The concept of optimal cerebral perfusion pressure (OptCPP) management is being developed and investigated to personalize the treatment of traumatic brain injury patients. One of the main factors limiting the clinical application of this method is intermittent nature of the slow arterial blood pressure and intracranial pressure waves which are used to identify the OptCPP value. In order to improve the reliability of OptCPP value identification, it is necessary to accumulate at least 4 hours of cerebrovascular autoregulation, cerebral perfusion pressure, and other multimodal physiological monitoring data. This results in delayed application of OptCPP management therapy, which may adversely affect the patients’ treatment. Despite lengthy data acquisition, the OptCPP value can be identified in only about 60% of the total patient monitoring time. Moreover, for some patients the OptCPP value cannot be determined at all. In this project, we will to develop an innovative OptCPP identification and management algorithm based on machine learning that enables reliable and continuous identification of OptCPP values.
Project funding:
KTU Research and Innovation Fund
Period of project implementation: 2020-04-06 - 2020-12-31
Project partners: Lithuanian University of Health Sciences
