Physiologic validation of the compensatory reserve metric obtained from pulse oximetry: A step towards 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 due to the size and cost-intensive nature of standard vital sign monitors or photoplethysmographic volume-clamp (PPGVC) devices used to measure arterial waveforms. In this regard, photoplethysmographic measurements obtained from pulse oximetry (PPGPO) may serve as a useful, portable alternative. This study aimed to validate CRM values obtained using PPGPO. METHODS: Forty-nine healthy adults (25 females) underwent a graded lower body negative pressure (LBNP) protocol to simulate hemorrhage. Arterial waveforms were sampled using PPGPO and PPGVC. The CRM was calculated using a one-dimensional convolutional neural network. Cardiac output and stroke volume were measured using PPGVC. A brachial artery catheter was used to measure intraarterial pressure. A 3-lead ECG 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 mmHg (Range: 45-100 mmHg). 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). CRM values obtained with both PPGPO and PPGVC 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 PPGPO 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 IV.

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.

EVIDENCE FOR BENEFICIAL USE OF THE COMPENSATORY RESERVE MEASUREMENT IN GUIDING INTRAOPERATIVE RESUSCITATION: A PROSPECTIVE COHORT STUDY OF ORTHOTOPIC LIVER TRANSPLANT RECIPIENTS.

ABSTRACT: Introduction: The compensatory reserve measurement (CRM) is a continuous noninvasive monitoring technology that provides an assessment of the integrated capacity of all physiological mechanisms associated with responses to a hypovolemic stressor such as hemorrhagic shock. No prior studies have analyzed its use for intraoperative resuscitation guidance. Methods: A prospective observational study was conducted of 23 patients undergoing orthotopic liver transplant. Chart review was performed to identify timing of various intraoperative events. Data were compared based on predefined thresholds for existence of hemorrhagic shock: CRM lower than 40%, systolic blood pressure (SBP) lower than 90 mm Hg (SBP90), and heart rate (HR) higher than 100 beats per minute (HR100). Regression analysis was performed for predicting resuscitation events, and nonlinear eXtreme Gradient Boosting (XGBoost) models were used to compare CRM with standard vital sign measures. Results: Events where CRM dropped lower than 40% were 2.25 times more likely to lead to an intervention, whereas HR100 and SBP90 were not associated with intraoperative interventions. XGBoost prediction models showed superior discriminatory capacity of CRM alone compared with the model with SBP and HR and no difference when all three were combined (CRM-HR-SBP). All XGBoost models outperformed equivalent linear regression models. Conclusion: These results demonstrate that CRM can provide an adjunctive clinical tool that can augment early and accurate of hemodynamic compromise and promote goal-directed resuscitation in the perioperative setting.

Towards a Lightweight Classifier to Detect Hypovolemic Shock.

Predicting the ability of an individual to compensate for blood loss during hemorrhage and detect the likely onset of hypovolemic shock is necessary to permit early clinical intervention. Towards this end, the compensatory reserve metric (CRM) has been demonstrated to directly correlate with an individual's ability to maintain compensatory mechanisms during loss of blood volume from onset (one-hundred percent health) to exsanguination (zero percent health). This effort describes a lightweight, three-class predictor (good, fair, poor) of an individual's compensatory reserve using a linear support-vector machine (SVM) classifier. A moving mean filter of the predictions demonstrates a feasible model for implementation of real-time hypovolemia monitoring on a wearable device, requiring only 408 bytes to store the models' coefficients and minimal processor cycles to complete the computations.

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.

Verification and Validation of Lower Body Negative Pressure as a Non-Invasive Bioengineering Tool for Testing Technologies for Monitoring Human Hemorrhage.

