The brain is at risk of serious cerebrovascular insult during the 400,000 cardiopulmonary bypass (CPB) surgeries performed annually in the United States. There is strong clinical evidence that over two-thirds of patients exhibit neurologic or neuropsychologic (NP) Postoperative deficits caused by emboli passing to the brain during surgery. There is a critical need for a device that can not only detect emboli, but also classify them as to type. Classification is critical for correlating neurological deficits with emboli type, determining the source of the emboli, and subsequently changing surgical procedures and/or administering neuroprotective agents to minimize brain injury. Continuous-wave Doppler ultrasound equipment can detect emboli, but cannot provide the information needed to classify emboli composition. In Phase I, ORINCON and the Bowman Gray School of Medicine utilized broadband pulse-echo ultrasound and an artificial neural network to demonstrate significant classification capability on in-vitro data. In Phase II, we will refine and integrate components from Phase I into a PC-based system capable of accurate, real-time emboli detection and classification in both extracorporeal pump circuits and the carotid artery. Extensive in-vitro and in-vivo data will be collected to permit refinement and thorough evaluation of classification capabilities. Clinical studies will be performed to correlate neuropsychologic deficits with embolus composition.
There is immediate commercial potential for a low-cost (less than 25K), real-time automated emboli detection and classification system. The market includes manufacturers of cardiopulmonary pump circuit devices (Medtronics, Pall, Cobe, Sarns), hundreds of medical centers performing cardiopulmonary bypass, and producers of neuroprotective drugs (Astra, Bayer, Sterling). A letter from Medtronics expressing this commercial potential is enclosed.
