Running on empty? The compensatory reserve index

Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis

BACKGROUND

Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging within medicine for accurate prediction modeling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma.

METHODS

This systematic review was registered on the International Prospective register of Systematic Reviews (CRD4202237110). MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were systematically searched. Publications reporting an ML model that predicted blood transfusion in injured adult patients were included. Data extraction and risk of bias assessment were performed using validated frameworks. Data were synthesized narratively because of significant heterogeneity.

RESULTS

Twenty-five ML models for blood transfusion prediction in trauma were identified. Models incorporated diverse predictors and varied ML methodologies. Predictive performance was variable, but eight models achieved excellent discrimination (area under the receiver operating characteristic curve, >0.9) and nine models achieved good discrimination (area under the receiver operating characteristic curve, >0.8) in internal validation. Only two models reported measures of calibration. Four models have been externally validated in prospective cohorts: the Bleeding Risk Index, Compensatory Reserve Index, the Marsden model, and the Mina model. All studies were considered at high risk of bias often because of retrospective data sets, small sample size, and lack of external validation.

DISCUSSION

This review identified 25 ML models developed to predict blood transfusion requirement after injury. Seventeen ML models demonstrated good to excellent performance in silico, but only four models were externally validated. To date, ML models demonstrate the potential for early and individualized blood transfusion prediction, but further research is critically required to narrow the gap between ML model development and clinical application.

LEVEL OF EVIDENCE

Systematic Review Without Meta-analysis; Level IV.

Compensatory reserve index during central hypovolemia and volume loading in healthy young and older hyperthermic adults: A pilot study

The compensatory reserve index (CRI), derived from machine learning algorithms from peripherally obtained photoplethysmography signals, provides a non-invasive assessment of cardiovascular stability, that may be useful clinically. Briefly, the CRI device provides a value between 0 and 1, with 1 reflecting full compensable capabilities and 0 reflecting little to no compensable capabilities. However, the CRI algorithm was developed in younger to middle aged adults, such that it is unknown if older age modulates CRI responses to cardiovascular challenges. In young and older subjects, we compared CRI responses to normothermic and hyperthermic progressive lower body negative pressure (LBNP), and volume loading with saline infusion. Eleven younger (20-36 years) and 10 older (61-75 years) healthy participants underwent (1) graded normothermic LBNP up to 30 mmHg, (2) graded hyperthermic (1.5°C increase in blood temperature) LBNP up to 30 mmHg, and (3) infusion of 15 mL/kg saline (volume loading) with hyperthermia maintained. CRI was obtained throughout each procedure. CRI at 30 mmHg LBNP was 0.18 and 0.24 units greater in the older group during normothermic and hyperthermic LBNP, respectively. However, CRI was not different between age groups at any other LBNP stage, nor did CRI change with volume loading regardless of age. In response to passive hyperthermia alone, regression analyses showed that heart rate was the strongest predictor of CRI. Blood temperature, rate pressure product, and stroke volume were also predictive of CRI but to a lesser extent. In conclusion, age attenuates the reduction in CRI during progressive normothermic and hyperthermic LBNP, but only at 30 mmHg. Second, the CRI was unchanged during volume loading in all subjects. Future studies should determine whether the age differences in CRI reflect age differences in LBNP tolerance.

Use of an Artificial Intelligence Device for Evaluating Blood Loss in Complex Major Orthopaedic Surgery Procedures

BACKGROUND

An artificial intelligence algorithm that analyzes the pulse oximeter waveform in the fingertip can be used to determine the compensatory reserve index (CRI) in trauma patients. This measurement shows the remaining cardiovascular capacity and is known to be more specific and sensitive in detecting blood loss than are routine vital signs. We hypothesized that the CRI measurement could predict loss of reserve cardiovascular capacity in patients undergoing major orthopaedic surgery, and therefore could help in their management.

METHODS

A total of 304 patients undergoing lower extremity arthroplasty consented to participate in waveform monitoring. Pulse oximeter waveforms were sensed with a fingertip probe and processed with a tablet computer that remained with the patient during surgery and recovery in the hospital. The CRI, systolic blood pressure, and heart rate were evaluated throughout the postoperative period.

