Every individual has a unique immunologic identifier known as the HLA type. Further, each individual can produce antibodies directed at other HLA types, which serves to direct the immune system away from the self. In transplantation, the HLA mismatch between the organ donor and the recipient is the basis for many post-transplant complications, and it ultimately limits the survival of the transplant recipient. We propose to use combine 2 national datasets, SRTR and PHTS, in order to analyze the impact of different HLA combinations on posttransplant complications and survival. The goal is to identify specific HLA patterns that may be either helpful or harmful to long-term survival and to allow the clinician to recognize them in a timely manner in order to improve key aspects of transplant decision-making at the time of donor organ evaluation and post-transplant care. We plan to use these findings to develop a decision support tool for transplant physicians to use while evaluating donors. If successful, this work will allow transplant physicians to use HLA data to drive key clinical decisions on an individualized basis in order to improve long-term survival and quality of life after pediatric heart transplantation. This work applies to all pediatric heart transplant patients, and is particularly relevant to Fontan patients, who are at increased risk of having complex, potentially adverse pre-transplant HLA antibody profiles.

The first two aims are to analyze the impact of HLA donor/recipient matching of pretransplant HLA antibodies on survival and adverse events. The third aim is to develop a decision support tool for predicting the results of a virtual crossmatch at the time of a donor offer. In all aims, we propose to develop precise information that is useful for individualized patient decisions, aka precision medicine.