Comprehensive Intensive Care Research (CICR)
Our research effort focuses on optimizing combat casualty care in the critical care environment. The military defines the critical care environment as any environment the wounded service member arrives where there is a fixed surgical facility capable of damage control medical repair and extensive monitoring. This environment can also include transportation from one critical care environment to another such as a helicopter or airplane. Our task area also has the capability to manufacturer FDA regulated medical devices.
- To capitalize on increased survival on the battlefield by improving the care of the complex, critically injured patient from the Role 2 (Forward Surgical) through Role 5 (Continental United States Medical Treatment Facility ICU.)
- Turn existing clinical data into actionable information to reduce mortality, complications, and maximize functional outcomes in the intensive care setting by researching and developing decision support technologies.
- Provide technology solutions in support of the SDAC (Sense, Decide, Act, Communicate) concept for patient care. New approaches for automation, documentation, processing, and sensing modalities that can be incorporated into the next generation of critical care solutions.
- Conduct laboratory research studies using new technologies and measuring their effectiveness in the care of the combat casualty.
CURRENT RESEARCH EFFORTS
- Development of decision support, automation, and closed loop technologies in the critical care environment.
- Development, Analysis, and Implementation of new Indices and vital signs for Enhanced Patient Status Prediction, Diagnosis and Treatment.
- Development of advanced computer algorithms using machine learning, artificial intelligence, and digital signal processing for enabling technologies (clinical decision support systems) to be deployed throughout the entire critical care spectrum.
- Comprehensive treatment of single-organ (e.g. lung, kidney) and multi-organ failure in combat casualties.
Liu NT, Batchinsky AI, Cancio LC, Baker WL, Salinas J. Development and validation of a novel fusion algorithm for continuous, accurate, and automated R-wave detection and calculation of signal-derived metrics. J Crit Care. 2013;28:885.e9-885.e18
Hinojosa-Laborde C, Shade RE, Muniz GW, Bauer C, Goei KA, Pidcoke HF, Chung KK, Cap AP, Convertino VA. Validation of lower body negative pressure as an experimental model of hemorrhage. J Appl Physiol. 2013 Dec 19.
Liu NT, Cancio LC, Salinas J, Batchinsky AI. Reliable real-time calculation of heart-rate complexity in critically ill patients using multiple noisy waveform sources. J Clin Monit Comput. 2014;28:123-131.
Liu NT, Holcomb JB, Wade CE, Batchinsky AI, Cancio LC, Darrah MI, Salinas J. Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients. Med Biol Eng Comput. 2014;52:193-203.