Potential savings in the treatment pathway of liver transplantation: an inter-sectorial analysis of cost-rising factors.

INTRODUCTION: Identification of cost-driving factors in patients undergoing liver transplantation is essential to target reallocation of resources and potential savings. AIM: The aim of this study is to identify main cost-driving factors in liver transplantation from the perspective of the Statutory Health Insurance. METHODS: Variables were analyzed with multivariable logistic regression to determine their influence on high cost cases (fourth quartile) in the outpatient, inpatient and rehabilitative healthcare sectors as well as for medications. RESULTS: Significant cost-driving factors for the inpatient sector of care were a high labMELD-score (OR 1.042), subsequent re-transplantations (OR 7.159) and patient mortality (OR 3.555). Expenditures for rehabilitative care were significantly higher in patients with a lower adjusted Charlson comorbidity index (OR 0.601). The indication of viral cirrhosis and hepatocellular carcinoma resulted in significantly higher costs for medications (OR 21.618 and 7.429). For all sectors of care and medications each waiting day had a significant impact on high treatment costs (OR 1.001). Overall, cost-driving factors resulted in higher median treatment costs of 211,435 €. CONCLUSIONS: Treatment costs in liver transplantation were significantly influenced by identified factors. Long pre-transplant waiting times that increase overall treatment costs need to be alleviated by a substantial increase in donor organs to enable transplantation with lower labMELD-scores. Disease management programs, the implementation of a case management for vulnerable patients, medication plans and patient tracking in a transplant registry may enable cost savings, e.g., by the avoidance of otherwise necessary re-transplants or incorrect medication.

Decision modelling for economic evaluation of liver transplantation.

As the gap between a shortage of organs and the immense demand for liver grafts persists, every available donor liver needs to be optimized for utility, urgency and equity. To overcome this challenge, decision modelling might allow us to gather evidence from previous studies as well as compare the costs and consequences of alternative options. For public health policy and clinical intervention assessment, it is a potentially powerful tool. The most commonly used types of decision analytical models include decision trees, the Markov model, microsimulation, discrete event simulation and the system dynamic model. Analytic models could support decision makers in the field of liver transplantation when facing specific problems by synthesizing evidence, comprising all relevant options, generalizing results to other contexts, extending the time horizon and exploring the uncertainty. For modeling studies of economic evaluation for transplantation, understanding the current nature of the disease is crucial, as well as the selection of appropriate modelling techniques. The quality and availability of data is another key element for the selection and development of decision analytical models. In addition, good practice guidelines should be complied, which is important for standardization and comparability between economic outputs.

Evaluation of published assessment tools for comorbidity in liver transplantation: a systematic review protocol.

INTRODUCTION: Liver transplantation is considered the best therapy option for end-stage liver disease. Different factors including recipient comorbidity at time of transplantation are supposed to have substantial impact on outcomes. Although several studies have focused on comorbidity assessment indices for liver transplant recipients, there is no systematic review available on the methodological details and prognostic accuracy of these instruments. The aim of this study is to systematically review recipient comorbidity assessment indices in the context of liver transplantation. METHODS AND ANALYSIS: PubMed, Embase, Web of Science and PsyINFO databases will be searched. Studies describing, using or evaluating specific assessment tools to predict the effect of comorbidity on clinical outcomes after liver transplantation will be included. The selection will be conducted independently by two reviewers. The study characteristics and methodological information on published comorbidity assessment tools will be extracted into a predefined structural table. This approach will be deployed to systematically extract information on the validity, reliability and practical feasibility of investigated comorbidity assessment tools for comparative evaluation. Narrative information synthesis will be conducted, and additional meta-analytical comparison will be performed, if appropriate. ETHICS AND DISSEMINATION: All data are collected from published literature. Thus, formal ethics review for the research is not required. The findings of this systematic review will be published in a peer-reviewed journal and presented at relevant conferences. The results of this systematic review will be highly relevant for further research on prognostic models, clinical decision making and optimisation of donor organ allocation. PROSPERO REGISTRATION NUMBER: CRD42017074609.

High resource utilization in liver transplantation-how strongly differ costs between the care sectors and what are the main cost drivers?: a retrospective study.

To control treatment pathways of transplant patients across healthcare sectors, a profound knowledge of the underlying cost structure is necessary. The aim of this study was to analyze the resource utilization of patients undergoing liver transplantation. Data on resource utilization for 182 liver-transplanted patients was investigated retrospectively. The observational period started with the entry on the waiting list and ended up to 3 years after transplantation. Median treatment cost was 144 424€. During waiting time, median costs amounted to 9466€; 72% of costs were attributed to inpatient care, 3% to outpatient care, and 26% to pharmaceuticals. During the first year after transplantation, median costs of 105 566€ were calculated; 83% were allocated for inpatient and 1% outpatient care, 14% for drugs, and 1% for rehabilitative care. During follow-up after the first year of transplantation, median costs amounted to 20 115€; 75% of these were caused by pharmaceuticals, 21% by inpatient, 4% by outpatient, and <1% by rehabilitative services. Subgroup analyses (e.g., for labMELD scores) were done. Costs incurred by inpatient care and pharmaceuticals are the dominating cost factors. These findings encourage a debate on challenges and improvements for cost-efficient clinical management between different healthcare sectors.