Wearable technology for compensatory reserve to sense hypovolemia

Physiologic validation of the Compensatory Reserve Metric obtained from pulse oximetry: A step toward advanced medical monitoring on the battlefield

BACKGROUND

The Compensatory Reserve Metric (CRM) provides a time sensitive indicator of hemodynamic decompensation. However, its in-field utility is limited because of the size and cost-intensive nature of standard vital sign monitors or photoplethysmographic volume-clamp (PPG VC ) devices used to measure arterial waveforms. In this regard, photoplethysmographic measurements obtained from pulse oximetry may serve as a useful, portable alternative. This study aimed to validate CRM values obtained using pulse oximeter (PPG PO ).

METHODS

Forty-nine healthy adults (25 females) underwent a graded lower body negative pressure (LBNP) protocol to simulate hemorrhage. Arterial waveforms were sampled using PPG PO and PPG VC . The CRM was calculated using a one-dimensional convolutional neural network. Cardiac output and stroke volume were measured using PPG VC . A brachial artery catheter was used to measure intra-arterial pressure. A three-lead electrocardiogram was used to measure heart rate. Fixed-effect linear mixed models with repeated measures were used to examine the association between CRM values and physiologic variables. Log-rank analyses were used to examine differences in shock determination during LBNP between monitored hemodynamic parameters.

RESULTS

The median LBNP stage reached was 70 mm Hg (range, 45-100 mm Hg). Relative to baseline, at tolerance, there was a 47% ± 12% reduction in stroke volume, 64% ± 27% increase in heart rate, and 21% ± 7% reduction in systolic blood pressure ( p < 0.001 for all). Compensatory Reserve Metric values obtained with both PPG PO and PPG VC were associated with changes in heart rate ( p < 0.001), stroke volume ( p < 0.001), and pulse pressure ( p < 0.001). Furthermore, they provided an earlier detection of hemodynamic shock relative to the traditional metrics of shock index ( p < 0.001 for both), systolic blood pressure ( p < 0.001 for both), and heart rate ( p = 0.001 for both).

CONCLUSION

The CRM obtained from PPG PO provides a valid, time-sensitized prediction of hemodynamic decompensation, opening the door to provide military medical personnel noninvasive in-field advanced capability for early detection of hemorrhage and imminent onset of shock.

LEVEL OF EVIDENCE

Diagnostic Tests or Criteria; Level III.

Comparing the compensatory reserve metric obtained from invasive arterial measurements and photoplethysmographic volume-clamp during simulated hemorrhage

PURPOSE

The compensatory reserve metric (CRM) is a novel tool to predict cardiovascular decompensation during hemorrhage. The CRM is traditionally computed using waveforms obtained from photoplethysmographic volume-clamp (PPGVC), yet invasive arterial pressures may be uniquely available. We aimed to examine the level of agreement of CRM values computed from invasive arterial-derived waveforms and values computed from PPGVC-derived waveforms.

METHODS

Sixty-nine participants underwent graded lower body negative pressure to simulate hemorrhage. Waveform measurements from a brachial arterial catheter and PPGVC finger-cuff were collected. A PPGVC brachial waveform was reconstructed from the PPGVC finger waveform. Thereafter, CRM values were computed using a deep one-dimensional convolutional neural network for each of the following source waveforms; (1) invasive arterial, (2) PPGVC brachial, and (3) PPGVC finger. Bland-Altman analyses were used to determine the level of agreement between invasive arterial CRM values and PPGVC CRM values, with results presented as the Mean Bias [95% Limits of Agreement].

RESULTS

The mean bias between invasive arterial- and PPGVC brachial CRM values at rest, an applied pressure of -45mmHg, and at tolerance was 6% [-17%, 29%], 1% [-28%, 30%], and 0% [-25%, 25%], respectively. Additionally, the mean bias between invasive arterial- and PPGVC finger CRM values at rest, applied pressure of -45mmHg, and tolerance was 2% [-22%, 26%], 8% [-19%, 35%], and 5% [-15%, 25%], respectively.

