In similar experiments, blood lactate and mental status did not change until ∼50% of central blood volume (equivalent to ∼1.0 L total blood volume over a period of 20 min or 50 mL/min) of central blood volume was reduced (42). The data presented in Figures 1 and 2 support the notion that our current vital sign monitors are not very “smart” because they have been designed to measure clinical outcome variables that are maintained by the body's mechanisms to compensate for blood loss during the initial phase of hemorrhage. To improve the intelligence of such decision-support tools, we need technology that can measure compromise to integrated blood pressure compensation mechanisms (43) and “individualize” the assessment of a patient's progression toward “shock” well in advance of clinically important changes in traditional vital signs (44). This approach requires the capability to measure the integrated sum total of all mechanisms that compose the reserve to compensate for blood loss. We call this physiological measurement the compensatory reserve.
The compensatory reserve represents a new paradigm for measuring the sum total of all compensatory mechanisms (e.g., tachycardia, vasoconstriction, breathing) that together contribute to “protect” against inadequate tissue perfusion during blood loss and other low circulating blood volume states (29, 32, 42, 45, 46). A conceptual model of the compensatory reserve is presented in Figure 3(46).
When there exists complete capacity for compensating for a relative loss of circulating blood volume, an individual has 100% of their compensatory reserve (green arrow in Fig. 3). Relative blood volume refers to the amount of circulating blood available to fill the available vascular space. For example, a relative blood volume deficit develops in the setting of peripheral vasodilation without the loss of blood (e.g., heat exposure) because the vascular space has been expanded. As relative circulating blood volume is reduced in time (x-axis) due to conditions such as hemorrhage or dehydration (i.e., progressively moving along the blood volume deficit red line), compensatory mechanisms are recruited to maintain adequate oxygen to vital organs. The time scale for the rate of “relative blood volume deficit” will depend on the severity of hemorrhage. If the relative blood volume deficit continues to a state where maximum compensation is reached (i.e., the circle in Fig. 3 where the blood volume deficit “red” line intersects with the “zero” compensatory reserve “blue” line), decompensatory shock will occur. Our research has revealed that the compensatory reserve is reduced from the onset of blood loss as compensation is initiated. In this regard, the earliest and most accurate indication of hemorrhage is reflected by measuring the compensatory reserve.
The development of a dynamic technology that allows for accurate real-time assessment of the compensatory reserve required five critical components: a model for the study of the physiology of human hemorrhage; a model with a reproducible clinical outcome (hemodynamic decompensation); identification of signals that reflect compensatory mechanisms; development of a large data “library” of signals; and development of an algorithm to recognize INDIVIDUAL patients.
To identify clinical responses that represent the compensatory reserve, a model was developed that allowed us to study the physiology of human hemorrhage. This model is known as lower body negative pressure (LBNP) and is demonstrated in Figure 4(27, 47). The lower body of a subject is placed in a chamber that is sealed air tight at the waist by a neoprene skirt (Fig. 4A). The pressure in the chamber can be precisely controlled at negative pressures in a stepwise fashion (Fig. 4B). The increasing negative pressure causes a progressive redistribution of blood away from the upper body circulation (e.g., heart and brain) to the lower body below the waist. We have demonstrated that hemodynamic, neuroendocrine, metabolic, and coagulation responses to LBNP are similar to those of actual hemorrhage in humans (48, 49) and nonhuman primates (50, 51).
A unique and important feature to the LBNP model is its ability to provide a reproducible clinical outcome. The continuous LBNP profile allows us to take each subject to a state of intolerance to central blood loss (Fig. 4C) and subsequently the point of decompensation (i.e., depletion of the compensatory reserve) that replicates the terminal cardiovascular collapse observed in critically ill patients (52). This tolerance point is extremely reproducible in experiments repeated in the same individuals over both acute (< 1 h) and chronic (> 1 year) time periods (53, 54). As such, this model has allowed for development of an algorithm that can accurately predict the end of compensation, i.e., the initial decompensatory phase of Class III shock as a specific clinical outcome (32, 33, 46). The use of this experimental approach provides a method to study the physiology of human hemorrhage and subsequently overcomes the challenge of attempting to interpret outcomes in clinical settings where significant inter-patient variability is amplified by uncontrolled factors that may contribute to the final clinical outcome.
