a random-effects model. Cutoff values for predicting graft loss at 3 months, 1 year, and 3 years were analyzed for each of the scores. Measures of calibration and discrimination used in studies validating the DRI and the ET-DRI were summarized. DRI ≥ 1.4 (six studies, n = 35 580 patients) and ET-DRI ≥ 1.4 (four studies, n = 11 666 patients) were associated with the highest risk of graft loss at all time points. BAR > 18 was associated with the highest risk of 3-month and 1-year graft loss (n = 6499 patients). A DRI cutoff of 1.8 and an ET-DRI cutoff of 1.7 were estimated using a summary receiver operator characteristic curve, but the sensitivity and specificity of these cutoff values were low. A DRI and ET-DRI score ≥ 1.4 and a BAR score > 18 have a negative influence on graft survival, but these cutoff values are not well suited for predicting graft loss.

Roles of BATF/JUN/IRF4 complex in tacrolimus mediated immunosuppression on Tfh cells in acute rejection after liver transplantation

Rejection injury is a serious complication after liver transplantation (LTx). Tacrolimus (Tac) is a key immunosuppressive agent in the prevention of liver rejection after transplantation. The basic leucine zipper ATF-like transcription factor (BATF)/JUN/interferon regulatory factor 4 (IRF4) complex serves critical functions in the immune response. This study aimed to explore the role of the BATF/JUN/IRF4 complex in rejection after LTx by treatment with Tac. Herein, DA and Lewis (LEW) rats were used to construct the LTx animal model. The recipient LEW rats were treated with Tac or saline, subcutaneously. Splenic mononuclear cells were treated with Tac at 1 and 10 nM after stimulation with interleukin-6 (IL-6), and the expression of factors associated with the nuclear factor of activated T cells (NFAT)-BATF/JUN/IRF4 and IL-21 were detected. The results demonstrated that Tac prolonged the allografts survival and attenuated inflammation injury, and decreased the percentage frequencies of T follicular helper (Tfh) cells in peripheral blood mononuclear cells and inhibited B-cell lymphoma 6 (Bcl-6) and IL-6 expression in Tfh cells. In addition, Tac inhibited the expression of the BATF/JUN/IRF4 complex, Bcl-6 and IL-21 NFATc1 and NFATc2 were inhibited by Tac, and interacted with the promoter of BATF and IRF4. In conclusion, the attenuation of rejection injury may be dependent on the NFAT-BATF/JUN/IRF4-IL-21 axis, and the BATF/JUN/IRF4 complex participates in the inhibition of IL-21-producing Tfh cells after treatment with Tac. These findings suggest that the BATF/JUN/IRF4 complex-IL-21 axis may be used as a potential target for attenuating rejection injury after LTx.

Predicting Short-term Survival after Liver Transplantation using Machine Learning

Liver transplantation is one of the most effective treatments for end-stage liver disease, but the demand for livers is much higher than the available donor livers. Model for End-stage Liver Disease (MELD) score is a commonly used approach to prioritize patients, but previous studies have indicated that MELD score may fail to predict well for the postoperative patients. This work proposes to use data-driven approach to devise a predictive model to predict postoperative survival within 30 days based on patient’s preoperative physiological measurement values. We use random forest (RF) to select important features, including clinically used features and new features discovered from physiological measurement values. Moreover, we propose a new imputation method to deal with the problem of missing values and the results show that it outperforms the other alternatives. In the predictive model, we use patients’ blood test data within 1–9 days before surgery to construct the model to predict postoperative patients’ survival. The experimental results on a real data set indicate that RF outperforms the other alternatives. The experimental results on the temporal validation set show that our proposed model achieves area under the curve (AUC) of 0.771 and specificity of 0.815, showing superior discrimination power in predicting postoperative survival.

Liver transplantation for hepatocellular carcinoma with live donors or extended criteria donors: a propensity score-matched comparison

Background

To compare patient survival after liver transplantation (LT) for hepatocellular carcinoma (HCC) from live donors (LD) or extended criteria donors (ECD).

