Application of the BAR score as a predictor of short- and long-term survival in liver transplantation patients

LIVER RETRANSPLANTATION: PROGNOSTIC SCORES AND RESULTS IN THE STATE OF PARANÁ

BACKGROUND

Hepatic retransplantation is associated with higher morbidity and mortality when compared to primary transplantation. Given the scarcity of organs and the need for efficient allocation, evaluating parameters that can predict post-retransplant survival is crucial.

AIMS

This study aimed to analyze prognostic scores and outcomes of hepatic retransplantation.

METHODS

Data on primary transplants and retransplants carried out in the state of Paraná in 2019 and 2020 were analyzed. The two groups were compared based on 30-day survival and the main prognostic scores of the donor and recipient, namely Model for End-Stage Liver Disease (MELD), MELD-albumin (MELD-a), Donor MELD (D-MELD), Survival Outcomes Following Liver Transplantation (SOFT), Preallocation Score to Predict Survival Outcomes Following Liver Transplantation (P-SOFT), and Balance of Risk (BAR).

RESULTS

A total of 425 primary transplants and 30 retransplants were included in the study. The main etiology of hepatopathy in primary transplantation was ethylism (n=140; 31.0%), and the main reasons for retransplantation were primary graft dysfunction (n=10; 33.3%) and hepatic artery thrombosis (n=8; 26.2%). The 30-day survival rate was higher in primary transplants than in retransplants (80.5% vs. 36.7%, p=0.001). Prognostic scores were higher in retransplants than in primary transplants: MELD 30.6 vs. 20.7 (p=0.001); MELD-a 31.5 vs. 23.5 (p=0.001); D-MELD 1234.4 vs. 834.0 (p=0.034); SOFT 22.3 vs. 8.2 (p=0.001); P-SOFT 22.2 vs. 7.8 (p=0.001); and BAR 15.6 vs. 8.3 (p=0.001). No difference was found in terms of Donor Risk Index (DRI).

CONCLUSIONS

Retransplants exhibited lower survival rates at 30 days, as predicted by prognostic scores, but unrelated to the donor's condition.

Recipient Survival after Orthotopic Liver Transplantation: Interpretable Machine Learning Survival Tree Algorithm for Patient-Specific Outcomes

BACKGROUND

Elucidating contributors affecting liver transplant survival is paramount. Current methods offer crude global group outcomes. To refine patient-specific mortality probability estimation and to determine covariate interaction using recipient and donor data, we generated a survival tree algorithm, Recipient Survival After Orthotopic Liver Transplantation (ReSOLT), using United Network Organ Sharing (UNOS) transplant data.

STUDY DESIGN

The UNOS database was queried for liver transplants in patients ≥18 years old between 2000 and 2021. Preoperative factors were evaluated with stepwise logistic regression; 43 significant factors were used in survival tree modeling. Graft survival of <7 days was excluded. The data were split into training and testing sets and further validated with 10-fold cross-validation. Survival tree pruning and model selection was achieved based on Akaike information criterion and log-likelihood values. Log-rank pairwise comparisons between subgroups and estimated survival probabilities were calculated.

RESULTS

A total of 122,134 liver transplant patients were included for modeling. Multivariable logistic regression (area under the curve = 0.742, F1 = 0.822) and survival tree modeling returned 8 significant recipient survival factors: recipient age, donor age, recipient primary payment, recipient hepatitis C status, recipient diabetes, recipient functional status at registration and at transplantation, and deceased donor pulmonary infection. Twenty subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves (p < 0.001 among all by log rank test) with 5- and 10-year survival probabilities.

CONCLUSIONS

Survival trees are a flexible and effective approach to understand the effects and interactions of covariates on survival. Individualized survival probability following liver transplant is possible with ReSOLT, allowing for more coherent patient and family counseling and prediction of patient outcome using both recipient and donor factors.

Interpretable prediction of mortality in liver transplant recipients based on machine learning

BACKGROUND

Accurate prediction of the mortality of post-liver transplantation is an important but challenging task. It relates to optimizing organ allocation and estimating the risk of possible dysfunction. Existing risk scoring models, such as the Balance of Risk (BAR) score and the Survival Outcomes Following Liver Transplantation (SOFT) score, do not predict the mortality of post-liver transplantation with sufficient accuracy. In this study, we evaluate the performance of machine learning models and establish an explainable machine learning model for predicting mortality in liver transplant recipients.