RESULTS

The CRI measurement identified a group of patients who were significantly more likely to require transfusions and emergency medical care (P = .000021). Patients who had morbid obesity were especially likely to have low CRI results and a high percentage of clinical events. A CRI of 0.40 or more was evaluated retrospectively as the criterion for withholding transfusion in 54 patients, but that group had a significantly higher incidence of transfusion later in treatment than did the cohort as a whole. The systolic blood pressure and heart rate were not useful in predicting the need for transfusion until late in treatment.

CONCLUSIONS

This study suggests that the CRI measurement can identify patients at risk for transfusion and the need for urgent medical care and may aid in the management of blood loss and transfusion in major orthopedic surgery.

Prediction of Occult Hemorrhage in the Lower Body Negative Pressure Model: Initial Validation of Machine Learning Approaches

INTRODUCTION

Detection of occult hemorrhage (OH) before progression to clinically apparent changes in vital signs remains an important clinical problem in managing trauma patients. The resource-intensiveness associated with continuous clinical patient monitoring and rescue from frank shock makes accurate early detection and prediction with noninvasive measurement technology a desirable innovation. Despite significant efforts directed toward the development of innovative noninvasive diagnostics, the implementation and performance of the newest bedside technologies remain inadequate. This poor performance may reflect the limitations of univariate systems based on one sensor in one anatomic location. It is possible that when signals are measured with multiple modalities in multiple locations, the resulting multivariate anatomic and temporal patterns of measured signals may provide additional discriminative power over single technology univariate measurements. We evaluated the potential superiority of multivariate methods over univariate methods. Additionally, we utilized machine learning-based models to compare the performance of noninvasive-only to noninvasive-plus-invasive measurements in predicting the onset of OH.

MATERIALS AND METHODS

We applied machine learning methods to preexisting datasets derived using the lower body negative pressure human model of simulated hemorrhage. Employing multivariate measured physiological signals, we investigated the extent to which machine learning methods can effectively predict the onset of OH. In particular, we applied 2 ensemble learning methods, namely, random forest and gradient boosting.

RESULTS

Analysis of precision, recall, and area under the receiver operating characteristic curve showed a superior performance of multivariate approach to that of the univariate ones. In addition, when using both invasive and noninvasive features, random forest classifier had a recall 95% confidence interval (CI) of 0.81 to 0.86 with a precision 95% CI of 0.65 to 0.72. Interestingly, when only noninvasive features were employed, the results worsened only slightly to a recall 95% CI of 0.80 to 0.85 and a precision 95% CI of 0.61 to 0.73.

CONCLUSIONS

Multivariate ensemble machine learning-based approaches for the prediction of hemodynamic instability appear to hold promise for the development of effective solutions. In the lower body negative pressure multivariate hemorrhage model, predictions based only on noninvasive measurements performed comparably to those using both invasive and noninvasive measurements.

Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95-1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0-15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38-0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

Detection of Postpartum Hemorrhage Using Compensatory Reserve Index in Patients Undergoing Cesarean Delivery

BACKGROUND

Postpartum hemorrhage (PPH) is the leading cause of maternal death worldwide. Early recognition and management are imperative for improved outcomes. The compensatory reserve index (CRI) is a novel physiological parameter that trends changes in intravascular volume, by continuously comparing extracted photoplethysmogram waveforms to a reference model that was derived from a human model of acute blood loss. This study sought to determine whether the CRI pattern was differential between those who do and do not experience PPH during cesarean delivery and compare these results to the American Society of Anesthesiologists (ASA) standards for noninvasive monitoring.

METHODS

Parturients undergoing cesarean delivery were enrolled between February 2020 and May 2021. A noninvasive CRI monitor was applied to collect continuous CRI values throughout the intraoperative and immediate postpartum periods. Patients were stratified based on blood loss into PPH versus non-PPH groups. PPH was defined as a quantitative blood loss >1000 mL. Function-on-scalar (FoS) regression was used to compare trends in CRI between groups (PPH versus non-PPH) during the 10 to 60-minute window after delivery. Two subanalyses excluding patients who received general anesthesia and preeclamptics were performed.