Methods

Data from consecutive LT procedures for HCC involving either LD or ECD were reviewed. Patient survival was our primary outcome. Re-transplantation (Re-LT), ischemic type bile lesions (ITBL), and tumor recurrence represented secondary outcomes. The primary outcome was statistically analyzed using Kaplan-Meier estimates and Cox proportional hazards regression; logistic regression analyses were used for statistical analysis of the secondary outcomes. Propensity score was calculated based on patient age, sex, hepatitis C viral infection (HCV), laboratory model for end-stage liver disease (labMELD) score, bridging treatment, Milan criteria, α-fetoprotein levels, and tumor grade.

Results

The study evaluated 109 recipients undergoing LT from either LD (n=57) or ECD (n=52). LT procedure (hazard ratio [HR] 2.349, 95% confidence interval [CI] 1.151-4.794, P=0.0190), age (HR 1.075, 95%CI 1.020-1.133, P=0.0074) and labMELD score (HR 1.082, 95%CI 1.021-1.147, P=0.0075) reached significance by Cox proportional hazards regression. After adjustment with the propensity score (stratification with 5 strata), the LT procedure was still significant (HR 2.401, 95%CI 1.114-5.175, P=0.0253). Tumor grade (odds ratio [OR] 9.628, 95%CI 1.120-82.752, P=0.0391), labMELD score (OR 1.224, 95%CI 1.019-1.471, P=0.0306), and Milan criteria (OR 6.375, 95%CI 1.239-32.796, P=0.0267) gained statistical significance by logistic regression analysis for Re-LT, ITBL, and tumor recurrence, respectively.

Conclusions

LT for HCC showed superior patient survival with ECD rather than LD grafts. Re-LT, ITBL, and tumor recurrence showed no significant differences between the two groups. However, the diverging criteria for the definition of ECD grafts represent a considerable limitation for the wide application of this policy.

Association and interaction between model for end-stage liver disease score and minimally invasive treatment with regard to mortality of patients with hepatitis B virus-associated hepatocellular carcinoma and portal vein tumor thrombi

The development of minimally invasive treatment over the last two decades has had a great impact on hepatitis B virus (HBV)-associated primary liver cancer. The model for end-stage liver disease (MELD) score is the optimal evaluated parameter for mortality in patients with end-stage liver disease. However, the association between MELD score and minimally invasive treatment with regard to the mortality of patients with HBV-associated hepatocellular carcinoma (HCC) with a portal vein tumor thrombus (PVTT) remains unclear. In the present study, a total of 173 patients who had been diagnosed with HBV-associated HCC and PVTT in the Beijing Ditan Hospital (Beijing, China), between January 2012 and January 2015, were screened. Follow-up was performed to observe the survival time and collect information on the demographic characteristics and associated clinical indicators present in the cohort. The patient's age, sex, laboratory parameters and the use of minimally invasive treatment were analyzed with SPSS 20.0 software. Independent risk factors for mortality were screened by Cox regression analysis. Logistic regression indicated that there was an interaction between the MELD score and minimally invasive treatment. In addition, a MELD score ≤17.85 was associated with a lower mortality rate subsequent to minimally invasive treatment.

The EMALT Score: An Improved Model for Prediction of Early Mortality in Liver Transplant Recipients

PURPOSE

Needs, risks, and outcomes of patients admitted to a post liver transplant intensive care unit (POLTICU) differ in important ways from those admitted to pretransplant intensive care units (ICUs). The aim of this study was to create the optimal model to risk stratify POLTICU patients.

METHODS

Consecutive patients who underwent first deceased donor liver transplantation (LT) at a large United States center between 2008 and 2014 were followed from admission to LT and to discharge or death. Receiver-operating characteristic analysis was performed to assess the value of various scores in predicting in-hospital mortality. A predictive model was developed using logistic regression analysis.

RESULTS

A total of 697 patients underwent LT, and 3.2% died without leaving the hospital. A model for in-hospital mortality was derived from variables available within 24 hours of admission to the POLTICU. Key variables best predicting survival were white blood cell count, 24-hour urine output, and serum glucose. A model using these variables performed with an area under the curve (AUC) of 0.88, compared to the Acute Physiology and Chronic Health Evaluation III and Model for End-Stage Liver Disease, which performed with AUCs of 0.74 and 0.60, respectively.

CONCLUSION

An improved model, the early mortality after LT (EMALT) score, performs better than conventional models in predicting in-hospital mortality after LT.