METHOD

The optimal feature set for the prediction of the mortality was selected by a wrapper method based on binary particle swarm optimization (BPSO). With the selected optimal feature set, seven machine learning models were applied to predict mortality over different time windows. The best-performing model was used to predict mortality through a comprehensive comparison and evaluation. An interpretable approach based on machine learning and SHapley Additive exPlanations (SHAP) is used to explicitly explain the model's decision and make new discoveries.

RESULTS

With regard to predictive power, our results demonstrated that the feature set selected by BPSO outperformed both the feature set in the existing risk score model (BAR score, SOFT score) and the feature set processed by principal component analysis (PCA). The best-performing model, extreme gradient boosting (XGBoost), was found to improve the Area Under a Curve (AUC) values for mortality prediction by 6.7%, 11.6%, and 17.4% at 3 months, 3 years, and 10 years, respectively, compared to the SOFT score. The main predictors of mortality and their impact were discussed for different age groups and different follow-up periods.

CONCLUSIONS

Our analysis demonstrates that XGBoost can be an ideal method to assess the mortality risk in liver transplantation. In combination with the SHAP approach, the proposed framework provides a more intuitive and comprehensive interpretation of the predictive model, thereby allowing the clinician to better understand the decision-making process of the model and the impact of factors associated with mortality risk in liver transplantation.

BAR Score Performance in Predicting Survival after Living Donor Liver Transplantation: A Single-Center Retrospective Study

METHODS

146 adult liver transplant recipients were included. Univariate and multivariate analyses were used to determine the independent predictors of survival at 3 months, 1 year, and 5 years. The receiver operating characteristic (ROC) curve for the BAR score was plotted, and the area under the ROC curve (AUROC) was calculated. Kaplan-Meier curve and log-rank test were used to compare survival above and below the best cutoff values.

RESULTS

The mean age was 52.45 ± 8.54 years, and 59.6% were males. The survival rates were 89, 78.8, and 72% at 3 months, 1 year, and 5 years, respectively. The BAR score demonstrated a clinically significant value in the prediction of 3-month (AUROC = 0.89), 1-year (AUROC = 0.76), and 5-year survival (AUROC = 0.71). Among the investigated factors associated with survival, BAR score <10 points was the only independent predictor of 3-month (OR 7.34, p < 0.0001), 1-year (OR 3.37, p=0.001), and 5-year survival (OR 2.83, p=0.044).

CONCLUSIONS

BAR is a simple and easily applicable scoring system that could significantly predict short- and long-term survival after LDLT. A large multicenter study is warranted to validate our results in the Egyptian population.

Improving Matching Between Recipient and Donor in Liver Transplantation: Are Scores Able to Predict Long-term Survival?

BACKGROUND

The primary goal was to calculate the value of delta Model for End-Stage Liver Disease (D-MELD) and Balance of Risk (BAR) scores in patients who underwent liver transplant. The secondary objective was to evaluate D-MELD and BAR scores' ability to predict patient and graft survival.

METHODS

We retrospectively evaluated 336 patients who underwent liver transplant in a tertiary medical center between January 2010 and December 2020. The D-MELD and the BAR scores were evaluated through a receiver operating characteristic curve with the calculation of area under the curve (AUC) to evaluate the predictive score power for 3-month, 6-month, 1-year, and 5-year patient and graft survivals.

RESULTS

The AUCs of D-MELD score in predicting 5-year patient and graft survival were 0.506 (95% CI, 0.43-0.57) and 0.49 (95% CI, 0.42-0.56), respectively. The AUCs of BAR score in predicting 5-year patient and graft survival were 0.50 (95% CI, 0.33-0.66) and 0.49 (95% CI, 0.30-0.67), respectively.

CONCLUSIONS

We could not confirm the ability to predict long-term survival by using D-MELD and BAR scores in our sample; however, there is a statistically significant trend in receiver operating characteristic curves of 5-year patient and graft survivals. We encourage the use of new scoring systems with a greater external validation to improve allograft allocation.

The Lactate-to-Platelet Ratio: A Novel Predictor for Short-Term Early Allograft Failure After Liver Transplantation

BACKGROUND

Early allograft dysfunction (EAD) is a criterion to evaluate initial graft dysfunction associated with inferior graft survival and postoperative complications after liver transplantation (LT). This study defined the lactate-to-platelet ratio (LPR) as lactate level immediately post-LT/platelet count on postoperative day 1 and evaluated its association with EAD and short-term graft failure.