Since hemorrhage is a leading cause of preventable death in both civilian and military settings, the development of advanced decision support monitoring capabilities is necessary to promote improved clinical outcomes. The emergence of lower body negative pressure (LBNP) has provided a bioengineering technology for inducing progressive reductions in central blood volume shown to be accurate as a model for the study of the early compensatory stages of hemorrhage. In this context, the specific aim of this study was to provide for the first time a systematic technical evaluation to meet a commonly accepted engineering standard based on the FDA-recognized Standard for Assessing Credibility of Modeling through Verification and Validation (V&V) for Medical Devices (ASME standard V&V 40) specifically highlighting LBNP as a valuable resource for the safe study of hemorrhage physiology in humans. As an experimental tool, evidence is presented that LBNP is credible, repeatable, and validated as an analog for the study of human hemorrhage physiology compared to actual blood loss. The LBNP tool can promote the testing and development of advanced monitoring algorithms and evaluating wearable sensors with the goal of improving clinical outcomes during use in emergency medical settings.

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability.

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient's status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

Superiority of compensatory reserve measurement compared with the Shock index for early and accurate detection of reduced central blood volume status.

BACKGROUND: Shock index (SI) equals the ratio of heart rate (HR) to systolic blood pressure (SBP) with clinical evidence that it is more sensitive for trauma patient status assessment and prediction of outcome compared with either HR or SBP alone. We used lower body negative pressure (LBNP) as a human model of central hypovolemia and compensatory reserve measurement (CRM) validated for accurate tracking of reduced central blood volume to test the hypotheses that SI: (1) presents a late signal of central blood volume status; (2) displays poor sensitivity and specificity for predicting the onset of hemodynamic decompensation; and (3) cannot identify individuals at greatest risk for the onset of circulatory shock. METHODS: We measured HR, SBP, and CRM in 172 human subjects (19-55 years) during progressive LBNP designed to determine tolerance to central hypovolemia as a model of hemorrhage. Subjects were subsequently divided into those with high tolerance (HT) (n = 118) and low tolerance (LT) (n = 54) based on completion of 60 mm Hg LBNP. The time course relationship between SI and CRM was determined and receiver operating characteristic (ROC) area under the curve (AUC) was calculated for sensitivity and specificity of CRM and SI to predict hemodynamic decompensation using clinically defined thresholds of 40% for CRM and 0.9 for SI. RESULTS: The time and level of LBNP required to reach a SI = 0.9 (~60 mm Hg LBNP) was significantly greater ( p < 0.001) compared with CRM that reached 40% at ~40 mm Hg LBNP. Shock index did not differ between HT and LT subjects at 45 mm Hg LBNP levels. ROC AUC for CRM was 0.95 (95% CI = 0.94-0.97) compared with 0.91 (0.89-0.94) for SI ( p = 0.0002). CONCLUSION: Despite high sensitivity and specificity, SI delays time to detect reductions in central blood volume with failure to distinguish individuals with varying tolerances to central hypovolemia. LEVEL OF EVIDENCE: Diagnostic Test or Criteria; Level III.

Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation.

Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.

Feature Importance Analysis for Compensatory Reserve to Predict Hemorrhagic Shock.

Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate on an arterial blood pressure (ABP) waveform acquired via photoplethysmography have been shown to provide an effective early indicator. However, these machine learning approaches lack physiological interpretability. In this paper, we evaluate the importance of nine ABP-derived features that provide physiological insight, using a database of 40 human subjects from a lower-body negative pressure model of progressive central hypovolemia. One feature was found to be considerably more important than any other. That feature, the half-rise to dicrotic notch (HRDN), measures an approximate time delay between the ABP ejected and reflected wave components. This delay is an indication of compensatory mechanisms such as reduced arterial compliance and vasoconstriction. For a scale of 0% to 100%, with 100% representing normovolemia and 0% representing decompensation, linear regression of the HRDN feature results in root-mean-squared error of 16.9%, R2 of 0.72, and an area under the receiver operating curve for detecting decompensation of 0.88. These results are comparable to previously reported results from the more complex black box machine learning models. Clinical Relevance- A single physiologically interpretable feature measured from an arterial blood pressure waveform is shown to be effective in monitoring for blood loss and impending hemorrhagic shock based on data from a human lower-body negative pressure model of progressive central hypolemia.

Closed-Loop Controlled Fluid Administration Systems: A Comprehensive Scoping Review.

