Endovascular Perfusion Augmentation for Critical Care (EPACC): Personalized and Adaptive Therapy for Resuscitation After Trauma

Post-injury resuscitation is predicated on maintaining adequate systemic perfusion via balanced blood transfusion, crystalloids, and vasopressors. Yet, these medical supplies are often limited in resource-constrained environments. Furthermore, the care of critically injured patients during damage control resuscitation requires continuous patient management at the bedside to optimize outcomes. This project aims to develop a novel resuscitation platform that optimizes critical care management in real time. By harnessing the power of endovascular devices with AI assisted fluid and medication delivery, this platform can provide highly nuanced, dynamic and patient-centered precision critical care beyond what is possible by conventional means. This is a multi-institution project with collaborators from Wake Forest School of Medicine, the United States Air Force, and the Naval Medical Research.

People

Faculty

  • M. Austin Johnson, Department of Emergency Medicine, UC Davis School of Medicine
  • Jason Adams, Pulmonary and Critical Care Medicine, UC Davis School of Medicine.
  • Chen-Nee Chuah, Electrical & Computer Engineering

Graduate Students

  • Chitrabanu Gupta, ECE (PhD)
  • Rahul Krishnamurthy, ECE (MS)

Alumni

  • Dr. Debraj Basu (postdoc, 2019-2021)
  • Fatemeh Radaei, Computer Science (MS, 2020)
  • Justin Wang, Computer Science (BS)

Publications

  • M. Saffarpour, D. Basu, F. Radaei, K. Vali, J. Y. Adams, C-N. Chuah, and S. Ghiasi, "Physiowise: A Hybrid Approach to Dicrotic Notch Identification," ACM Transactions on Computing for Healthcare. [DOI: 10/1145/3578556]
  • M. Saffarpour, D. Basu, F. Radaei, K. Vali, J. Y. Adams, S. Ghiasi, and C-N. Chuah, "Dicrotic Notch Identification: A Generalizable Hybrid Approach under Arterial Blood Pressure (ABP) Curve Deformations," 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Oct 31-Nov 4, 2021. [DOI: 10/1145/3578556]
Thesis/Reports

  • Fatemeh Radaei, "Prediction of Fluid-Responsiveness in Patients at Intensive Care Unit Using Machine Learning Modeling," M.S. Plan-I Thesis, December 2020.

Funding

This project is supported by Department of Defense (DoD) CDMRP Grant# W81XWH1820072 (2018-21) titled “Endovascular Perfusion Augmentation for Critical Care (EPACC): Personalized and Adaptive Therapy for Resuscitation After Trauma."