The impact of advanced patient age in liver transplantation: a European Liver Transplant Registry propensity-score matching study.

BACKGROUND: The futility of liver transplantation in elderly recipients remains under debate in the HCV eradication era. METHODS: The aim was to assess the effect of older age on outcome after liver transplantation. We used the ELTR to study the relationship between recipient age and post-transplant outcome. Young and elderly recipients were compared using a PSM method. RESULTS: A total of 10,172 cases were analysed. Recipient age >65 years was identified as an independent risk factor associated with reduced patient survival (HR:1.42 95%CI:1.23-1.65,p < 0.001). After PSM, 2124 patients were matched, and the same association was found between elderly recipients and patient survival and graft survival (p < 0.001). As hepatocellular carcinoma and alcoholic cirrhosis were independent prognostic factors for patient and graft survival a propensity score-matching was performed for each. Patient and graft survival were significantly worse (p < 0.05) in the alcoholic cirrhosis elderly group. However, patient and graft survival in the hepatocellular carcinoma cohort were similar (p > 0.05) between groups. CONCLUSION: Liver transplantation is an acceptable and safe curative option for elderly transplant candidates, with worse long-term outcomes compare to young candidates. The underlying liver disease for liver transplantation has a significant impact on the selection of elderly patients.

LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment.

Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is invasive, costly, slow, and not always feasible during liver procurement. Consequently, surgeons often rely on subjective visual assessments based on the liver's colour and texture, which are prone to errors and heavily depend on the surgeon's experience. The aim of this study was to develop and validate a simple, rapid, and accurate method for detecting steatosis in donor livers to improve the decision-making process during liver procurement. We developed LiverColor, a co-designed software platform that integrates image analysis and machine learning to classify a liver graft into valid or non-valid according to its steatosis level. We utilized an in-house dataset of 192 cases to develop and validate the classification models. Colour and texture features were extracted from liver photographs, and graft classification was performed using supervised machine learning techniques (random forests and support vector machine). The performance of the algorithm was compared against biopsy results and surgeons' classifications. Usability was also assessed in simulated and real clinical settings using the Mobile Health App Usability Questionnaire. The predictive models demonstrated an area under the receiver operating characteristic curve of 0.82, with an accuracy of 85%, significantly surpassing the accuracy of visual inspections by surgeons. Experienced surgeons rated the platform positively, appreciating not only the hepatic steatosis assessment but also the dashboarding functionalities for summarising and displaying procurement-related data. The results indicate that image analysis coupled with machine learning can effectively and safely identify valid livers during procurement. LiverColor has the potential to enhance the accuracy and efficiency of liver assessments, reducing the reliance on subjective visual inspections and improving transplantation outcomes.