scholarly journals Compensatory reserve versus traditional vital signs in the obstetric patient

2022 ◽  
Vol 226 (1) ◽  
pp. S219-S220
Author(s):  
Lakha Prasannan ◽  
Rachel P. Gerber ◽  
Weiwei Shan ◽  
Natalie Meirowitz ◽  
Adiel Fleischer
Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6413
Author(s):  
Victor A. Convertino ◽  
Steven G. Schauer ◽  
Erik K. Weitzel ◽  
Sylvain Cardin ◽  
Mark E. Stackle ◽  
...  

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.


2018 ◽  
Vol 104 (2) ◽  
pp. 120-123
Author(s):  
H J Pynn ◽  
J E Smith

AbstractPatients often compensate for physiological insults before demonstrating abnormalities in traditionally measured vital signs such as pulse and blood pressure. The Compensatory Reserve Index analyses arterial waveform and may detect early circulatory shock. This article reviews the current evidence exploring this technology and its potential applicability in the military environment.


2018 ◽  
Vol 34 (9) ◽  
pp. 696-706 ◽  
Author(s):  
Mithun R. Suresh ◽  
Kevin K. Chung ◽  
Alicia M. Schiller ◽  
Aaron B. Holley ◽  
Jeffrey T. Howard ◽  
...  

Hypovolemic shock exists as a spectrum, with its early stages characterized by subtle pathophysiologic tissue insults and its late stages defined by multi-system organ dysfunction. The importance of timely detection of shock is well known, as early interventions improve mortality, while delays render these same interventions ineffective. However, detection is limited by the monitors, parameters, and vital signs that are traditionally used in the intensive care unit (ICU). Many parameters change minimally during the early stages, and when they finally become abnormal, hypovolemic shock has already occurred. The compensatory reserve (CR) is a parameter that represents a new paradigm for assessing physiologic status, as it comprises the sum total of compensatory mechanisms that maintain adequate perfusion to vital organs during hypovolemia. When these mechanisms are overwhelmed, hemodynamic instability and circulatory collapse will follow. Previous studies involving CR measurements demonstrated their utility in detecting central blood volume loss before hemodynamic parameters and vital signs changed. Measurements of the CR have also been used in clinical studies involving patients with traumatic injuries or bleeding, and the results from these studies have been promising. Moreover, these measurements can be made at the bedside, and they provide a real-time assessment of hemodynamic stability. Given the need for rapid diagnostics when treating critically ill patients, CR measurements would complement parameters that are currently being used. Consequently, the purpose of this article is to introduce a conceptual framework where the CR represents a new approach to monitoring critically ill patients. Within this framework, we present evidence to support the notion that the use of the CR could potentially improve the outcomes of ICU patients by alerting intensivists to impending hypovolemic shock before its onset.


2018 ◽  
Vol 124 (2) ◽  
pp. 442-451 ◽  
Author(s):  
Victor A. Convertino ◽  
Michael N. Sawka

Traditional monitoring technologies fail to provide accurate or early indications of hypovolemia-mediated extremis because physiological systems (as measured by vital signs) effectively compensate until circulatory failure occurs. Hypovolemia is the most life-threatening physiological condition associated with circulatory shock in hemorrhage or sepsis, and it impairs one’s ability to sustain physical exertion during heat stress. This review focuses on the physiology underlying the development of a novel noninvasive wearable technology that allows for real-time evaluation of the cardiovascular system’s ability to compensate to hypovolemia, or its compensatory reserve, which provides an individualized estimate of impending circulatory collapse. Compensatory reserve is assessed by real-time changes (sampled millions of times per second) in specific features (hundreds of features) of arterial waveform analog signals that can be obtained from photoplethysmography using machine learning and feature extraction techniques. Extensive experimental evidence employing acute reductions in central blood volume (using lower-body negative pressure, blood withdrawal, heat stress, dehydration) demonstrate that compensatory reserve provides the best indicator for early and accurate assessment for compromises in blood pressure, tissue perfusion, and oxygenation in resting human subjects. Engineering challenges exist for the development of a ruggedized wearable system that can measure signals from multiple sites, improve signal-to-noise ratios, be customized for use in austere conditions (e.g., battlefield, patient transport), and be worn during strenuous physical activity.


2013 ◽  
Vol 115 (8) ◽  
pp. 1196-1202 ◽  
Author(s):  
Victor A. Convertino ◽  
Greg Grudic ◽  
Jane Mulligan ◽  
Steve Moulton

Trauma patients with “compensated” internal hemorrhage may not be identified with standard medical monitors until signs of shock appear, at which point it may be difficult or too late to pursue life-saving interventions. We tested the hypothesis that a novel machine-learning model called the compensatory reserve index (CRI) could differentiate tolerance to acute volume loss of individuals well in advance of changes in stroke volume (SV) or standard vital signs. Two hundred one healthy humans underwent progressive lower body negative pressure (LBNP) until the onset of hemodynamic instability (decompensation). Continuously measured photoplethysmogram signals were used to estimate SV and develop a model for estimating CRI. Validation of the CRI was tested on 101 subjects who were classified into two groups: low tolerance (LT; n = 33) and high tolerance (HT; n = 68) to LBNP (mean LBNP time: LT = 16.23 min vs. HT = 25.86 min). On an arbitrary scale of 1 to 0, the LT group CRI reached 0.6 at an average time of 5.27 ± 1.18 (95% confidence interval) min followed by 0.3 at 11.39 ± 1.14 min. In comparison, the HT group reached CRI of 0.6 at 7.62 ± 0.94 min followed by 0.3 at 15.35 ± 1.03 min. Changes in heart rate, blood pressure, and SV did not differentiate HT from LT groups. Machine modeling of the photoplethysmogram response to reduced central blood volume can accurately trend individual-specific progression to hemodynamic decompensation. These findings foretell early identification of blood loss, anticipating hemodynamic instability, and timely application of life-saving interventions.


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