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Clinical Aspects

Specificity of Compensatory Reserve and Tissue Oxygenation as Early Predictors of Tolerance to Progressive Reductions in Central Blood Volume

Howard, Jeffrey T.; Janak, Jud C.; Hinojosa-Laborde, Carmen; Convertino, Victor A.

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doi: 10.1097/SHK.0000000000000632
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Abstract

INTRODUCTION

Circulatory shock resulting from acute uncontrolled hemorrhage remains a leading cause of death in both military (1) and civilian trauma (2). Accurate assessment of patient status may be delayed because of cardiovascular compensatory mechanisms that prevent early changes in primary indicators of shock such as severe hypotension (3). Vasoconstriction is one such mechanism that can be an early compensatory event, resulting in reduced peripheral tissue oxygenation (4, 5) and alterations in features of the arterial waveform (6). We previously reported that noninvasive measurements of deep muscle oxygen saturation (SmO2) and compensatory reserve based on changes in arterial waveform features using a specifically computed machine-learning based compensatory reserve index (CRI) provide earlier indications of central blood volume loss compared with standard vital sign measurements (6–12). However, like any clinical measurement, for either SmO2 or CRI to prove effective for real-time monitoring and identification of patients at risk for hemodynamic instability resulting from hemorrhage, they must demonstrate both high sensitivity and specificity when applied to individual patients.

The inability to compensate for acute central hypovolemia (e.g., progressive hemorrhage) underlies the rapid onset of hypotension and a potentially inadequate cerebral perfusion that can lead to life-threatening hemodynamic instability (e.g., circulatory shock). One prominent observation is the existence of a subset of individuals who have relatively low tolerance to reduced central blood volume (6, 13–16). It is critical to assess physiological measures that can provide identification of individual patients with low tolerance to hemorrhage because they are inherently at highest risk for earlier onset of shock. The difference in individual tolerance to experimentally induced reductions in central blood volume can serve as a model to assess the sensitivity and specificity of measurements such as SmO2 and compensatory reserve. We have previously shown that the compensatory reserve measured by the CRI has greater specificity when used to monitor individual patients compared with traditional vital signs (e.g., blood pressure, heart rate, arterial oxygen saturation) and stroke volume during actual or simulated hemorrhage in humans (7, 8). Recently, increased attention has been given to the measurement of SmO2 as a prognostic tool for early assessment of the status of patients with progressive hemorrhage (12, 17, 18). More specifically for the application to military medicine, the Committee on Tactical Combat Casualty Care is considering technology for the measurement of SmO2 and compensatory reserve as a medic tool to guide resuscitation (19). In the present experiment, we conducted a head-to-head comparison of technologies during controlled simulated hemorrhage in humans to test the hypothesis that measurement of the compensatory reserve would provide greater sensitivity and/or specificity to predict the onset of hemodynamic decompensation compared with SmO2.

PATIENTS AND METHODS

Subjects and ethical approval

Fifty-five healthy non-smoking normotensive men (n = 26) and women (n = 29) with a mean (STD) age of 28 ± 7 years, height 172 ± 10 cm, and weight 72 ± 15 kg volunteered to participate as subjects for this investigation. These subjects are a subset of a larger group of subjects who underwent various lower body negative pressure (LBNP) experiments, which have been used in other published studies (7). The data used in this study were selected from the subjects who participated in specific protocols that included the measures of SmO2 and CRI during progressive LBNP-induced hypovolemia. Subjects received a verbal and written briefing of all procedures and risks associated with the study and were made familiar with the laboratory, the protocol, and the instrumentation. Subjects were encouraged to ask questions of the investigators before giving their written informed consent to participate. All experimental procedures were conducted in accordance with a protocol reviewed and approved by the Institutional Review Board of the Office of Human Research Protection under the US Army Medical Research and Materiel Command. All subjects underwent a medical history and physical examination by a physician to ensure that they had no previous or current medical conditions that might preclude their participation. In addition, female subjects underwent a urine test within an hour before the experiment to ensure that they were not pregnant. Subjects were instructed to maintain their normal sleep patterns, refrain from exercise, and abstain from caffeine and other autonomic stimulants including nonprescription drugs for at least 24 h prior to each experiment.

