Measurement of compensatory reserve predicts racial differences in tolerance to simulated hemorrhage in women

Supervisory Algorithm for Autonomous Hemodynamic Management Systems

Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems.

Physiology of Human Hemorrhage and Compensation

Hemorrhage is a leading cause of death following traumatic injuries in the United States. Much of the previous work in assessing the physiology and pathophysiology underlying blood loss has focused on descriptive measures of hemodynamic responses such as blood pressure, cardiac output, stroke volume, heart rate, and vascular resistance as indicators of changes in organ perfusion. More recent work has shifted the focus toward understanding mechanisms of compensation for reduced systemic delivery and cellular utilization of oxygen as a more comprehensive approach to understanding the complex physiologic changes that occur following and during blood loss. In this article, we begin with applying dimensional analysis for comparison of animal models, and progress to descriptions of various physiological consequences of hemorrhage. We then introduce the complementary side of compensation by detailing the complexity and integration of various compensatory mechanisms that are activated from the initiation of hemorrhage and serve to maintain adequate vital organ perfusion and hemodynamic stability in the scenario of reduced systemic delivery of oxygen until the onset of hemodynamic decompensation. New data are introduced that challenge legacy concepts related to mechanisms that underlie baroreflex functions and provide novel insights into the measurement of the integrated response of compensation to central hypovolemia known as the compensatory reserve. The impact of demographic and environmental factors on tolerance to hemorrhage is also reviewed. Finally, we describe how understanding the physiology of compensation can be translated to applications for early assessment of the clinical status and accurate triage of hypovolemic and hypotensive patients. © 2021 American Physiological Society. Compr Physiol 11:1531-1574, 2021.

Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.