Effects of Simulated Pathophysiology on the Performance of a Decision Support Medical Monitoring System for Early Detection of Hemodynamic Decompensation in Humans
Abstract
A high fraction of both battlefield and civilian trauma deaths are caused by hemorrhage and subsequent cardiovascular collapse. It is estimated that 85% of such deaths are potentially preventable with adequate detection and intervention. However, early detection of hemorrhage and proper intervention is confounded by physiological compensatory mechanisms that can keep blood pressure and heart rate in or near normal range during bloodloss of up to 30% of total blood volume. These mechanisms limit the ability of care providers to detect the imminent risk of life threatening cardiovascular collapse with traditional vital signs. In this context, machine learning algorithms developed by the U.S. Army Institute of Surgical Research, using hemorrhage simulated by lower body negative pressure, have shown significant promise in detecting subtle changes in vita l signs and estimating changes in cardiac output and blood volume. These tools are currently being validated via collaborative research between the Mayo Clinic Department of Anesthesiology and the U.S. Army Institute of Surgical Research along with several industry partners. In this context, the goal of this application is to extend the pre-clinical validation of the U.S. Army Institute of Surgical Research decision support algorithm for blood loss to incorporate simulated pathophysiological conditions likely to be encountered during combat casualty care. These conditions include: 1) mild hypoxia to simulate altitude or pulmonary/chest wall injury, 2) epinephrine infusions to simulate hyperdynamic circulatory response to stress, 3) blood loss and resuscitation with hypertonic saline, and 4) endotoxin administration to simulate the onset of sepsis.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2014
- Accession Number
- AD1017023
Entities
People
- Michael Joyner