Experimental protocol

We used a one-group, repeated measures experimental design to compare the discriminative ability of SmO2 and compensatory reserve in predicting hemodynamic decompensation. Each subject reported to the laboratory for preparation to undergo progressive stepwise reductions in central blood volume induced by application of LBNP to simulate the hemodynamic challenges associated with severe hemorrhage (20, 21). Subjects assumed the supine position within an airtight chamber and were sealed at the level of the iliac crest by way of a neoprene skirt. The LBNP protocol consisted of a 5-min baseline period followed by 5 min of chamber decompression to −15, −30, −45, and −60 mm Hg, and additional increments of −10 mm Hg every 5 min until either the onset of hemodynamic decompensation or the completion of 5 min at −100 mm Hg.

Measurement of decompensation

The attending investigator closely monitored each subject in real time to determine the onset of hemodynamic decompensation, identified by a precipitous fall in systolic arterial pressure (SBP) below 80 mm Hg (class III shock) concurrent with presyncopal symptoms such as bradycardia, gray-out (loss of color vision), tunnel vision, sweating, nausea, or dizziness. No subject completed 5 min at −100 mm Hg, and all subjects expressed one or more subjective presyncopal symptoms. At the onset of cardiovascular collapse, the chamber vacuum was immediately released to ambient pressure to rapidly restore blood flow to the central compartment. To assure subject safety, an ACLS-certified caregiver was present in the laboratory during all LBNP tests. At each LBNP level each subject was evaluated in the manner described above, and was measured as having decompensated or not each level. The binary outcome of decompensated or not decompensated at each level of the experiment was used as the dependent variable in the subsequent analyses to assess the predictive properties of CRI and SmO2.

Hemodynamic measurements

Continuous heart rate was measured from a standard electrocardiogram. Beat-by-beat SBP and diastolic blood pressure were measured noninvasively using an infrared finger photoplethysmograph (Finometer Blood Pressure Monitor, TNO-TPD Biomedical Instrumentation, Amsterdam, The Netherlands). The Finometer blood pressure cuff was placed on the middle finger of the left hand which, in turn, was laid at heart level. Excellent estimates of directly measured intra-arterial pressures during various physiological maneuvers have been demonstrated with this device (22, 23). Analog signals of arterial pressure waveforms were obtained from the Finometer blood pressure cuff. Hemodynamic data were sampled at 500 Hz and recorded directly to data acquisition software (WINDAQ, Dataq Instruments, Akron, Ohio). Analysis of data was subsequently accomplished using commercially available analysis software (WinCPRS, Absolute Aliens, Turku, Finland).

Muscle oxygen saturation

As described previously in detail (10–12), SmO2 was measured using a novel near-infrared spectroscopy (NIRS) sensor (Reflectance Medical Inc, Worcester, Mass) placed on the skin over the flexor digitorum profundus (forearm muscle) that contained two fiber optic bundles for illumination and one bundle for detection. The illumination and detector bundles were designed with mathematical processing that removed the light reflected from the skin and fat, leaving only the absorbance spectrum of muscle (24). Concentrations of oxygenated (HbO2) and deoxygenated hemoglobin (Hb) were estimated from Taylor expansion attenuation model based upon Beer law. Using this approach, SmO2 is calculated as the ratio of HbO2 to total hemoglobin (HbT = HbO2 + Hb), where HbT is considered the amount of hemoglobin in the volume of tissue sampled by the NIRS sensor. Spectra were collected approximately every 20 s and the average values over the last 3 min of baseline and each LBNP level were calculated.

A machine-learning framework for estimating compensatory reserve

As detailed previously (7, 25), state-of-the-art feature-extraction and machine-learning techniques were used to collectively process analog arterial waveform signals obtained from the Finometer blood pressure cuff during LBNP experiments. The algorithm for measurement of compensatory reserve provides an index value that estimates the remaining proportion of physiological reserve available to compensate for changes in effective circulating blood volume by comparing waveforms over a 30-heartbeat window to reference waveforms obtained from humans during progressive central hypovolemia induced by the LBNP protocol (7). The estimated index value, called the compensatory reserve index (CRI), corresponds to the value of the most similar reference waveform in the set of waveforms used to develop the machine-learning model.

For clinical simplicity, the CRI has been normalized on a scale of 1 to 0 (100% to 0%), where “1” reflected the maximum capacity of physiological mechanisms (e.g., baroreflexes, respiration) to compensate for reduced central blood volume, and “0” implied imminent hemodynamic decompensation. Values between “1” and “0” indicated the proportion of compensatory reserve remaining. In concept CRI is the following quantity:

where

  • BLV is the current blood loss volume of the patient.
  • BLVHDD is the blood loss volume at which hemodynamic decompensation occurs.