RESULTS

Fifty-one patients were enrolled in the study. Thirteen (25.5%) patients experienced PPH. Pregnant patients who experienced PPH had, on average, lower postdelivery CRI values (-0.13; 95% CI, -0.13 to -0.12; P < .001) than those who did not experience PPH. This persisted even when adjusting for preeclampsia and administration of uterotonics. The average mean arterial pressure (MAP) measurements were not statistically significant (-1.67; 95% CI, -3.57 to 0.22; P = .09). Similar trends were seen when excluding patients who underwent general anesthesia. When excluding preeclamptics, CRI values remained lower in those who hemorrhaged (-0.18; 95% CI, -0.19 to -0.17; P < .001).

CONCLUSIONS

CRI detects changes in central volume status not distinguished by MAP. It has the potential to serve as a continuous, informative metric, notifying providers of acute changes in central volume status due to PPH during cesarean delivery.

THE COMPENSATORY RESERVE INDEX FOR PREDICTING HEMORRHAGIC SHOCK IN PREHOSPITAL TRAUMA

ABSTRACT

Background: The compensatory reserve index (CRI) is a noninvasive, continuous measure designed to detect intravascular volume loss. CRI is derived from the pulse oximetry waveform and reflects the proportion of physiologic reserve remaining before clinical hemodynamic decompensation. Methods: In this prospective, observational, prehospital cohort study, we measured CRI in injured patients transported by emergency medical services (EMS) to a single Level I trauma center. We determined whether the rolling average of CRI values over 60 s (CRI trend [CRI-T]) predicts in-hospital diagnosis of hemorrhagic shock, defined as blood product administration in the prehospital setting or within 4 h of hospital arrival. We hypothesized that lower CRI-T values would be associated with an increased likelihood of hemorrhagic shock and better predict hemorrhagic shock than prehospital vital signs. Results: Prehospital CRI was collected on 696 adult trauma patients, 21% of whom met our definition of hemorrhagic shock. The minimum CRI-T was 0.14 (interquartile range [IQR], 0.08-0.31) in those with hemorrhagic shock and 0.31 (IQR 0.15-0.50) in those without ( P = <0.0001). The positive likelihood ratio of a CRI-T value <0.2 predicting hemorrhagic shock was 1.85 (95% confidence interval [CI], 1.55-2.22). The area under the ROC curve (AUC) for the minimum CRI-T predicting hemorrhagic shock was 0.65 (95% CI, 0.60-0.70), which outperformed initial prehospital HR (0.56; 95% CI, 0.50-0.62) but underperformed EMS systolic blood pressure and shock index (0.74; 95% CI, 0.70-0.79 and 0.72; 95% CI, 0.67-0.77, respectively). Conclusions: Low prehospital CRI-T predicts blood product transfusion by EMS or within 4 hours of hospital arrival but is less prognostic than EMS blood pressure or shock index. The evaluated version of CRI may be useful in an austere setting at identifying injured patients that require the most significant medical resources. CRI may be improved with noise filtering to attenuate the effects of vibration and patient movement.

Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study

BACKGROUND

Predicting the onset of hemodynamic instability before it occurs remains a sought-after goal in acute and critical care medicine. Technologies that allow for this may assist clinicians in preventing episodes of hemodynamic instability (EHI). We tested a novel noninvasive technology, the Analytic for Hemodynamic Instability-Predictive Indicator (AHI-PI), which analyzes a single lead of electrocardiogram (ECG) and extracts heart rate variability and morphologic waveform features to predict an EHI prior to its occurrence.

METHODS

Retrospective cohort study at a quaternary care academic health system using data from hospitalized adult patients between August 2019 and April 2020 undergoing continuous ECG monitoring with intermittent noninvasive blood pressure (NIBP) or with continuous intraarterial pressure (IAP) monitoring.

RESULTS

AHI-PI's low and high-risk indications were compared with the presence of EHI in the future as indicated by vital signs (heart rate > 100 beats/min with a systolic blood pressure < 90 mmHg or a mean arterial blood pressure of < 70 mmHg). 4,633 patients were analyzed (3,961 undergoing NIBP monitoring, 672 with continuous IAP monitoring). 692 patients had an EHI (380 undergoing NIBP, 312 undergoing IAP). For IAP patients, the sensitivity and specificity of AHI-PI to predict EHI was 89.7% and 78.3% with a positive and negative predictive value of 33.7% and 98.4% respectively. For NIBP patients, AHI-PI had a sensitivity and specificity of 86.3% and 80.5% with a positive and negative predictive value of 11.7% and 99.5% respectively. Both groups performed with an AUC of 0.87. AHI-PI predicted EHI in both groups with a median lead time of 1.1 h (average lead time of 3.7 h for IAP group, 2.9 h for NIBP group).

