Kevin Ward, MD, Kenn Oldham, PhD, Kayvan Najarian, PhD, Michael Heung, MD, Sardar, Ansari PhD
Product Description: A small, wearable, noninvasive monitor that predicts the onset of intradialytic hypotension during hemodialysis and provides a warning to dialysis clinic staff, allowing them to implement countermeasures to prevent the hypotensive episode.
Project Overview: Nearly 400,000 patients in the US suffer from end-stage renal disease or chronic kidney disease. These patients account for the 43.5 million hemodialysis sessions per year. Intradialytic hypotension (IDH), a significant drop in blood pressure, occurs in 20-30% of all hemodialysis sessions. This sometimes leads to session abandonment and fluid volume overload as patients are not able to be adequately dialyzed. It is the most severe complication of dialysis and leads to significant morbidity and mortality.
Patients undergoing hemodialysis are currently monitored by intermittent blood pressure measurements, patient-reported symptoms, hematocrit levels, and oxygen saturation levels. Patients who have had IDH in previous hemodialysis sessions may be pre-emptively treated with vasopressors to prevent IDH onset. These standard-of-care practices can only detect IDH once it has begun or try to prevent IDH from occurring, but they cannot predict IDH occurrence and allow nephrologists or dialysis nurses to implement preventative measures, such as decreasing the ultrafiltration rate, in time to prevent hypotension.
Emergency Medicine Physician Kevin Ward, MD, Mechanical Engineer Kenn Oldham, PhD, and Computational Medicine and Bioinformatics colleagues Associate Professor Kayvan Najarian, PhD, Nephrologist Michael Heung, MD, and Postdoctoral Research Fellow Sardar Ansari, PhD, have developed a small, wearable, noninvasive monitor that predicts the onset of IDH during hemodialysis and provides a warning to dialysis clinic staff, allowing them to implement countermeasures to prevent the hypotensive episode and continue the dialysis session. With Coulter funding, the team will build prototype devices to obtain clinical data on patients undergoing dialysis in the U-M Acute Dialysis Unit to refine and optimize the prediction algorithm. The goal of the study is to demonstrate the ability to predict IDH within a minimum of 2 minutes of onset with 80% sensitivity and specificity.
Email Thomas Marten (tmarten@umich.edu) for more information.