Statistical methods

To account for correlations between repeated measures in the study design, least squared (LS) means are reported for descriptive comparison of SmO2 and compensatory reserve values. SmO2 and CRI LS means with 95% confidence intervals were calculated for the total sample and for each of the LBNP levels (baseline to −90 mm Hg). LS means for SmO2 and CRI stratified by tolerance level (i.e., HT vs. LT) were also calculated.

To test the hypothesis that the measure of compensatory reserve would exhibit greater ability to predict decompensation, generalized estimating equations (GEE) with logit link functions and compound symmetry covariance structures were used to perform repeated measures logistic regression analysis. There were two GEE models performed in this study which regressed hemodynamic decompensation on SmO2 and compensatory reserve, respectively. Based on predicted probabilities from the GEE models, receiver operating characteristic area under the curve (ROC AUC), sensitivity, and specificity were used to evaluate the ability of both measures to predict hemodynamic decompensation at each time point in the progressive LBNP simulated hemorrhage experiment. The predicted probabilities of hemodynamic decompensation for both SmO2 and compensatory reserve were reported by LBNP levels (baseline to −90 mm Hg). Statistical significance for comparison of ROC AUC, sensitivity, and specificity was set at α = 0.05. Additionally, generalized linear mixed models (GLLM) were used to test that SmO2 and compensatory reserve scores decreased in response to the progressive changes in LBNP levels. When stratified by individual tolerance level (HT vs. LT), SmO2 and CRI scores were higher for HT compared with LT participants. To account for multiple comparisons for least square means difference tests from the GLLM procedures, statistical significance was set at α = 0.006 for comparisons across all eight levels of LBNP, and α = 0.0125 for comparisons between LT and HT individuals from baseline to −45 mm Hg. All analyses were performed using SAS v9.2 (Cary, NC).

RESULTS

As reported previously in these subjects (26), blood pressure and heart rate did not display statistical changes until the 60-mm Hg level of LBNP compared with reductions in SmO2 and CRI during exposure to the initial 15-mm Hg LBNP level. Results of ROC analysis indicate that CRI (0.91) had a significantly higher ROCAUC value than SmO2 (0.68), suggesting that measurement of compensatory reserve is superior to SmO2 in its ability to predict hemodynamic decompensation (Table 1 and Fig. 1; both P ≤0.0001). This difference in predictive power is attributable to the fact that CRI has both higher sensitivity (0.87 vs. 0.65; P = 0.003) and specificity (0.80 vs. 0.63; P = 0.015) than SmO2 (Table 1). Furthermore, sequential repeated measures of SmO2 exhibited less response across successive LBNP levels than CRI (Fig. 2). For example, mean values of SmO2 decreased by only 13% from baseline to −45 mm Hg (P = 0.0007), and 39% from baseline to −90 mm Hg (P ≤ 0.0001). In contrast, CRI demonstrated a greater response to successive LBNP levels, decreasing by 60% from baseline to −45 mm Hg (P ≤ 0.0001) and 97% from baseline to −90 mm Hg (P ≤ 0.0001).

Table 1
Table 1:
Vital signs during lower body negative pressure hemorrhage simulation (n = 55)
Fig. 1
Fig. 1:
Receiver operating characteristic area under the curves and 95% confidence intervals predicting hemodynamic decompensation for the CRI.CRI indicates compensatory reserve index; SmO2: muscle oxygen saturation.
Fig. 2
Fig. 2:
Least squares means and 95% confidence intervals of participants’ vital signs during lower body negative pressure progressive hemorrhage simulation.(Left axis) CRI indicates compensatory reserve index; (right axis) SmO2, muscle oxygen saturation.

CRI-based predicted probabilities of decompensation derived from logistic regression models were more responsive to successive LBNP levels than SmO2-based probabilities. More specifically, the logistic regression model using SmO2 as the predictor showed little change in the probability of decompensation from baseline to −90 mm Hg (Fig. 3), increasing by only 8% from baseline to −45 mm Hg (0.12 vs. 0.13; P ≤ 0.01; non-significant) and 17% from baseline to −90 mm Hg (0.12 vs. 0.14; P ≤ 0.001). However, the logistic regression model using CRI as the predictor generated predicted probabilities that increased by approximately 110% from baseline to −45 mm Hg (<0.01 vs. 0.12; P ≤ 0.0001) and approximately 570% from baseline to −90 mm Hg (<0.01 vs. 0.58; P ≤ 0.0001). Last, the predicted probability of decompensation using the CRI remained less than 0.05 during the early stepwise chamber decompression levels (baseline to −30 mm Hg), and then steadily increased from 0.11 to 0.58 from −45 mm Hg throughout the remainder of the LBNP protocol (P ≤ 0.0001).