MATERIALS AND METHODS

This study reviewed 434 deceased-donor LTs from individuals with confirmed brain death between January 2008 and December 2014. The area under the curve (AUC) was used to compare the predictive capacity for 3-month graft survival between EAD and the LPR. Along with LPR, the risk factors for 3-month graft failure were analyzed by multivariate analysis.

RESULTS

EAD was reported in 127 patients (31%). The LPR in patients with EAD was significantly higher than that in patients without EAD (9.8 vs 5.9, P < .001). In the multivariate analysis, both the LPR (per 1.0 increase) and EAD were independent risk factors for 3-month graft failure (hazard ratio [HR] =1.03, P = .03; and HR = 9.14, P = .001). The comparison of the AUCs between the LPR and EAD showed no significant difference (0.79 vs 0.78, P = .84), whereas the combination of EAD and LPR had a better predictive capacity than EAD alone (0.86 vs 0.78, P < .001). The LPR showed an inverse relationship for predicting 3-month graft survival.

CONCLUSIONS

The LPR is a continuous parameter that enables prediction of initial graft function and estimation of the 3-month graft failure rate with the advantages of early availability and simple calculations.

Liver transplantation: survival and indexes of donor-recipient matching

OBJECTIVE

The aim of this study was to determine the prospective capacity and impact of donor risk index, preallocation survival outcomes following liver transplant, donor model for end-stage liver disease, and balance of risk on patients' 30-day survival after liver transplantation.

METHODS

We prospectively analyzed patient survival in a multicentric observational cohort of adult liver transplantation through the year of 2019 at the state of Paraná, Brazil. The receiver operating characteristic curve, the area under the curve, and the best cutoff point (i.e., the Youden's index) were estimated to analyze the prognostic value of each index.

RESULTS

In total, 252 liver transplants were included with an average model for end-stage liver disease score of 21.17 and a 30-day survival of 79.76%. The donor risk index was the only prognostic variable with no relation to patients' 30-day mortality model for end-stage liver disease and donor model for end-stage liver disease have no prognostic value on receiver operating characteristic curve, but preallocation survival outcomes following liver transplant, survival outcomes following liver transplant, and balance of risk presented good relationship with this observation. The cutoff value was estimated in 11-12 points for balance of risk and 9-12 for preallocation survival outcomes following liver transplant and survival outcomes following liver transplant. The 30-day survival for the group of transplants with scores up to 12 points (n=172) in all the three indexes was 87.79%, and for those transplants with scores higher than 12 it was 36.36%.

CONCLUSIONS

The 30-day survival is 79.76%, and balance of risk, survival outcomes following liver transplant, and preallocation survival outcomes following liver transplant are the good prognostic indexes. The cutoff value of 12 points has clinical usefulness to predict the post-liver transplantation results.

BAR, SOFT AND DRI POST-HEPATIC TRANSPLANTATION: WHAT IS THE BEST FOR SURVIVAL ANALYSIS?

BACKGROUND

Liver transplantation is the treatment of choice for patients with terminal liver disease. The Balance of Risk Score (BAR), Survival Outcomes Following Liver Transplantation (SOFT) and Donor Risk Index (DRI) scores are predictive systems for post-transplant survival.

AIM

To evaluate the most accurate score and the best cutoff point for each predictor in the brazilian population.

METHOD

Retrospective cross-sectional study of 177 patients. Data on the recipient, donor and transplant were analyzed and the prognostic scores BAR, SOFT and DRI were calculated for each transplant. To determine the BAR and SOFT cutoff points associated with death in three months, ROC curves were adjusted. Results : The best cutoff point for BAR was 9 points with an area under the ROC curve=0.69 and for SOFT it was 12 points with an area under the ROC curve=0.73. The DRI score did not discriminate survival (p = 0.139).

CONCLUSION

The SOFT score proved to be better than BAR for survival analysis post-hepatic transplantation and the DRI was not effective.