CONCLUSIONS

AHI-PI predicted EHIs with high sensitivity and specificity and within clinically significant time windows that may allow for intervention. Performance was similar in patients undergoing NIBP and IAP monitoring.

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.

Artificial intelligence and machine learning for hemorrhagic trauma care

Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.

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.

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.

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.

Detection of subclinical hemorrhage using electrical impedance: a porcine study

OBJECTIVE

Analyze the performance of electrical impedance tomography (EIT) in an innovative porcine model of subclinical hemorrhage and investigate associations between EIT and hemodynamic trends.

APPROACH

Twenty-five swine were bled at slow rates to create an extended period of subclinical hemorrhage during which the animal's heart rate (HR) and blood pressure (BP) remained stable from before hemodynamic deterioration, where stable was defined as < 15% decrease in BP and < 20% increase in HR - i.e. hemorrhages were hidden from standard vital signs of HR and BP. Continuous vital signs, photo-plethysmography, and continuous non-invasive EIT data were recorded and analyzed with the objective of developing an improved means of detecting subclinical hemorrhage - ideally as early as possible.

MAIN RESULTS

Best area-under-the-curve (AUC) values from comparing bleed to no-bleed epochs were 0.96 at a 80 ml bleed (~15.4 minutes) using an EIT-data-based metric and 0.79 at a 120 ml bleed (~23.1 minutes) from invasively measured BP - i.e. the EIT-data-based metric achieved higher AUCs at earlier points compared to standard clinical metrics without requiring image reconstructions.

SIGNIFICANCE

In this clinically relevant porcine model of subclinical hemorrhage, EIT appears to be superior to standard clinical metrics in early detection of hemorrhage.

The compensatory reserve index predicts recurrent shock in patients with severe dengue

BACKGROUND

Dengue shock syndrome (DSS) is one of the major clinical phenotypes of severe dengue. It is defined by significant plasma leak, leading to intravascular volume depletion and eventually cardiovascular collapse. The compensatory reserve Index (CRI) is a new physiological parameter, derived from feature analysis of the pulse arterial waveform that tracks real-time changes in central volume. We investigated the utility of CRI to predict recurrent shock in severe dengue patients admitted to the ICU.

METHODS

We performed a prospective observational study in the pediatric and adult intensive care units at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Patients were monitored with hourly clinical parameters and vital signs, in addition to continuous recording of the arterial waveform using pulse oximetry. The waveform data was wirelessly transmitted to a laptop where it was synchronized with the patient's clinical data.

RESULTS

One hundred three patients with suspected severe dengue were recruited to this study. Sixty-three patients had the minimum required dataset for analysis. Median age was 11 years (IQR 8-14 years). CRI had a negative correlation with heart rate and moderate negative association with blood pressure. CRI was found to predict recurrent shock within 12 h of being measured (OR 2.24, 95% CI 1.54-3.26), P < 0.001). The median duration from CRI measurement to the first recurrent shock was 5.4 h (IQR 2.9-6.8). A CRI cutoff of 0.4 provided the best combination of sensitivity and specificity for predicting recurrent shock (0.66 [95% CI 0.47-0.85] and 0.86 [95% CI 0.80-0.92] respectively).

CONCLUSION

CRI is a useful non-invasive method for monitoring intravascular volume status in patients with severe dengue.

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.

Baseline Values of the Compensatory Reserve Index in a Healthy Pediatric Population