The ability to determine the tolerance of each individual subject revealed the diversity of compensation. Our experiments allowed us to recognize and classify two distinct populations (32, 42, 53, 55, 56)—those individuals with high tolerance (completion of the 60-mm Hg level of the LBNP protocol presented in Fig. 4) to reduced central blood volume (i.e., good compensators) and those with low tolerance (poor compensators who failed to complete the 60 mmHg level of the LBNP protocol). This experimental observation corroborates the reporting of survivors and non-survivors from clinical studies of patients who reach hemodynamic decompensation in a shorter amount of time when compared with patients with similar rates of blood loss (23, 57). Perhaps the most important deficiency in the clinical literature is the failure to consider these diverse physiologies when assessing or developing clinical algorithms. This is best illustrated in Figure 5 with the comparison of shock index (SI) in two groups of humans exposed to progressive reduction in central blood volume.
The SI is defined as the ratio of the heart rate to systolic blood pressure (HR/SBP) that has been advocated as a “new” vital sign (58, 59). In its evolution, an SI > 0.9 was proposed as an indicator of impending hemorrhagic shock (58, 60, 61). Indeed, there is compelling evidence suggesting that SI is more sensitive than either HR or SBP alone as an early predictor of hemorrhage (60, 62, 63), massive transfusion (64), morbidity (58), and mortality in geriatric patients (59, 61). Based on the SI threshold of 0.9, doctrine would indicate that Group 2 would be considered at higher risk for developing shock than Group 1. However, this would be an incorrect assessment since Group 2 actually represented individuals with high tolerance to reduced central blood volume compared with the low tolerant Group 1. In experimentally controlled human experiments, SI was found to respond late to progressive reductions in central blood volume during simulated hemorrhage (30). This example (among many others) emphasizes the importance of developing an assessment technology that can distinguish the compensatory capability of each individual patient to provide the most sensitive and specific indicator(s) of shock.
Our LBNP experiments provided the opportunity to obtain a comprehensive set of physiological measurements (e.g., blood pressures, heart rate, heart rate variability and complexity, SpO2, stroke volume, vascular resistance, respiration rate, pulse character, mental status, end-tidal CO2, blood lactate, tissue oxygen, sympathetic nerve activity, etc.) during continuous progressive reductions in central blood volume similar to those that occur during hemorrhage. These standard measures either changed very late in the process of reduced central blood volume (Table 1), displayed low specificity with predicting decompensation (Table 1), or failed to distinguish individual tolerances (32, 56). Against expectations, we found that specific patterns of blood pressure oscillations were the most predictive physiological measures for predicting how tolerant individuals were to reductions in central blood volume (42, 56, 65). This relationship is best illustrated in the two tracings presented in Figure 6 that were obtained in our laboratory from two healthy humans during their exposure to progressive reductions in central blood volume (65).