CONCLUSION

There is generally good agreement between CRM values obtained from invasive arterial waveforms and values obtained from PPGVC waveforms. Invasive arterial waveforms may serve as an alternative for computation of the CRM.

A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

Intraoperative Use of Compensatory Reserve Measurement in Orthotopic Liver Transplant: Improved Sensitivity for the Prediction of Hypovolemic Events

INTRODUCTION

The compensatory reserve measurement (CRM) is a continuous non-invasive monitoring technology that measures the summation of all physiological mechanisms involved in the compensatory response to central hypovolemia. The CRM is displayed on a 0% to 100% scale. The objective of this study is to characterize the use of CRM in the operative setting and determine its ability to predict hypovolemic events compared to standard vital signs. Orthotopic liver transplant was used as the reference procedure because of the predictable occurrence of significant hemodynamic shifts.

METHODS

A prospective observational cohort study was conducted on 22 consecutive patients undergoing orthotopic liver transplant. The subjects were monitored in accordance with the standard of care. The CRM data were collected concurrently with intraoperative staff blinded to the outputs. The data were stored on secure devices on encrypted files. Based on prior literature, subgroup analysis was performed for high-tolerance (good compensators) and low-tolerance (poor compensators) groups, which was based on a shock index threshold of 0.9. Threshold events were defined as follows: CRM below 60% (CRM60), systolic blood pressure (SBP) below 90 mmHg (SBP90), and heart rate (HR) above 100 beats per minute (HR100).

RESULTS

Complete data were captured in 22 subjects as a result of device malfunction or procedure cancellation. Sensitivity analysis was performed for the detection of hypovolemia at the time of the event. CRM60 was the most sensitive (62.6%) when compared to other threshold measures such as SBP90 (30.6%), HR100 (23.1%), elevated lactate (54.6%), and a drop in hemoglobin (41.7%). The number of patients meeting the CRM60 threshold at the time of the first transfusion (TFX) was higher when compared to SBP90 and HR100 in the overall group (P = .001 and P < .001, respectively) and both the high-tolerance (P = .002 and P = .001, respectively) and low-tolerance groups (P = .016 and P = .001, respectively). Similar results supporting the higher sensitivity of CRM were observed when comparing the number of patients below the threshold at the time of the first vasopressor administration. Start time was standardized so that the time-to-threshold signals for hemodynamic and laboratory parameters could be compared. The median time-to-CRM signal detection before the TFX event was -15.0 minutes (i.e., 15 minutes before TFX). There was no difference when compared to the SBP threshold (median time -5.0 minutes, P = .64) but was significantly sooner when compared to HR (P = .006), lactate (P = .002), and hemoglobin (P < .001).

CONCLUSIONS

At the time of the first TFX, the CRM had a higher rate of detection of a hypovolemic event compared to SBP and HR, indicating a higher sensitivity for the detection of the first hypovolemic event. When combined with all hypovolemic events, sensitivity analysis showed that CRM60 provides the earlier predictive capability. Given that SBP is the clinical standard of care for the initiation of TFX, the finding that median time to event detection was statistically similar between CRM60 and SBP90 was not unexpected. When compared to other measures of hypovolemia, the CRM consistently showed earlier detection of hypovolemic events. Although this study had a small sample size, it produced significant results and can serve as a proof of concept for future large-scale studies.

The answer at our fingertips: Volume status in cirrhosis determined by machine learning and pulse oximeter waveform

OBJECTIVE

The objective of our study was to determine if the waveform from a simple pulse oximeter-like device could be used to accurately assess intravascular volume status in cirrhosis.

METHODS

Patients with cirrhosis underwent waveform recording as well as serum brain natriuretic peptide (BNP) on the day of their cardiac catheterization where invasive cardiac pressures were measured. Waveforms were processed to generate features for machine learning models in order to predict the filling pressures (regression) or to classify the patients as volume overloaded or not (defined as an LVEDP>15).