The objective of this study is to describe the compensatory reserve index (CRI) baseline values in a healthy cohort of healthy pediatric patients and evaluate the existing correlation with other physiological parameters that influence compensatory hemodynamic mechanisms. CRI is a computational algorithm that has been broadly applied to non-invasively estimate hemodynamic vascular adaptations during acute blood loss. So far, there is a lack of baseline values from healthy individuals, which complicates accurately estimating the severity of the hemodynamic compromise. Additionally, the application of this technology in pediatric populations is limited to a few reports, highlighting a marked variability by age, weight, and other physiological parameters. The CRI of 205 healthy subjects from 0 to 60 years of age were prospectively evaluated from January to February 2020 at several public outpatient clinics in El Salvador; vital signs and sociodemographic data were also collected during this period. After data collection, baseline values were classified for each age group. Multiple correlation models were tested between the CRI and the other physiological parameters. CRI value varies significantly for each age group, finding for patients under 18 years old a mean value lower than 0.6, which is currently considered the lower normal limit for adults. CRI presents strong correlations with other physiological variables such as age, weight, estimated blood volume, and heart rate (R > 0.8, R2 > 0.6, p < 0.0001). There is significant variability in the CRI normal values observed in healthy patients based on age, weight, estimated blood volume, and heart rate.

Comparison of non-invasive physiological assessment tools between simple and perforated appendicitis in children

PURPOSE

The role of non-invasive measures of physiologic reserve, specifically the Compensatory reserve index (CRI) and the Shock index pediatric age-adjusted (SIPA), is unknown in the management of children with acute appendicitis. CRI is a first-in-class algorithm that uses pulse oximetry waveforms to continuously monitor central volume status loss. SIPA is a well-validated, but a discontinuous measure of shock that has been calibrated for children.

METHODS

Children with suspected acute appendicitis (2-17 years old) were prospectively enrolled at a single center from 2014 to 2015 and monitored with a CipherOx CRI™ M1 pulse oximeter. CRI values range from 1 (normovolemia) to 0 (life-threatening hypovolemia). SIPA is calculated by dividing heart rate by systolic blood pressure and categorized as normal or abnormal, based on age-specific cutoffs. Univariate and multivariable regression models were developed with simple versus perforated appendicitis as the outcome.

RESULTS

Almost half the patients (45/94, 48%) had perforated appendicitis. On univariate analysis, the median admission CRI value was significantly higher (0.60 versus 0.33, p < 0.001) and the ED SIPA values were significantly lower (0.90 versus 1.10, p = 0.002) in children with simple versus perforated appendicitis. In a multivariable model, only CRI significantly detected differences in the physiologic state between patients with simple and perforated appendicitis.

CONCLUSIONS

CRI is a non-invasive measure of physiologic reserve that may be used to accurately guide early management of children with acute simple versus perforated appendicitis.

Early Maladaptive Cardiovascular Responses are Associated with Mortality in a Porcine Model of Hemorrhagic Shock

BACKGROUND

Hemorrhage is a leading cause of death on the battlefield. Current methods for predicting hemodynamic deterioration during hemorrhage are of limited accuracy and practicality. During a study of the effects of remote ischemic preconditioning in pigs that underwent hemorrhage, we noticed arrhythmias among all pigs that died before the end of the experiment but not among surviving pigs. The present study was designed to identify and characterize the early maladaptive hemodynamic responses (tachycardia in the presence of hypotension without a corresponding increase in cardiac index or mean arterial blood pressure) and their predictive power for early mortality in this experimental model.

METHODS

Controlled hemorrhagic shock was induced in 16 pigs. Hemodynamic parameters were monitored continuously for 7 h following bleeding. Changes in cardiovascular and laboratory parameters were analyzed and compared between those that had arrhythmia and those that did not.

RESULTS

All animals had similar changes in parameters until the end of the bleeding phase. Six animals developed arrhythmias and died early, while 10 had no arrhythmias and survived longer than 6 h or until euthanasia. Unlike survivors, those that died did not compensate for cardiac output (CO), diastolic blood pressure (DBP), and stroke volume (SV). Oxygen delivery (DO2) and mixed venous saturation (SvO2) remained low in animals that had arrhythmia, while achieving certain measures of recuperation in animals that did not. Serum lactate increased earlier and continued to rise in all animals that developed arrhythmias. No significant differences in hemoglobin concentrations were observed between groups.

CONCLUSIONS

Despite similar initial changes in variables, we found that low CO, DBP, SV, DO2, SvO2, and high lactate are predictive of death in this animal model. The results of this experimental study suggest that maladaptive responses across a range of cardiovascular parameters that begin early after hemorrhage may be predictive of impending death, particularly in situations where early resuscitative treatment may be delayed.

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.

Early Detection of Hypotension Using a Multivariate Machine Learning Approach

INTRODUCTION

The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension.

