In the United States, there are approximately 110,000 people in need of a kidney transplant, 13 of which die every day before finding a donor match. Successful transplantation, particularly of kidneys, is contingent on the compatibility of donor antigens and the recipient’s antibodies. Current methods for determining compatibility via a crossmatch is quite cumbersome and dependent on the laboratory, evaluator, and previous transplant center experience to yield an accuracy of just 70-85%. UNC has streamlined this process by employing algorithms using data-driven mathematical models to predict flow cytometric crossmatch outcomes. If the virtual crossmatch is determined to be likely positive, indicating likely reactivity between recipient and donor, the organ offer is typically declined and issued to another potential recipient. This process is lengthy and delays the overall transplant. Expediting improved virtual recipient and donor compatibility assessments are even more pertinent now, than ever, due to the current COVID-19 pandemic resulting in a number of complications for the organ matching process. These new algorithms project to (1) increase accuracy to 97%, correlating to lower cold ischemic time per organ offer and (2) speed up the evaluation process without human bias or intervention, all while (3) improving accessibility for attending physicians with an intuitive app.
- Development of data-driven models for the flow cytometric crossmatch Weimer, Eric T., and Katherine A. Newhall. “Development of data-driven models for the flow cytometric crossmatch.” Human immunology 80.12 (2019): 983-989.