RESULTS

Nine of 26 patients (35%) had intravascular volume overload. Regression analysis using PPG features (R²  = 0.66) was superior to BNP (R²  = 0.22). Linear discriminant analysis correctly classified patients with an accuracy of 78%, sensitivity of 60%, positive predictive value of 90%, and an AUROC of 0.87.

CONCLUSIONS

Machine learning-enhanced analysis of pulse ox waveforms can estimate intravascular volume overload with a higher accuracy than conventionally measured BNP.

A Wearable and Real-Time Pulse Wave Monitoring System Based on a Flexible Compound Sensor

Continuous monitoring of pulse waves plays a significant role in reflecting physical conditions and disease diagnosis. However, the current collection equipment cannot simultaneously achieve wearable and continuous monitoring under varying pressure and provide personalized pulse wave monitoring targeted different human bodies. To solve the above problems, this paper proposed a novel wearable and real-time pulse wave monitoring system based on a novel flexible compound sensor. Firstly, a custom-packaged pressure sensor, a signal stabilization structure, and a micro pressurization system make up the flexible compound sensor to complete the stable acquisition of pulse wave signals under continuously varying pressure. Secondly, a real-time algorithm completes the analysis of the trend of the pulse wave peak, which can quickly and accurately locate the best pulse wave for different individuals. Finally, the experimental results show that the wearable system can both realize continuous monitoring and reflecting trend differences and quickly locate the best pulse wave for different individuals with the 95% accuracy. The weight of the whole system is only 52.775 g, the working current is 46 mA, and the power consumption is 160 mW. Its small size and low power consumption meet wearable and portable scenarios, which has significant research value and commercialization prospects.

Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms

Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.

Unifying the Estimation of Blood Volume Decompensation Status in a Porcine Model of Relative and Absolute Hypovolemia Via Wearable Sensing

Hypovolemia remains the leading cause of preventable death in trauma cases. Recent research has demonstrated that using noninvasive continuous waveforms rather than traditional vital signs improves accuracy in early detection of hypovolemia to assist in triage and resuscitation. This work evaluates random forest models trained on different subsets of data from a pig model (n = 6) of absolute (bleeding) and relative (nitroglycerin-induced vasodilation) progressive hypovolemia (to 20% decrease in mean arterial pressure) and resuscitation. Features for the models were derived from a multi-modal set of wearable sensors, comprised of the electrocardiogram (ECG), seismocardiogram (SCG) and reflective photoplethysmogram (RPPG) and were normalized to each subject.s baseline. The median RMSE between predicted and actual percent progression towards cardiovascular decompensation for the best model was 30.5% during the relative period, 16.8% during absolute and 22.1% during resuscitation. The least squares best fit line over the mean aggregated predictions had a slope of 0.65 and intercept of 12.3, with an R² value of 0.93. When transitioned to a binary classification problem to identify decompensation, this model achieved an AUROC of 0.80. This study: a) developed a global model incorporating ECG, SCG and RPPG features for estimating individual-specific decompensation from progressive relative and absolute hypovolemia and resuscitation; b) demonstrated SCG as the most important modality to predict decompensation; c) demonstrated efficacy of random forest models trained on different data subsets; and d) demonstrated adding training data from two discrete forms of hypovolemia increases prediction accuracy for the other form of hypovolemia and resuscitation.

Tracking DO2 with Compensatory Reserve During Whole Blood Resuscitation in Baboons

