Influence of donor age on liver transplantation outcomes: A multivariate analysis and comparative study.

BACKGROUND: The growing disparity between the rising demand for liver transplantation (LT) and the limited availability of donor organs has prompted a greater reliance on older liver grafts. Traditionally, utilizing livers from elderly donors has been associated with outcomes inferior to those achieved with grafts from younger donors. By accounting for additional risk factors, we hypothesize that the utilization of older liver grafts has a relatively minor impact on both patient survival and graft viability. AIM: To evaluate the impact of donor age on LT outcomes using multivariate analysis and comparing young and elderly donor groups. METHODS: In the period from April 2013 to December 2018, 656 adult liver transplants were performed at the University Hospital Merkur. Several multivariate Cox proportional hazards models were developed to independently assess the significance of donor age. Donor age was treated as a continuous variable. The approach involved univariate and multivariate analysis, including variable selection and assessment of interactions and transformations. Additionally, to exemplify the similarity of using young and old donor liver grafts, the group of 87 recipients of elderly donor liver grafts (≥ 75 years) was compared to a group of 124 recipients of young liver grafts (≤ 45 years) from the dataset. Survival rates of the two groups were estimated using the Kaplan-Meier method and the log-rank test was used to test the differences between groups. RESULTS: Using multivariate Cox analysis, we found no statistical significance in the role of donor age within the constructed models. Even when retained during the entire model development, the donor age's impact on survival remained insignificant and transformations and interactions yielded no substantial effects on survival. Consistent insignificance and low coefficient values suggest that donor age does not impact patient survival in our dataset. Notably, there was no statistical evidence that the five developed models did not adhere to the proportional hazards assumption. When comparing donor age groups, transplantation using elderly grafts showed similar early graft function, similar graft (P = 0.92), and patient survival rates (P = 0.86), and no significant difference in the incidence of postoperative complications. CONCLUSION: Our center's experience indicates that donor age does not play a significant role in patient survival, with elderly livers performing comparably to younger grafts when accounting for other risk factors.

Use of machine learning models for identification of predictors of survival and tumour recurrence in liver transplant recipients with hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the leading indications for liver transplantation (LT) however, selection criteria remain controversial. We aimed to identify survival factors and predictors for tumour recurrence using machine learning (ML) methods. We also compared ML models to the Cox regression model. Methods: Thirty pretransplant donor and recipient general and tumour specific parameters were analysed from 170 patients who underwent orthotopic liver transplantation for HCC between March 2013 and December 2019 at the University Hospital Merkur, Zagreb. Survival rates were calculated using the Kaplan-Meier method and multivariate analysis was performed using the Cox proportional hazards regression model. Data was also processed through Coxnet (a regularized Cox regression model), Random Survival Forest (RSF), Survival Support Vector Machine (SVM) and Survival Gradient Boosting models, which included pre-processing, variable selection, imputation of missing data, training and cross-validation of the models. The cross-validated concordance index (CI) was used as an evaluation metric and to determine the best performing model. Results: Kaplan-Meier curves for 5-year survival time showed survival probability of 80% for recipient survival and 82% for graft survival. The 5-year HCC recurrence was observed in 19% of patients. The best predictive accuracy was observed in the RSF model with CI of 0.72, followed by the Survival SVM model (CI 0.70). Overall ML models outperform the Cox regression model with respect to their limitations. Random Forest analysis provided several relevant outcome predictors: alpha fetoprotein (AFP), donor C-reactive protein (CRP), recipient age and neutrophil to lymphocyte ratio (NLR). Cox multivariate analysis showed similarities with RSF models in identifying detrimental variables. Some variables such as donor age and number of transarterial chemoembolization treatments (TACE) were pointed out, but these were not influential in our RSF model. Conclusions: Using ML methods in addition to classical statistical analysis, it is possible to develop sufficient prognostic models, which, compared to established risk scores, could help us quantify survival probability and make changes in organ utilization.

Learning Bayesian networks from survival data using weighting censored instances.

Different survival data pre-processing procedures and adaptations of existing machine-learning techniques have been successfully applied to numerous fields in clinical medicine. Zupan et al. (2000) proposed handling censored survival data by assigning distributions of outcomes to shortly observed censored instances. In this paper, we applied their learning technique to two well-known procedures for learning Bayesian networks: a search-and-score hill-climbing algorithm and a constraint-based conditional independence algorithm. The method was thoroughly tested in a simulation study and on the publicly available clinical dataset GBSG2. We compared it to learning Bayesian networks by treating censored instances as event-free and to Cox regression. The results on model performance suggest that the weighting approach performs best when dealing with intermediate censoring. There is no significant difference between the model structures learnt using either the weighting approach or by treating censored instances as event-free, regardless of censoring.

Impact of censoring on learning Bayesian networks in survival modelling.

OBJECTIVE: Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. METHODS AND MATERIALS: We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. RESULTS: We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. CONCLUSION: Presented methods for learning Bayesian networks from data can be used to learn from censored survival data in the presence of light censoring (up to 20%) by treating censored cases as event-free. Given intermediate or heavy censoring, the learnt models become tuned to the majority class and would thus require a different approach.