Physiological Closed-Loop Controlled systems continue to take a growing part in clinical practice, offering possibilities of providing more accurate, goal-directed care while reducing clinicians' cognitive and task load. These systems also provide a standardized approach for the clinical management of the patient, leading to a reduction in care variability across multiple dimensions. For fluid management and administration, the advantages of closed-loop technology are clear, especially in conditions that require precise care to improve outcomes, such as peri-operative care, trauma, and acute burn care. Controller design varies from simplistic to complex designs, based on detailed physiological models and adaptive properties that account for inter-patient and intra-patient variability; their maturity level ranges from theoretical models tested in silico to commercially available, FDA-approved products. This comprehensive scoping review was conducted in order to assess the current technological landscape of this field, describe the systems currently available or under development, and suggest further advancements that may unfold in the coming years. Ten distinct systems were identified and discussed.

Identifying critical DO2 with compensatory reserve during simulated hemorrhage in humans.

BACKGROUND: Based on previous experiments in nonhuman primates, we hypothesized that DO2 crit in humans is 5-6 ml O2 ·kg-1  min-1 . STUDY DESIGN AND METHODS: We measured the compensatory reserve (CRM) and calculated oxygen delivery (DO2 ) in 166 healthy, normotensive, nonsmoking subjects (97 males, 69 females) during progressive central hypovolemia induced by lower body negative pressure as a model of ongoing hemorrhage. Subjects were classified as having either high tolerance (HT; N = 111) or low tolerance (LT; N = 55) to central hypovolemia. RESULTS: HT and LT groups were matched for age, weight, BMI, and vital signs, DO2 and CRM at baseline. The CRM-DO2 relationship was best fitted to a logarithmic model in HT subjects (amalgamated R2  = 0.971) and a second-order polynomial model in the LT group (amalgamated R2  = 0.991). Average DO2 crit for the entire subject cohort was estimated at 5.3 ml O2 ·kg-1  min-1 , but was ~14% lower in HT compared with LT subjects. The reduction in DO2 from 40% CRM to 20% CRM was 2-fold greater in the LT compared with the HT group. CONCLUSIONS: Average DO2 crit in humans is 5.3 ml O2 ·kg-1  min-1 , but is ~14% lower in HT compared with LT subjects. The CRM-DO2 relationship is curvilinear in humans, and different when comparing HT and LT individuals. The threshold for an emergent monitoring signal should be recalibrated from 30% to 40% CRM given that the decline in DO2 from 40% CRM to 20% CRM for LT subjects is located on the steepest part of the CRM-DO2 relationship.

Compensatory reserve and pulse character: Enhanced potential to predict urgency for transfusion and other life-saving interventions after traumatic injury.

BACKGROUND: Field triage of trauma patients requires timely assessment of physiologic status to determine resuscitative needs. Vital signs and rudimentary assessments such as pulse character (PC) are used by first responders to guide decision making. The compensatory reserve measurement (CRM) has demonstrated utility as an easily interpretable method for assessing patient status. We hypothesized that the ability to identify injured patients requiring transfusion and other life-saving interventions (LSI) using a measurement of pulse character could be enhanced by the addition of the CRM. METHODS: We performed a prospective observational study on 300 trauma patients admitted to a level I trauma center. CRM was recorded continuously after device placement on arrival. Patient demographics, field and trauma resuscitation unit vital signs, therapeutic interventions, and outcomes were collected. A field SBP <100 mmHg was utilized as a surrogate for abnormal PC as previously validated. A patient with a CRM threshold value of <60% was considered clinically compromised with a risk of onset of decompensated shock. Data were analyzed to assess the capacity of CRM and pulse character separately or in combination to predict LSI defined as need for transfusion, intubation, tube thoracostomy, or operative/ angiographic hemorrhage control. RESULTS: An improvement in the predictive capability for LSI, transfusion, or a composite outcome was demonstrated by the combination of CRM and PC compared to either measure alone. CONCLUSIONS: Combining PC assessment with CRM has the potential to enhance the recognition of injured patients requiring life-saving intervention thus improving sensitivity of decision support for prehospital providers.