MATERIALS AND METHODS

Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized.

RESULTS

The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals.

CONCLUSION

In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.

Lessons Learned From the First Pilot Study of the Compensatory Reserve Index After Congenital Heart Surgery Requiring Cardiopulmonary Bypass

BACKGROUND

Early warning systems that utilize dense physiologic data and machine learning may aid prediction of decompensation after congenital heart surgery (CHS). The Compensatory Reserve Index (CRI) analyzes changing features of the pulse waveform to predict hemodynamic decompensation in adults, but it has never been studied after CHS. This study sought to understand the feasibility, safety, and potential utility of CRI monitoring after CHS with cardiopulmonary bypass (CPB).

METHODS

A single-center prospective pilot cohort of patients undergoing pulmonary valve replacement was studied. Compensatory Reserve Index was continuously measured from preoperative baseline through the first 24 postoperative hours. Average CRI values during selected procedural phases were compared between patients with an intensive care unit (ICU) length of stay (LOS) <3 days versus LOS ≥3 days.

RESULTS

Twenty-three patients were enrolled. On average, 17,445 (±3,152) CRI data points were collected and 0.33% (±0.40) of data were missing per patient. There were no adverse events related to monitoring. Five (21.7%) patients had an ICU LOS ≥3 days. Compared to the ICU LOS <3 days group, the ICU LOS ≥3 days group had a greater decrease in CRI from baseline to immediately after CPB (-0.3 ± 0.1 vs -0.1 ± 0.2, P = .003) and were less likely to recover to baseline CRI during the monitoring period (20% vs 83%, P = .017).

CONCLUSIONS

Compensatory Reserve Index monitoring after CHS with CPB seems feasible and safe. Early changes in CRI may precede meaningful clinical outcomes, but this requires further study.

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.

A Pilot Study using the Compensatory Reserve Index to evaluate individuals with Postural Orthostatic Tachycardia syndrome

PURPOSE

The diagnosis of Postural Orthostatic Tachycardia syndrome traditionally involves orthostatic vitals evaluation. The Compensatory Reserve Index is a non-invasive, FDA-cleared algorithm that analyses photoplethysmogram waveforms in real time to trend subtle waveform features associated with varying degrees of central volume loss, from normovolemia to decompensation. We hypothesised that patients who met physiologic criteria for Postural Orthostatic Tachycardia syndrome would have greater changes in Compensatory Reserve Index with orthostatic vitals.

METHODS

Orthostatic vitals and Compensatory Reserve Index values were assessed in individuals previously diagnosed with Postural Orthostatic Tachycardia syndrome and healthy controls aged 12-21 years. Adolescents were grouped for comparison based on whether they met heart rate criteria for Postural Orthostatic Tachycardia syndrome (physiologic Postural Orthostatic Tachycardia syndrome).

RESULTS

Sixty-one patients were included. Eighteen percent of patients with an existing Postural Orthostatic Tachycardia syndrome diagnosis met heart rate criteria, and these patients had significantly greater supine to standing change in Compensatory Reserve Index (0.67 vs. 0.51; p<0.001). The optimal change in Compensatory Reserve Index for physiologic Postural Orthostatic Tachycardia syndrome was 0.60. Patients with physiologic Postural Orthostatic Tachycardia syndrome were more likely to report previous diagnoses of anxiety or depression (p = 0.054, 0.042).

CONCLUSION

An accurate diagnosis of Postural Orthostatic Tachycardia syndrome may be confounded by related comorbidities. Only 18% (8/44) of previously diagnosed Postural Orthostatic Tachycardia syndrome patients met heart rate criteria. Findings support the utility of objective physiologic measures, such as the Compensatory Reserve Index, to more accurately identify patients with true autonomic dysfunction.

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.

A Noninvasive Stroke Volume Monitoring for Early Detection of Minimal Blood Loss: A Pilot Study

INTRODUCTION

Alternation in traditional vital signs can only be observed during advanced stages of hypovolemia and shortly before the hemodynamic collapse. However, even minimal blood loss induces a decrease in the cardiac preload which translates to a decrease in stroke volume, but these indices are not readily monitored. We aimed to determine whether minor hemodynamic alternations induced by controlled and standardized hypovolemia can be detected by a whole-body bio-impedance technology.