Since the waveform features created by blood being ejected from the heart represent the systolic and diastolic pressures, it may not be clear why simple measurement of blood pressure fails to provide an accurate assessment of blood loss. The answer is best illustrated in the two tracings presented in Figure 8. The vertical red broken lines represent the points in time where Korotkoff sounds appear (systolic pressure, line “a”) and disappear (diastolic pressure, line “b”). The upper tracing reflects human arterial waveforms recorded in the supine posture over a period of approximately 1 min. The top horizontal broken line in the upper tracing intersects the appearance of Korotkoff sounds (vertical line “a”) at a systolic pressure of 116 mmHg. The bottom horizontal broken line in the upper tracing intersects the disappearance of Korotkoff sounds (vertical line “b”), at a diastolic pressure of 78 mmHg. The recording presented in the lower tracing of Figure 8 was taken from the same individual as that recorded in the upper record, but during a reduction in central blood volume >50%. Compared with the top tracing, the bottom tracing displays significant changes in arterial waveform features (i.e., pulse pressure) that include oscillations that fluctuate between blood pressures of 110/90 and 90/70. However, the lower tracing of Figure 8 also demonstrates that the use of traditional sphygomanometry with appearance and disappearance of Korotkoff sounds at the same points in time as those in the upper tracing (broken vertical lines “a” and “b”) could yield the same blood pressure (116/78 mmHg) as that of a person with “normal” central blood volume. Comparison of these tracings demonstrates that there is additional information that can be extracted from continuous measurement of arterial waveforms (i.e., morphology, oscillations) that can be used to increase specificity in the clinical assessment of a bleeding patient. Since oscillations in blood pressure or flow waveforms are associated with tolerance to central blood volume loss (42, 45, 56, 65), it is not unexpected that waveform feature analysis has provided a clinical tool for distinguishing poor from good compensators.
All compensatory mechanisms that impact cardiac output (e.g., autonomic nerve activity, cardiac filling, respiration, cardiac medications, etc.) are contained within features of the ejected wave, while all compensatory mechanisms that affect vascular resistance (e.g., sympathetic nerve activity, circulating catecholamines, arterial pH or CO2, arterial elasticity, muscle contractions, etc.) are represented by features of the reflected wave. Thus, a monitoring technology that will provide the most sensitive and specific assessment of the sum total of all mechanisms of compensation for an individual patient will include continuous measurements of changes in features of both ejected and reflected waves, to include oscillatory patterns.
The recognition that indicators of blood loss can be linked to characteristic features of the photoplethysmogram (PPG) waveform is not new. The fundamental premise that systolic pressure represents functional output of the heart and diastolic pressure reflects arteriolar tone (68) provides the basis that simple pulse pressure represents a gross estimate of integrated compensatory mechanisms that control cardiac output and peripheral perfusion. As such, the PPG has been proposed for use as a noninvasive tool for estimating stroke volume and cardiac output (14, 18, 69–71), and subsequently track reductions in circulating blood volume as myocardial filling are reduced during hemorrhage. Similarly, measures of dynamic physiological responses such as pulse pressure variability (4, 72–75) and heart rate complexity (17) have been promoted as potential indicators of cardiac output in patients or animals with circulating blood volume compromise resulting from acute blood loss. Although proven to be responsive to reductions in central blood volume (i.e., sensitivity), the primary limitation of all these techniques is that they are based on population averages from clinical trials and fail to account for multiple features that construct each waveform obtained from individual patients. As such, the relatively poor specificity produced by these population-based algorithms fails to distinguish individuals with differing levels of tolerance to blood loss and subsequently cannot provide early recognition of hemodynamic decompensation due to progressive reduction in central blood volume in those patients at greatest risk for developing hemorrhagic shock (33).
Development of a model capable of “learning” the circulating blood volume status of any specific individual patient requires a large database of physiological signals from a broad spectrum of individual humans with varying demographics (e.g., sex, age) exposed to progressive reductions in central blood volume. In this regard, arterial waveforms obtained from more than 260 healthy men and women ranging in age from 18 to 55 years of age during supine rest and progressive reduction in central blood volume using carefully controlled LBNP protocols over the past 12 years have been collected and archived at the U.S. Army Institute of Surgical Research (32). This experimentation has provided the largest and most unique known data set with hundreds of thousands of arterial waveforms from individuals with both high and low tolerance to central hypovolemia (represented in Fig. 9 as the “Algorithm Waveform Library”).