Fig. 3
Fig. 3:
Predicted probabilities of decompensation (least squares means and 95% confidence intervals) from baseline to −90 mm Hg.CRI indicates compensatory reserve index; SmO2, muscle oxygen saturation.

DISCUSSION

With the use of a physiological model designed to distinguish human subjects with varying levels of tolerance to hemorrhage, as measured by repeated measures of hemodynamic decompensation, we tested the hypothesis that lower tolerance to progressive reductions in central blood volume would be associated with earlier reductions in compensatory reserve compared to SmO2. The results from our study support the hypothesis that the measurement of compensatory reserve provides a predictor of hemodynamic decompensation with greater sensitivity and specificity, i.e., higher ROC AUC, compared with SmO2. The superiority of CRI for assessment of both sensitivity and specificity suggests that measurement of the compensatory reserve possesses unique characteristics that accurately distinguish between individuals with higher and lower reserves for compensation during progressive central hypovolemia that could not be differentiated with the measurement of SmO2.

The statistically lower ROC AUC observed with the measurement of SmO2 is not unexpected given the fact that tissue oxygen reflects only one peripheral (local) outcome caused by hemorrhage. Despite statistically lower SmO2 at hemodynamic decompensation compared with baseline, the relatively low values for sensitivity and specificity in SmO2 are partly reflected by a range of ROC AUC values at the point of hemodynamic decompensation that are within the total range of baseline values (Table 1). Perhaps as important to low sensitivity and specificity in SmO2 is the inherent assumption of homogeneity across all tissues represented by a sensor that is placed on a single small area of muscle. In contrast, the sum total of mechanisms involved in compensation to blood loss can be measured by features of the arterial waveform that will involve changes in both total vascular resistance (reflective wave) and cardiac output (ejective wave) (6). Not only do changes in reflective waveform features represent the impact of endocrine- and sympathetically mediated vascular tone as well as oxygen delivery and utilization at the local tissue, but changes in ejective waveform features reflect integration of mechanisms underlying chronotropic and inotropic responses that control the heart. Since individuals with high tolerance to reduced central blood volume display higher heart rate (cardiac), sympathetic nerve activity, and peripheral vascular resistance (13), a greater sensitivity and specificity of compensatory reserve reflects algorithms that were developed with the recognition of individual responses of integrated cardiovascular mechanisms.

Results from three recent investigations using controlled hemorrhage models in human studies show that measurement of the compensatory reserve has greater discriminative ability to predict the impact of blood loss compared with traditional vital signs (8, 27, 28). The results of the present investigation are consistent with these previous findings, and provide additional evidence that compensatory reserve offers a superior method for reflecting cardiovascular status than other measures. Whether compared with traditional vital signs or a newer metric such as SmO2, our results indicate that greater sensitivity and specificity produced by arterial waveform feature analysis algorithms appears to be the result of an ability to “learn” and differentiate higher tolerant from lower tolerant individuals. For example, based on SmO2, the predicted probability of hemodynamic decompensation failed to detect a change during progressive reductions in central blood volume (Fig. 3). In contrast, the predicted probability of hemodynamic decompensation consistently increased from baseline to later stages of high level LBNP based on the measurement of compensatory reserve (Fig. 3), indicating a more robust response to hemodynamic changes associated with progressive decreases of central blood volume.

Our experimental approach also provided the opportunity to test the hypothesis that cardiovascular decompensation to progressive central hypovolemia occurs because humans reach intolerable reductions in SmO2 and compensatory reserve. Indeed, both the compensatory reserve and SmO2 identified early and progressive reductions in central blood volume induced by LBNP. Our results supported this hypothesis for the compensatory reserve since CRI measurement distinguished between decompensating individuals throughout the duration of the experiment by demonstrating an earlier response to depletion of reserve (i.e., CRI approaching zero), compared with SmO2. Our data demonstrated that measurements of SmO2 failed to predict decompensation because of similarities in the trajectory (slope) and maximum magnitude of reduced SmO2 between individuals. In this regard, the use of a low SmO2 as an indicator of compromised cardiovascular stability may lead to errors in triage. For example, patients with a large reserve to compensate for blood loss may have misclassified risks for developing shock based on SmO2 because this measure failed to provide any indication of closeness to decompensation over progressive levels of LBNP. Consistent with our hypothesis, the measurement of compensatory reserve estimated earlier status of compromise because SmO2 levels were significantly less responsive to progressive central hypovolemia (Figs. 2 and 3). These findings support the notion that advanced machine-learning techniques are essential for the identification of real-time subtle changes in patient status directly associated with compensatory mechanisms. These patterns and features differ across individuals and are unrecognized by measurement of SmO2 and standard vital signs.