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms.

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer®) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar (p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions.

Advanced medical monitoring for the battlefield: A review on clinical applicability of compensatory reserve measurements for early and accurate hemorrhage detection.

ABSTRACT: Hemorrhagic shock remains the leading cause of mortality in civilian trauma and battlefield settings. The ability of combat medics and other military medical personnel to obtain early identification and assessment of a bleeding casualty is hampered by the use of standard vital signs that fail to provide early predictive indicators of the onset of shock because of compensatory mechanisms. Over the past decade, the emergence and application of new technologies that incorporate the use of artificial intelligence have revealed that continuous, real-time arterial waveform analysis (AWFA) reflects the recruitment of such compensatory mechanism. As such, AWFA can provide early hemorrhage detection and indication of the onset of overt shock compared with standard vital signs. In this review, we provide for the first time a summary of clinical data collected in patients with varying conditions of blood loss, sepsis, and resuscitation with direct comparison of AWFA and standard vital signs. Receiver operating characteristic area under the curve data clearly demonstrate that AWFA provides greater accuracy with early indicators for changes in blood volume compared with standard vital signs. A consistently greater sensitivity generated by AWFA compared with vital signs is associated with its ability to provide earlier hemorrhage detection, while higher specificity reflects its propensity to distinguish "poor" compensators (i.e., those with relatively low tolerance to blood loss) from "good" compensators. The data presented in this review demonstrate that integration of AWFA into medical monitoring capabilities has the potential to improve clinical outcomes of casualties by providing earlier and individualized assessment of blood loss and resuscitation.

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.

Battlefield Vital Sign Monitoring in Role 1 Military Treatment Facilities: A Thematic Analysis of After-Action Reviews from the Prehospital Trauma Registry.

INTRODUCTION: The Prehospital Trauma Registry (PHTR) captures after-action reviews (AARs) as part of a continuous performance improvement cycle and to provide commanders real-time feedback of Role 1 care. We have previously described overall challenges noted within the AARs. We now performed a focused assessment of challenges with regard to hemodynamic monitoring to improve casualty monitoring systems. MATERIALS AND METHODS: We performed a review of AARs within the PHTR in Afghanistan from January 2013 to September 2014 as previously described. In this analysis, we focus on AARs specific to challenges with hemodynamic monitoring of combat casualties. RESULTS: Of the 705 PHTR casualties, 592 had available AAR data; 86 of those described challenges with hemodynamic monitoring. Most were identified as male (97%) and having sustained battle injuries (93%), typically from an explosion (48%). Most were urgent evacuation status (85%) and had a medical officer in their chain of care (65%). The most common vital sign mentioned in AAR comments was blood pressure (62%), and nearly one-quarter of comments stated that arterial palpation was used in place of blood pressure cuff measurements. CONCLUSIONS: Our qualitative methods study highlights the challenges with obtaining vital signs-both training and equipment. We also highlight the challenges regarding ongoing monitoring to prevent hemodynamic collapse in severely injured casualties. The U.S. military needs to develop better methods for casualty monitoring for the subset of casualties that are critically injured.

Compensatory reserve detects subclinical shock with more expeditious prediction for need of life-saving interventions compared to systolic blood pressure and blood lactate.