In collaboration with robotics engineers originally from the University of Colorado, high-speed computer techniques that combined measurement of feature changes in the entirety of each arterial waveform (Fig. 7, red lines) with machine-learning mathematics were used to develop a set of mathematical algorithms that provided the most accurate index of compensatory reserve. This measurement tool has been termed the Compensatory Reserve Index (CRI) (32, 46). The algorithms are capable of processing 100 million data points per second, ‘finding’ hundreds of specific features that enable trending from normovolemia to the time of decompensation. Each continuous noninvasive PPG waveform (represented in Fig. 9A as the monitored “Patient's Arterial Waveform”) is the input to calculate an estimate of an individual's compensatory reserve (represented in Fig. 9C as the “CRI Estimate”) based on comparison to the large “library” of reference waveforms (represented in Fig. 9B as the “Algorithm Waveform Library”) generated from progressive levels of central hypovolemia (32).
The CRI is normalized on a simplified scale of 1 to 0, where “1” represents normovolemia in the supine position (i.e., maximum capacity for compensation) and “0” represents the circulating blood volume at which hemodynamic decompensation (i.e., initial decompensatory phase of shock) occurs. Values between “1” and “0” indicate the proportion of compensatory reserve remaining. The algorithm produces the first CRI value after 30 heartbeats of initialization and, then, in real time, provides a new CRI value after every subsequent heartbeat. The CRI monitor screen has been constructed to include the visualization of a “bar” similar to a fuel gauge of a car that indicates the amount of fuel that is left in the tank (Fig. 10) with three colors that correspond to patient status of adequate compensation (green), moderately compromised (amber), and unstable or emergent (red). Accuracy analysis demonstrates a correlation ≥0.95 for estimating compensatory reserve at any point in time, and of “0” at which an individual will experience hemodynamic decompensation (32, 46).
Thus, the algorithm for measuring compensatory reserve functions on the basis that alterations in circulating blood volume status over some period of time are more important than the population average at a single point, because the CRI is evaluating subtle changes in every waveform. The algorithm output (Fig. 9C) reflects the integration of all physiological compensatory mechanisms, and the changes in their status and features in response to physiologic stressors (e.g., hemorrhage) that give a unique individual-specific predictive capability to assess one's capacity (reserve) to compensate. Each individual's compensatory reserve (e.g., a traffic accident patient with trauma and unknown blood loss due to undiagnosed injuries) is correctly estimated in real time because the machine-learning capability of the algorithm accounts for compromised circulating blood volume as it “learns” and “normalizes” the totality of compensatory mechanisms based on the individual's arterial waveform features. The algorithm recognizes that each individual patient starts with a baseline CRI near 1 that reflects a normal hydrated physiological state. Therefore, while an individual may have a reduced ability to compensate due to lower circulating blood volume (i.e., hemorrhage in the case of the traffic accident patient), the baseline physiologic measure will take this into consideration based on what the algorithm has “learned” from previous human responses to progressive central hypovolemia (i.e., the process demonstrated in Fig. 9).
Another clinical investigation was designed to evaluate pediatric patients with Dengue hemorrhagic fever (DHF) as a model of internal hemorrhage. The results from a 7-year-old patient are presented in Figure 12 (unpublished observation). At the time of hospital admission, the compensatory reserve registered at only 10% (CRI = 0.1), indicating that the patient was near decompensatory shock. It is clear that resuscitative therapy was affective in restoring the compensatory reserve to >40% by day 3 and nearly 80% by day 5. The response of this patient is typical of numerous subsequent DHF cases that have been completed with monitoring of the compensatory reserve. These results hold the clinically important implication that the technology developed for measurement of the compensatory reserve from data collected on adults appeared to be equally sensitive and specific for children, indicating that identification of specific features of the arterial waveform appears to be age independent. As such, these results support the notion that measurement of the compensatory reserve is applicable to children as well as to adults.