METHODS

This was a non-randomized controlled trial that enrolled healthy blood donors. Vital signs, as well as shock index and stroke volume (SV), were recorded using noninvasive cardiac system, a noninvasive whole-body impedance-based hemodynamic analysis system, during phlebotomy.

RESULTS

Sixty subjects were included in the study group and 20 in the control group. Blood loss of 450 mL resulted in a significant decrease in systolic blood pressure (5 mm Hg; 95% CI 3, 6) and SV (5.07 mL; 95% CI 3.21, 6.92), and increase in shock index (0.03 bpm/mm Hg; 95% CI 0.01, 0.05). Clinically detectable changes (≥10%) in blood pressure and shock index were detectable in 15% and 5%, respectively. SV decreased by more than 10% in 40% of blood donors. No significant changes occurred in the control group.

CONCLUSION

Continuous noninvasive monitoring of SV may be superior to conventional indices (e.g., heart rate, blood pressure, or shock index) for early identification of acute blood loss. As an operator-independent and point-of-care technology, the SV whole body bio-impedance measurement may assist in accurate monitoring of potentially bleeding patients and early identification of hemorrhage.

The Compensatory Reserve Index Responds to Acute Hemodynamic Changes in Patients with Congenital Heart Disease: A Proof of Concept Study

Patients with congenital heart disease (CHD) who undergo cardiac procedures may become hemodynamically unstable. Predictive algorithms that utilize dense physiologic data may be useful. The compensatory reserve index (CRI) trends beat-to-beat progression from normovolemia (CRI = 1) to decompensation (CRI = 0) in hemorrhagic shock by continuously analyzing unique sets of features in the changing pulse photoplethysmogram (PPG) waveform. We sought to understand if the CRI accurately reflects changing hemodynamics during and after a cardiac procedure for patients with CHD. A transcatheter pulmonary valve replacement (TcPVR) model was used because left ventricular stroke volume decreases upon sizing balloon occlusion of the right ventricular outflow tract (RVOT) and increases after successful valve placement. A single-center, prospective cohort study was performed. The CRI was continuously measured to determine the change in CRI before and after RVOT occlusion and successful TcPVR. Twenty-six subjects were enrolled with a median age of 19 (interquartile range (IQR) 13-29) years. The mean (± standard deviation) CRI decreased from 0.66 ± 0.15 1-min before balloon inflation to 0.53 ± 0.16 (p = 0.03) 1-min after balloon deflation. The mean CRI increased from a pre-valve mean CRI of 0.63 [95% confidence interval (CI) 0.56-0.70] to 0.77 (95% CI 0.71-0.83) after successful TcPVR. In this study, the CRI accurately reflected acute hemodynamic changes associated with TcPVR. Further research is justified to determine if the CRI can be useful as an early warning tool in patients with CHD at risk for decompensation during and after cardiac procedures.

Cognitive and physical performance are well preserved following standard blood donation: A noninferiority, randomized clinical trial

BACKGROUND

A walking blood bank (WBB) refers to the use of fellow combatants for battlefield blood donation. This requires pretesting combatants for infectious diseases and blood type. A fundamental prerequisite for this technique is that the donating soldier will suffer minimal physiological and mental impact. The purpose of the current study is to assess the effect of blood shedding on battlefield performance.

METHODS

This is a double-blind randomized control trial. Forty Israel Defense Forces combatants volunteered for the study. Participants underwent baseline evaluation, including repeated measurement of vital signs, cognitive evaluation, physical evaluation, and a strenuous shooting test. Three weeks after the baseline evaluation, subjects were randomized to either blood donation or the control group. For blinding purposes, all subjects underwent venous catheterization for the duration of a blood donation. Repeated vital signs and function evaluation were then performed.

RESULTS

Thirty-six patients were available for randomization. Baseline measurements were similar for both groups. Mean strenuous shooting score was 80.5 ± 9.5 for the control group and 82 ± 6.6 for the test group (p = 0.58). No clinically or statistically significant differences were found in tests designed to evaluate cognitive performance or physical functions. Vital signs taken multiple times were also similar between the test and control groups.

CONCLUSIONS

Executive, cognitive, and physical functions were well preserved after blood donation. This study supports the hypothesis that a WBB does not decrease donor combat performance. The categorical prohibition of physical exercise following blood donation might need to be reconsidered in both military and civilian populations.

