[Cost analysis of liver transplantation in a community hospital: association with the Model for End-stage Liver Disease, a prognostic index to prioritize the most severe patients]

Patients Benefit from Liver Transplantation for Hepatocellular Carcinoma beyond Milan Criteria without Harming the Health Care System

Liver transplantation (LT) is the only definitive treatment to cure hepatocellular carcinoma (HCC) in cirrhosis. Waiting-list candidates are selected by the model for end-stage liver disease (MELD). However, many indications are not sufficiently represented by labMELD. For HCC, patients are selected by Milan-criteria: Milan-in qualifies for standard exception (SE) and better organ access on the waiting list; while Milan-out patients are restricted to labMELD and might benefit from extended criteria donor (ECD)-grafts. We analyzed a cohort of 102 patients (2011−2020). Patients with labMELD (no SE, Milan-out, n = 56) and matchMELD (SE-HCC, Milan-in, n = 46) were compared. The median overall survival was not significantly different (p = 0.759). No difference was found in time on the waiting list (p = 0.881), donor risk index (p = 0.697) or median costs (p = 0.204, EUR 43,500 (EUR 17,800−185,000) for labMELD and EUR 30,300 (EUR 17,200−395,900) for matchMELD). Costs were triggered by a cut-off labMELD of 12 points. Overall, the deficit increased by EUR 580 per labMELD point. Cost drivers were re-operation (p < 0.001), infection with multiresistant germs (p = 0.020), dialysis (p = 0.017), operation time (p = 0.012) and transfusions (p < 0.001). In conclusion, this study demonstrates that LT for HCC is successful and cost-effective in low labMELD patients independent of Milan-criteria. Therefore, ECD-grafts are favorized in Milan-out HCC patients with low labMELD.

Predictors of micro-costing components in liver transplantation

OBJECTIVES:

Although liver transplantation procedures are common and highly expensive, their cost structure is still poorly understood. This study aimed to develop models of micro-costs among patients undergoing liver transplantation procedures while comparing the role of individual clinical predictors using tree regression models.

METHODS:

We prospectively collected micro-cost data from patients undergoing liver transplantation in a tertiary academic center. Data collection was conducted using an Intranet registry integrated into the institution's database for the storing of financial and clinical data for transplantation cases.

RESULTS:

A total of 278 patients were included and accounted for 300 procedures. When evaluating specific costs for the operating room, intensive care unit and ward, we found that in all of the sectors but the ward, human resources were responsible for the highest costs. High cost supplies were important drivers for the operating room, whereas drugs were among the top four drivers for all sectors. When evaluating the predictors of total cost, a MELD score greater than 30 was the most important predictor of high cost, followed by a Donor Risk Index greater than 1.8.

CONCLUSION:

By focusing on the highest cost drivers and predictors, hospitals can initiate programs to reduce cost while maintaining high quality care standards.