The role of the comprehensive complication index for the prediction of survival after liver transplantation

In the last years, several scoring systems based on pre- and post-transplant parameters have been developed to predict early post-LT graft function. However, some of them showed poor diagnostic abilities. This study aims to evaluate the role of the comprehensive complication index (CCI) as a useful scoring system for accurately predicting 90-day and 1-year graft loss after liver transplantation. A training set (n = 1262) and a validation set (n = 520) were obtained. The study was registered at https://www.ClinicalTrials.gov (ID: NCT03723317). CCI exhibited the best diagnostic performance for 90 days in the training (AUC = 0.94; p < 0.001) and Validation Sets (AUC = 0.77; p < 0.001) when compared to the BAR, D-MELD, MELD, and EAD scores. The cut-off value of 47.3 (third quartile) showed a diagnostic odds ratio of 48.3 and 7.0 in the two sets, respectively. As for 1-year graft loss, CCI showed good performances in the training (AUC = 0.88; p < 0.001) and validation sets (AUC = 0.75; p < 0.001). The threshold of 47.3 showed a diagnostic odds ratio of 21.0 and 5.4 in the two sets, respectively. All the other tested scores always showed AUCs < 0.70 in both the sets. CCI showed a good stratification ability in terms of graft loss rates in both the sets (log-rank p < 0.001). In the patients exceeding the CCI ninth decile, 1-year graft survival rates were only 0.7% and 23.1% in training and validation sets, respectively. CCI shows a very good diagnostic power for 90-day and 1-year graft loss in different sets of patients, indicating better accuracy with respect to other pre- and post-LT scores.Clinical Trial Notification: NCT03723317.

Accuracy of the BAR score in the prediction of survival after liver transplantation

INTRODUCTION AND AIM

The Balance of Risk (BAR) Score, a simple scoring system that combines six independent donor and recipient variables to predict outcome after liver transplantation (LT), was validated in a large U.S./European cohort of patients. This study aims to assess the performance of the BAR score to predict survival after liver transplantation and determine the factors associated with short and long-term survival in Latin-American patients.

MATERIAL AND METHODS

A retrospective cohort study was performed in 194 patients [112 (55.4%) males; mean age 52±14 years] who underwent 202 LT during the period 2003-2015. Demographic, clinical, pathological and surgical variables, as well as mortality and survival rates, were analyzed. The BAR score was investigated through a receiver operating characteristics (ROC) curve with the calculation of the area under the curve (AUC) to evaluate the predictive score power for 3-month, 1 and 5-year mortality in a matched donor-recipient cohort. Youden index was calculated to identify optimal cutoff points.

RESULTS

The AUC of BAR score in predicting 3-month, 1-year and 5-year mortality were 0.755 (CI95% 0.689-0.812), 0.702 (CI95% 0.634-0.764) and 0.610 (CI95% 0.539-0.678) respectively. The best cut-off point was a BAR score ≥15 points. In the multivariate analysis BAR score <15 was associated with higher survival rates at 3 months and 1 and 5-years.

CONCLUSIONS

BAR score <15 points is an independent predictor of better short and long-term survival in Latin-American patients undergoing LT. The BAR scoring system has an adequate diagnostic capacity allowing to predict 3 and 12-month mortality.

Predictive Capacity of Risk Models in Liver Transplantation

Supplemental Digital Content is available in the text.

Several risk models to predict outcome after liver transplantation (LT) have been developed in the last decade. This study compares the predictive performance of 7 risk models.

Liver transplantation for critically ill cirrhotic patients: Overview and pragmatic proposals

Liver transplantation for critically ill cirrhotic patients with acute deterioration of liver function associated with extrahepatic organ failures is controversial. While transplantation has been shown to be beneficial on an individual basis, the potentially poorer post-transplant outcome of these patients taken as a group can be held as an argument against allocating livers to them. Although this issue concerns only a minority of liver transplants, it calls into question the very heart of the allocation paradigms in place. Indeed, most allocation algorithms have been centered on prioritizing the sickest patients by using the model for end-stage liver disease score. This has led to allocating increasing numbers of livers to increasingly critically ill patients without setting objective or consensual limits on how sick patients can be when they receive an organ. Today, finding robust criteria to deem certain cirrhotic patients too sick to be transplanted seems urgent in order to ensure the fairness of our organ allocation protocols. This review starts by fleshing out the argument that finding such criteria is essential. It examines five types of difficulties that have hindered the progress of recent literature on this issue and identifies various strategies that could be followed to move forward on this topic, taking into account the recent discussion on acute on chronic liver failure. We move on to review the literature along four axes that could guide clinicians in their decision-making process regarding transplantation of critically ill cirrhotic patients.