Liver Viability Testing During Machine Perfusion

FAQ

Liver Perfusion Viability Assessment: FAQ 1. What is the purpose of viability testing in liver transplantation? Viability testing aims to predict the function of a liver graft after transplantation. This is crucial due to the increasing use of marginal donor livers, which are more susceptible to ischemia-reperfusion injury (IRI) and primary non-function (PNF). By identifying non-viable livers pre-transplant, unnecessary surgeries and potential harm to recipients can be avoided. 2. Why are mitochondria central to viability testing? Mitochondria are the energy powerhouses of cells and are essential for maintaining cellular function and viability. During ischemia, mitochondria are damaged, leading to energy depletion and the release of harmful molecules that trigger inflammation and further injury. Assessing mitochondrial function during perfusion provides valuable insights into the overall health and viability of the liver graft. 3. What are the main types of machine perfusion used for liver viability assessment? The two primary types are: Normothermic machine perfusion (NMP): Perfusion at body temperature (37°C) with a blood-based solution, mimicking physiological conditions. This allows for real-time assessment of liver function and injury. Hypothermic machine perfusion (HMP): Perfusion at a lower temperature (4°C-8°C) with a preservation solution. This focuses on reducing preservation injury and restoring cellular energy. 4. What are the commonly assessed parameters during NMP? Several parameters are used to gauge viability during NMP: Perfusion quality: Stable portal vein and hepatic artery flows indicate good vascular function. Lactate clearance: A decrease in lactate levels suggests adequate oxygenation and metabolic activity. Transaminase levels: Elevated AST and ALT reflect hepatocyte injury. Bile production and quality: Bile flow, pH, and composition provide information about hepatobiliary function. Glucose metabolism: Glucose utilization and response to insulin indicate metabolic function. Acid-base balance: Maintaining a physiological pH is crucial for organ function. 5. What are the challenges associated with using lactate as a key viability marker during NMP? While lactate clearance is frequently used, its predictive value is limited. Challenges include: Variations in lactate thresholds: Different studies propose various cutoff values, making interpretation inconsistent. Confounding factors: Erythrocytes in the perfusate can also release lactate, especially during prolonged perfusion. Limited predictive power: Lactate clearance alone might not accurately predict post-transplant outcomes, as even severely injured livers can clear some lactate. 6. How is viability assessed during HMP? During HMP, the focus is on mitochondrial function and injury. A specific protein, flavin mononucleotide (FMN), released from damaged mitochondria, is measured in the perfusate. Elevated FMN levels correlate with increased mitochondrial injury and poor post-transplant outcomes. 7. What are the advantages of using FMN as a viability marker during HMP? FMN offers several advantages as a marker: Specificity: FMN is directly linked to mitochondrial injury, providing a precise indicator of cellular damage. Strong correlation with outcomes: Elevated FMN levels have been shown to accurately predict graft loss and dysfunction after transplantation. Established threshold: A clinically useful cutoff value for FMN has been identified, aiding decision-making. 8. What are the future directions in liver perfusion viability assessment? Standardized protocols: Establishing consistent perfusion techniques and assessment criteria across studies is vital for obtaining comparable data. Biomarker discovery: Continued research to identify new and more specific biomarkers of liver viability, potentially through advanced techniques like metabolomics and proteomics. Real-time monitoring: Development of technologies for continuous and real-time assessment of viability during perfusion, facilitating rapid decision-making. Artificial intelligence: Utilizing machine learning algorithms to analyze complex datasets and improve the accuracy of viability prediction.