A potential limitation of this investigation was the use of a simulated hemorrhage experimental model that involved testing responses of healthy participants rather than being conducted on actual trauma patients with hemorrhage. However, the sensitivity and specificity of the measurement for compensatory reserve reported from this experiment (0.87, 0.80) are remarkably similar (0.80, 0.76) to results of a controlled hemorrhage of 20% estimated circulating blood volume conducted in humans (8). Furthermore, recently documented case observations have been reported (29) in which CRI was measured in patients with trauma followed by sepsis, acute appendicitis, burn injury, massive hematemesis, active labor during childbirth, cardiopulmonary resuscitation, and postural orthostatic tachycardia syndrome. In addition, the algorithm demonstrated the capability to distinguish trauma patients with blunt injury (i.e., little or no hemorrhage) from trauma patients with penetrating injury (i.e., severe hemorrhage), and track resuscitation effectiveness (6). In addition, clinical investigations are currently ongoing in patients with various pathophysiological conditions of reduced circulating blood volume that include Dengue hemorrhagic fever, trauma, orthostatic hypotension, burn injury, and sepsis, or those undergoing voluntary blood donation, controlled hemorrhage, renal dialysis, or cardiopulmonary resuscitation in efforts to test the effectiveness of the CRI technology in “real-world” clinical settings. All preliminary observations indicate a successful translation of the technology for measurement of the compensatory reserve developed in normal, healthy volunteers to unhealthy patients who may have compromised compensatory mechanisms. The initial success for clinical application of the CRI algorithm in patients with various pathological conditions is because the CRI has “learned” what healthy patients look like. This relationship defines the concept of “Precision” or individualized medicine.

The translation of understanding implications of measuring the compensatory reserve to clinical practice is critical to advancing care of a patient with severe hemorrhage. Bleeding cannot be stopped if it is not recognized. Data collected from humans undergoing various experimental (6–9, 25, 27, 28) and clinical (6, 29) conditions of hemorrhage have provided compelling and consistent evidence that the clinician can conclude with high probability that progressive and continuous reduction in an individual's compensatory reserve is indicative of a hemorrhaging patient. Thus, continuous measurement of the compensatory reserve provides the first real-time capability for hemorrhage detection. It is also critical that the CRI technology demonstrate the capability to provide real-time determination of status in patients with hemorrhage independent of baseline measurements. This criterion has been demonstrated in trauma patients with and without hemorrhage as well as those suffering with Dengue hemorrhagic fever upon hospital admission (6). Perhaps as important is that once bleeding is stopped and/or resuscitation has been initiated, continued monitoring of the CRI provides the clinician with feedback that their applied interventions have been effective. However, it is also important to appreciate that individuals with relatively poor capacity to compensate for bleeding may lose much less blood and develop earlier onset of shock than those with large compensatory reserve (6, 8). Providing a capability to accurately triage patients at highest risk for early onset of shock is critical information in situations of mass casualties that are routine in military battlefield settings. Clearly, accurate (i.e., sensitive and specific) diagnosis and effective treatment of an injured patient depends on having information regarding both active bleeding and compensatory status. As such, measurement of the compensatory response provides three significant advantages to the clinician: it can be effectively used to identify bleeding patients, it provides real-time feedback of intervention effectiveness, and it is a sensitive and specific indicator for triage of which patient (s) is at greatest risk for developing shock.

CONCLUSIONS

The results of the present investigation demonstrated that measurement of the compensatory reserve increases sensitivity and specificity for early and accurate prediction of the onset of hemodynamic decompensation compared with SmO2. This superior sensitivity and specificity reflects development of a machine-learning algorithm that was based on recognition of physiology obtained from individuals with high and low tolerance to progressive reductions in central blood volume. Specifically, the SmO2 response of humans with high tolerance to blood loss is associated with greater tolerance to tissue dysoxia. As seen in previous results (6, 7), our data support the notion that measurement of compensatory reserve could be used as a triage decision-support tool capable of providing a more sensitive and specific triage tool for early prediction and appropriate timing of interventions to prevent circulatory shock.

Acknowledgments

The authors acknowledge the assistance of Gary Muniz, Peter Scott, and Gwenn Ellerby for their assistance in data collection and analysis.

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Keywords:

Blood loss; compensatory reserve; hemorrhage; hypovolemia; near infrared spectroscopy; pH; prehospital; tissue oxygen saturation; triage; vasoconstriction

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