The University of Massachusetts Amherst

Chen's NIH Grant Supports New Method to Predetermine the Viability of Donor Kidneys

Yu Chen, biomedical engineering associate professor, is the principal investigator (PI) on a $679,945 R01 grant to UMass Amherst from the National Institutes of Health (NIH) that will develop a groundbreaking optical coherence tomography (OCT) device for the preassessment of kidneys and their viability as potential transplant organs.

Chen says that his new device is aimed at answering the vital clinical need to decrease the more than 8,000 deaths that occur worldwide each year from failure to find compatible kidney transplant matches, to reduce the nearly four-year lag time on donor waitlists, and to cut down the number of failed transplants.

According to Dr. Chen, the current process for screening donor kidneys employs two methods: pathological scores based on anatomical features from a biopsy; and the Kidney Donor Profile Index (KDPI), derived from the donor's medical history, which includes data about hypertension, diabetes, weight and other factors.

However, clinical research indicates that these two procedures have limited predictive effectiveness when trying to predetermine the compatibility of a kidney for transplant into a particular patient.

"OCT is an imaging technology that can obtain high-resolution, non-invasive, cross-sectional images of biological tissues in sit and in real time. We have demonstrated that OCT can provide non-invasive real-time, histopathological information on the kidney that is impossible to obtain using an other known prodecure." said Chen.

Chen believes his, "preliminary trials have demonstrated that OCT imaging of human donor kidneys with a hand-held unit in the operating room is safe and that the entire kidney can be evaluated within a realtively short period of time."

According to Chen, "Our central hypothesis is that more comprehensive morphological parameters as measured by OCT can be used to determine post-transplantation renal function."

Chen notes that this NIH project is a collaborative proposal involving interdisciplinary skills in OCT, robotics, deep learning, surgery, and nephrology; all employed “to facilitate the translation from the bench to the bedside.”

“This technology can increase the number of healthy kidneys available for transplantation by making the most efficient use of available donor kidneys, eliminate the possible use of bad donor kidneys, provide an [accurate] measure of expected post-transplant renal function, and allow better distinction between post-transplant immunological rejection and
ischemic-induced acute renal failure," Chen concluded.

 

Yu Chen