Hemorrhagic shock can be mitigated by timely and accurate resuscitation designed to restore adequate delivery of oxygen (DO2) by increasing cardiac output (CO). However, standard care of using systolic blood pressure (SBP) as a guide for resuscitation may be ineffective and can potentially be associated with increased morbidity. We have developed a novel vital sign called the compensatory reserve measurement (CRM) generated from analysis of arterial pulse waveform feature changes that has been validated in experimental and clinical models of hemorrhage. We tested the hypothesis that thresholds of DO2 could be accurately defined by CRM, a noninvasive clinical tool, while avoiding over-resuscitation during whole blood resuscitation following a 25% hemorrhage in nonhuman primates. To accomplish this, adult male baboons (n = 12) were exposed to a progressive controlled hemorrhage while sedated that resulted in an average (± SEM) maximal reduction of 508 ± 18 mL of their estimated circulating blood volume of 2,130 ± 60 mL based on body weight. CRM increased from 6 ± 0.01% at the end of hemorrhage to 70 ± 0.02% at the end of resuscitation. By linear regression, CRM values of 6% (end of hemorrhage), 30%, 60%, and 70% (end of resuscitation) corresponded to calculated DO2 values of 5.9 ± 0.34, 7.5 ± 0.87, 9.3 ± 0.76, and 11.6 ± 1.3 mL O2·kg·min during resuscitation. As such, return of CRM to ∼65% during resuscitation required only ∼400 mL to restore SBP to 128 ± 6 mmHg, whereas total blood volume replacement resulted in over-resuscitation as indicated by a SBP of 140 ± 7 mmHg compared with an average baseline value of 125 ± 5 mmHg. Consistent with our hypothesis, thresholds of calculated DO2 were associated with specific CRM values. A target resuscitation CRM value of ∼65% minimized the requirement for whole blood while avoiding over-resuscitation. Furthermore, 0% CRM provided a noninvasive metric for determining critical DO2 at approximately 5.3 mL O2·kg·min.

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.

Combat medic testing of a novel monitoring capability for early detection of hemorrhage

BACKGROUND

Current out-of-hospital protocols to determine hemorrhagic shock in civilian trauma systems rely on standard vital signs with military guidelines relying on heart rate and strength of the radial pulse on palpation, all of which have proven to provide little forewarning for the need to implement early intervention prior to decompensation. We tested the hypothesis that addition of a real-time decision-assist machine-learning algorithm, the compensatory reserve measurement (CRM), used by combat medics could shorten the time required to identify the need for intervention in an unstable patient during a hemorrhage profile as compared with vital signs alone.

METHODS

We randomized combat medics from the Army Medical Department Center and School Health Readiness Center of Excellence into three groups: group 1 viewed a display of no simulated hemorrhage and unchanging vital signs as a control (n = 24), group 2 viewed a display of simulated hemorrhage and changing vital signs alone (hemorrhage; n = 31), and group 3 viewed a display of changing vital signs with the addition of the CRM (hemorrhage + CRM; n = 22). Participants were asked to push a computer key when they believed the patient was becoming unstable and needed medical intervention.

RESULTS

The average time of 11.0 minutes (95% confidence interval, 8.7-13.3 minutes) required by the hemorrhage + CRM group to identify an unstable patient (i.e., stop the video sequence) was less by more than 40% (p < 0.01) compared with 18.9 minutes (95% confidence interval, 17.2-20.5 minutes) in the hemorrhage group.

CONCLUSION

The use of a machine-learning monitoring technology designed to measure the capacity to compensate for central blood volume loss resulted in reduced time required by combat medics to identify impending hemodynamic instability.

LEVEL OF EVIDENCE

Diagnostic, level IV.

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.

Evidence for misleading decision support in characterizing differences in tolerance to reduced central blood volume using measurements of tissue oxygenation

BACKGROUND

The physiological response to hemorrhage includes vasoconstriction in an effort to shunt blood to the heart and brain. Hemorrhaging patients can be classified as "good" compensators who demonstrate high tolerance (HT) or "poor" compensators who manifest low tolerance (LT) to central hypovolemia. Compensatory vasoconstriction is manifested by lower tissue oxygen saturation (StO₂ ), which has propelled this measure as a possible early marker of shock. The compensatory reserve measurement (CRM) has also shown promise as an early indicator of decompensation.

METHODS

Fifty-one healthy volunteers (37% LT) were subjected to progressive lower body negative pressure (LBNP) as a model of controlled hemorrhage designed to induce an onset of decompensation. During LBNP, CRM was determined by arterial waveform feature analysis. StO₂ , muscle pH, and muscle H⁺ concentration were calculated from spectrum using near-infrared spectroscopy (NIRS) on the forearm.

