Incidental findings during donor liver assessment: Single center experience

Intraoperative consultation of donor liver is an important part of transplant evaluation and determination of liver eligibility. In this study, we describe incidental pathologic findings discovered during the pretransplant evaluation of liver donors in our Institution from 1/2010 to 12/2022. During this 13-year period 369 intraoperative consultations from 262 liver donors were performed. Of those cases, incidental findings were identified in 22 cases (5.9 %) from 19 donors (7.3 %); two donors had more than one lesion. The median age of this subset of patients was 53 years (range: 18-70) and females predominated (63 %). Sixteen of the donors had abnormal findings in the liver: 6 bile duct hamartoma (BDH), 5 hyalinized nodule with Histoplasma capsulatum, 5 focal nodular hyperplasia (FNH), 2 bile duct adenomas (BDA), 1 biliary cyst and 1 hemangioma. One donor had both FNH and a BDH. One BDH and 1 BDA case was misdiagnosed as malignancy during the frozen section evaluation. Three donors had extrahepatic pathologies: a pancreatic tail schwannoma, a low-grade appendiceal mucinous neoplasm, and a lymph node with metastatic endometrial endometrioid adenocarcinoma. Of the 19 livers, the final organ disposition was available for 9: 6 were transplanted (67 %) and 3 were discarded (33 %). Two of the 3 discarded organs were misdiagnosed BDH and BDA cases, and one was incorrectly reported as having 90 % microvesicular steatosis during the frozen assessment. We present the clinicopathologic characteristics of liver donors with incidental findings during the pre-transplant evaluation which could lead to unwarranted graft dismissal if misdiagnosed. Additionally, incidental fungal infections can have implications for immunosuppressive therapy and the decision to use or reject the graft.

Computed tomography-based machine learning for donor lung screening before transplantation

BACKGROUND

Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation.

METHODS

Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures.

RESULTS

Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant.

CONCLUSIONS

We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.

Assessment of potential heart donors: A statement from the French heart transplant community

Assessment of potential donors is an essential part of heart transplantation. Despite the shortage of donor hearts, donor heart procurement from brain-dead organ donors remains low in France, which may be explained by the increasing proportion of high-risk donors, as well as the mismatch between donor assessment and the transplant team's expectations. Improving donor and donor heart assessment is essential to improve the low utilization rate of available donor hearts without increasing post-transplant recipient mortality. This document provides information to practitioners involved in brain-dead donor management, evaluation and selection, concerning the place of medical history, electrocardiography, cardiac imaging, biomarkers and haemodynamic and arrhythmia assessment in the characterization of potential heart donors.