INTRODUCTION: We conducted a prospective observational study on 205 trauma patients at a level I trauma facility to test the hypothesis that a compensatory reserve measurement (CRM) would identify higher risk for progression to shock and/or need a life-saving interventions (LSIs) earlier than systolic blood pressure (SBP) and blood lactate (LAC). METHODS: A composite outcome metric included blood transfusion, procedural LSI, and mortality. Discrete measures assessed as abnormal (ab) were SBP <90 mmHg, CRM <60%, and LAC >2.0. A graded categorization of shock was defined as: no shock (normal [n] SBP [n-SBP], n-CRM, n-LAC); sub-clinical shock (ab-CRM, n-SBP, n-LAC); occult shock (n-SBP, ab-CRM, ab-LAC); or overt shock (ab-SBP, ab-CRM, ab-LAC). RESULTS: Three patients displayed overt shock, 53 displayed sub-clinical shock, and 149 displayed no shock. After incorporating lactate into the analysis, 86 patients demonstrated no shock, 25 were classified as sub-clinical shock, 91 were classified as occult shock, and 3 were characterized as overt shock. Each shock subcategory revealed a graded increase requiring LSI and transfusion. Initial CRM was associated with progression to shock (odds ratio = 0.97; p < .001) at an earlier time than SBP or LAC. CONCLUSIONS: Initial CRM uncovers a clinically relevant subset of patients who are not detected by SBP and LAC. Our results suggest CRM could be used to more expeditiously identify injured patients likely to deteriorate to shock, with requirements for blood transfusion or procedural LSI.

Efficacy of the compensatory reserve measurement in an emergency department trauma population.

BACKGROUND: The Compensatory Reserve Measurement (CRM) is a novel method used to provide early assessment of shock based on arterial wave form morphology changes. We hypothesized that (1) CRM would be significantly lower in those trauma patients who received life-saving interventions compared with those not receiving interventions, and (2) CRM in patients who received interventions would recover after the intervention was performed. STUDY DESIGN AND METHODS: We captured vital signs along with analog arterial waveform data from trauma patients meeting major activation criteria using a prospective study design. Study team members tracked interventions throughout their emergency department stay. RESULTS: Ninety subjects met inclusion with 13 receiving a blood product and 10 a major airway intervention. Most trauma was blunt (69%) with motor vehicle collisions making up the largest proportion (37%) of injury mechanism. Patients receiving blood products had lower CRM values just prior to administration versus those who did not (50% versus 58%, p = .045), and lower systolic pressure (SBP; 95 versus 123 mmHg, p = .005), diastolic (DBP; 62 versus 79, p = .007), and mean arterial pressure (MAP; 75 versus 95, p = .006), and a higher pulse rate (HR; 101 versus 89 bpm, p = .039). Patients receiving an airway intervention had lower CRM values just prior to administration versus those who did not (48% versus 58%, p = .062); however, SBP, DBP, MAP, and HR were not statistically distinguishable (p ≥ .645). CONCLUSIONS: Our results support our hypotheses that the CRM distinguished those patients who received blood or an airway intervention from those who did not, and increased appropriately after interventions were performed.

Saving the brain after mild-to-moderate traumatic injury: A report on new insights of the physiology underlying adequate maintenance of cerebral perfusion.

ABSTRACT: Traumatic brain injury (TBI) is associated with increased morbidity and mortality in civilian trauma and battlefield settings. It has been classified across a continuum of dysfunctions, with as much as 80% to 90% of cases diagnosed as mild to moderate in combat casualties. In this report, a framework is presented that focuses on the potential benefits for acute noninvasive treatment of reduced cerebral perfusion associated with mild TBI by harnessing the natural transfer of negative intrathoracic pressure during inspiration. This process is known as intrathoracic pressure regulation (IPR) therapy, which can be applied by having a patient breath against a small inspiratory resistance created by an impedance threshold device. Intrathoracic pressure regulation therapy leverages two fundamental principles for improving blood flow to the brain: (1) greater negative intrathoracic pressure enhances venous return, cardiac output, and arterial blood pressure; and (2) lowering of intracranial pressure provides less resistance to cerebral blood flow. These two effects work together to produce a greater pressure gradient that results in an improvement in cerebral perfusion pressure. In this way, IPR therapy has the potential to counter hypotension and hypoxia, potentially significant contributing factors to secondary brain injury, particularly in conditions of multiple injuries that include severe hemorrhage. By implementing IPR therapy in patients with mild-to-moderate TBI, a potential exists to provide early neuroprotection at the point of injury and a bridge to more definitive care, particularly in settings of prolonged delays in evacuation such as those anticipated in future multidomain operations. LEVEL OF EVIDENCE: Report.