RESULTS

These values were statistically indistinguishable between HT and LT participants at baseline (p ≥ 0.25). HT participants exhibited lower (p = 0.01) StO₂ at decompensation compared to LT participants.

CONCLUSIONS

Lower StO₂ measured in patients during low flow states associated with significant hemorrhage does not necessarily translate to a more compromised physiological state, but may reflect a greater resistance to the onset of shock. Only the CRM was able to distinguish between HT and LT participants early in the course of hemorrhage, supported by a significantly greater ROC AUC (0.90) compared with STO₂ (0.68). These results support the notion that measures of StO₂ could be misleading for triage and resuscitation decision support.

1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms

We propose a Deep Convolutional Neural Network (CNN) architecture for computing a Compensatory Reserve Metric (CRM) for trauma victims suffering from hypovolemia (decreased circulating blood volume). The CRM is a single health indicator value that ranges from 100% for healthy individuals, down to 0% at hemodynamic decompensation - when the body can no longer compensate for blood loss. The CNN is trained on 20 second blood pressure waveform segments obtained from a finger-cuff monitor of 194 subjects. The model accurately predicts CRM when tested on data from 22 additional human subjects obtained from Lower Body Negative Pressure (LBNP) emulation of hemorrhage, attaining a mean squared error (MSE) of 0.0238 over the full range of values, including those from subjects with both low and high tolerance to central hypovolemia.

Bridging the gap between military prolonged field care monitoring and exploration spaceflight: the compensatory reserve

The concept of prolonged field care (PFC), or medical care applied beyond doctrinal planning timelines, is the top priority capability gap across the US Army. PFC is the idea that combat medics must be prepared to provide medical care to serious casualties in the field without the support of robust medical infrastructure or resources in the event of delayed medical evacuation. With limited resources, significant distances to travel before definitive care, and an inability to evacuate in a timely fashion, medical care during exploration spaceflight constitutes the ultimate example PFC. One of the main capability gaps for PFC in both military and spaceflight settings is the need for technologies for individualized monitoring of a patient's physiological status. A monitoring capability known as the compensatory reserve measurement (CRM) meets such a requirement. CRM is a small, portable, wearable technology that uses a machine learning and feature extraction-based algorithm to assess real-time changes in hundreds of specific features of arterial waveforms. Future development and advancement of CRM still faces engineering challenges to develop ruggedized wearable sensors that can measure waveforms for determining CRM from multiple sites on the body and account for less than optimal conditions (sweat, water, dirt, blood, movement, etc.). We show here the utility of a military wearable technology, CRM, which can be translated to space exploration.

Comparisons of Traditional Metabolic Markers and Compensatory Reserve as Early Predictors of Tolerance to Central Hypovolemia in Humans

Circulatory shock remains a leading cause of death in both military and civilian trauma. Early, accurate and reliable prediction of decompensation is necessary for the most efficient interventions and clinical outcomes. Individual tolerance to reduced central blood volume can serve as a model to assess the sensitivity and specificity of vital sign measurements. The compensatory reserve (CRM) is the measurement of this capacity. Measurements of muscle oxygen saturation (SmO2), blood lactate, and end tidal CO2 (EtCO2) have recently gained attention as prognostic tools for early assessment of the status of patients with progressive hemorrhage, but lack the ability to adequately differentiate individual tolerance to hypovolemia. We hypothesized that the CRM would better predict hemodynamic decompensation and provide greater specificity and sensitivity than metabolic measures. To test this hypothesis, we employed lower body negative pressure on healthy human subjects until symptoms of presyncope were evident. Receiver operating characteristic area under the curve (ROC AUC), sensitivity, and specificity were used to evaluate the ability of CRM, partial pressure of oxygen (pO2), partial pressure of carbon dioxide (pCO2), SmO2, lactate, EtCO2, potential of hydrogen (pH), base excess and hematocrit (Hct) to predict hemodynamic decompensation. The ROC AUC for CRM (0.94) had a superior ability to predict decompensation compared with pO2 (0.85), pCO2 (0.62), SmO2 (0.72), lactate (0.57), EtCO2 (0.74), pH (0.55), base excess (0.59), and Hct (0.67). Similarly, CRM also exhibited the greatest sensitivity and specificity. These findings support the notion that CRM provides superior detection of hemodynamic compensation compared with commonly used clinical metabolic measures.