In another recent investigation, the compensatory reserve was measured in humans who underwent a controlled hemorrhage of 20% of their blood volume (29). The blood was subsequently returned by transfusion. The results recorded from two subjects are presented in Figure 13. Measuring the compensatory reserve proved to be the most robust marker of individual variation between subjects. Several unique features of measuring the compensatory reserve are demonstrated by the data presented in Figure 2. First, the significant correlation of reduced compensatory reserve with the volume of blood loss in the two subjects indicates the ability of the algorithm to provide individualized assessment of compensatory status. Second, the difference in slopes of the relationship between blood volume and compensatory reserve represents the individual reserve required to compensate for bleeding. That is, the CRI was able to distinguish subject 1 (Fig. 13, left panel) as the most compromised individual requiring use of ∼70% of compensatory reserve after losing 1.2 L of blood compared with subject 2 (Fig. 13, right panel) who lost more blood (∼1.4 L) but only required use of ∼30% of compensatory reserve. In this regard, measurement of the compensatory reserve displayed significantly greater specificity for measuring the status of circulating blood volume in INDIVIDUAL patients compared with any standard vital sign, including stroke volume and cardiac output (29). Third, since subject 1 required more of his reserve to compensate for less blood loss than subject 2, the comparison of results in Figure 13 reinforces the notion that measurement of the compensatory response to hemorrhage is a more sensitive and specific predictor of shock than blood volume loss. Finally, the complete restoration of compensatory reserve following replacement of circulating volume in these individuals (Fig. 13, open circles), as well as the patient with DHF (Fig. 12), suggests that measurement of the compensatory reserve could provide a monitoring technology for accurate goal-directed fluid resuscitation.
The compensatory reserve represents the most sensitive and specific measure of clinical status in a patient with blood volume deficit such as ongoing hemorrhage. It can be assessed with advanced computer capabilities that involve continuous, real-time measurement of arterial waveform features using robotics machine-learning algorithms. Algorithm development included prediction of hemodynamic decompensation (i.e., early indication for onset of decompensatory phase of hemorrhagic shock) based on the concept of depleted compensatory reserve (i.e., zero reserve). The algorithms for measurement of compensatory reserve can be integrated on current medical monitors or on a simple pulse oximeter, and displayed in a simple color scheme fuel tank format (Fig. 14), providing an easily used and understood form factor for combat as well as civilian medics. Measurement of the compensatory reserve provides an early marker of clinical status in a bleeding patient well in advance of changes in standard vital signs and subsequently can buy more time for effective intervention. Because of its effectiveness in assessing relative circulating blood volume, goal-directed resuscitation may be accurately assessed by adequate restoration of the measured compensatory reserve.
Execution of several clinical research studies will prove critical to the continued development and acceptance by the medical community of the measurement of compensatory reserve as a standard of care in patients. To support this objective, data are being collected from ongoing investigations with several collaborators under IRB-approved protocols in patients with pathophysiological conditions of reduced circulating blood volume that include Dengue hemorrhagic fever, trauma, chronic orthostatic hypotension, burn injury, and sepsis, or those undergoing voluntary blood donation, controlled hemorrhage, renal dialysis, or cardiopulmonary resuscitation (77). Perhaps more specifically to addressing the needs of military medical prehospital care is the ongoing data collection during air ambulance transport of battlefield casualties by the Israeli Defense Force. Finally, and most importantly, the comparison of any given arterial waveform to a “library” of thousands of waveforms obtained from many humans at varying states of central blood volume provides the CRI algorithms a capability to “learn” to recognize specific individual clinical conditions. However, waiting for further clinical data does not detract from the strong evidence that the most sensitive and specific metric of patient status during hemorrhage is represented by continuous, real-time measurement of arterial waveform features that reflect the total integration of mechanisms that represent the reserve to compensate for blood loss. In this regard, measurement of the compensatory reserve could someday become an effective diagnostic tool for goal-directed interventions. These characteristics based on integrated physiology make measurement of the compensatory reserve the first and only diagnostic approach to assess and identify those patients who are at greatest risk for developing shock from the point of injury across all levels of care.
The authors thank the scientists and engineers at the University of Colorado who developed the CRI technology that allowed for measurement of the compensatory reserve; and Ms Mariam Calderon and Ms Jessie Fernandez for their assistance in the figure and editorial preparation of this manuscript.
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