Physiological comparison of hemorrhagic shock and V˙ O2max: A conceptual framework for defining the limitation of oxygen delivery

IMPACT STATEMENT

Disturbance of normal homeostasis occurs when oxygen delivery and energy stores to the body's tissues fail to meet the energy requirement of cells. The work submitted in this review is important because it advances the understanding of inadequate oxygen delivery as it relates to early diagnosis and treatment of circulatory shock and its relationship to disturbance of normal functioning of cellular metabolism in life-threatening conditions of hemorrhage. We explored data from the clinical and exercise literature to construct for the first time a conceptual framework for defining the limitation of inadequate delivery of oxygen by comparing the physiology of hemorrhagic shock caused by severe blood loss to maximal oxygen uptake induced by intense physical exercise. We also provide a translational framework in which understanding the fundamental relationship between the body's reserve to compensate for conditions of inadequate oxygen delivery as a limiting factor to V ˙ O2max helps to re-evaluate paradigms of triage for improved monitoring of accurate resuscitation in patients suffering from hemorrhagic shock.

Differentiating compensatory mechanisms associated with low tolerance to central hypovolemia in women

Women generally display lower tolerance to acute central hypovolemia than men. The measurement of compensatory reserve (CRM) is a novel metric that provides information about the sum total of all mechanisms that together work to compensate for the relative blood volume deficit. Hemodynamic decompensation occurs with depletion of the CRM (i.e., 0% CRM). In the present study, we hypothesized that the lower tolerance to progressive central hypovolemia reported in women can be explained by a faster reduction rate in CRM compared with men rather than sex differences in absolute integrated compensatory responses. Continuous, noninvasive measures of CRM were collected from 208 healthy volunteers (107 men and 85 women) who underwent progressive stepwise central hypovolemia induced by lower body negative pressure to the point of presyncope. Comparisons revealed shorter ( P < 0.01) times in female participants compared with male participants to reach 30% and 0% CRM. Similarly, the lower body negative pressure level, represented by the cumulative stress index, was less at 30% and 0% CRM in women compared with men ( P < 0.01). Changes in hemodynamic responses and frequency-domain data (oscillations in cerebral blood flow velocity and mean arterial blood pressure) were similar between men and women at 0% CRM ( P > 0.05). We conclude that compensatory responses to central hypovolemia in women were similar to men but were depleted at a faster rate compared with men. The earlier depletion of the compensatory reserve in women appears to be influenced by failure to maintain adequate cerebral oxygen delivery.

NEW & NOTEWORTHY We compared hemodynamic and metabolic responses in men and women to experimentally controlled reductions in central blood volume at physiologically equivalent levels of compensatory reserve. We corroborated previous findings that females have lower tolerance to central hypovolemia than males but demonstrated for the first time that compensatory responses are similar. Our findings suggest lower tolerance to central hypovolemia in women results from reaching critical cerebral delivery of oxygen faster than men.

Unmasking the Hypovolemic Shock Continuum: The Compensatory Reserve

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.

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage

Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock.

Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of −50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included: volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods: sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates.

Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73–0.98 and class 2: 0.56–0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived pulse height. The highest combination for class 2 was found for the model with the volumetric features (0.72 and 0.91).

Conclusion: The most sensitive models for the detection of advanced CBV reduction comprised data that describe features from volumetric parameters and from cerebral blood flow velocity hemodynamics. In a validated model of hemorrhage in humans these parameters provide the best indication of the progression of central hypovolemia.