The compensatory reserve: potential for accurate individualized goal-directed whole blood resuscitation

Hemorrhagic shock can be mitigated by timely and accurate resuscitation designed to restore adequate delivery of oxygen (DO₂ ). Current doctrine of using systolic blood pressure (SBP) as a guide for resuscitation can be associated with increased morbidity. The compensatory reserve measurement (CRM) is a novel vital sign based on the recognition that the sum of all mechanisms that contribute to the compensatory response to hemorrhage reside in features of the arterial pulse waveform. CRM can be assessed continuously and non-invasively in real time. Compared to standard vital signs, CRM provides an early, as well as more sensitive and specific, indicator of patient hemorrhagic status since the activation of compensatory mechanisms occurs immediately at the onset of blood loss. Recent data obtained from our laboratory experiments on non-human primates have demonstrated that CRM is linearly related to DO₂ during controlled progressive hemorrhage and subsequent whole blood resuscitation. We used this relationship to determine that the time of hemodynamic decompensation (i.e., CRM = 0%) is defined by a critical DO₂ at approximately 5.3 mL O₂ ∙kg^-1 ∙min^-1 . We also demonstrated that a target CRM of 35% during whole blood resuscitation only required replacement of 40% of the total blood volume loss to adequately sustain a DO₂ more than 50% (i.e., 8.1 mL O₂ ∙kg^-1 ∙min^-1 ) above critical DO₂ (i.e., threshold for decompensated shock) while maintaining hypotensive resuscitation (i.e., SBP at ~90 mmHg). Consistent with our hypothesis, specific values of CRM can be used to accurately maintain DO₂ thresholds above critical DO₂ , avoiding the onset of hemorrhagic shock with whole blood resuscitation.

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.

Low postnatal CRI values are associated with the need for ECMO in newborns with CDH

INTRODUCTION

Accurate, real-time technology is needed to predict which newborns with congenital diaphragmatic hernia (CDH) will require ECMO. The Compensatory Reserve Index (CRI) is a noninvasive monitoring technology that continuously trends an individual's capacity to compensate from normovolemia (CRI = 1) to decompensation (CRI = 0). We hypothesized that postnatal CRI values would be lower in CDH newborns that required ECMO than those who did not require ECMO.

METHODS

Newborns with a CDH were prospectively monitored with a CipherOx® CRI M1 device. We compared CRI values from delivery to ECMO (ECMO group) versus delivery to clinical stabilization (non-ECMO group).

RESULTS

Postnatal CRI values were available from 26 newborns. Eight underwent ECMO within 33 h of delivery, and median CRI prior to ECMO was 0.068 (IQR: 0.057, 0.078). Eighteen did not require ECMO. Median CRI from birth to 48 h was 0.112 (IQR: 0.082, 0.15). CRI values were significantly lower in newborns that required ECMO versus those who did not (p = 0.0035). Postnatal CRI had the highest AUC (0.85) compared to other prenatal prognostic measures.

CONCLUSION

Humans from newborns to adults share elemental features of the pulsatile waveform that are associated with progression to decompensation. CRI may be helpful when deciding when to initiate ECMO.

LEVEL OF EVIDENCE

Level III.

TYPE OF STUDY

Diagnostic test.

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.

Detecting central hypovolemia in simulated hypovolemic shock by automated feature extraction with principal component analysis

Assessment of the volume status by blood pressure (BP) monitoring is difficult, since baroreflex control of BP makes it insensitive to blood loss up to about one liter. We hypothesized that a machine learning model recognizes the progression of central hypovolemia toward presyncope by extracting information of the noninvasive blood pressure waveform parametrized through principal component analysis. This was tested in healthy volunteers exposed to simulated hemorrhage by lower body negative pressure (LBNP).

Fifty‐six healthy volunteers were subjected to progressive central hypovolemia. A support vector machine was trained on the blood pressure waveform. Three classes of progressive stages of hypovolemia were defined. The model was optimized for the number of principal components and regularization parameter for penalizing misclassification (cost): C. Model performance was expressed as accuracy, mean squared error (MSE), and kappa statistic (inter‐rater agreement).

Forty‐six subjects developed presyncope of which 41 showed an increase in model classification severity from baseline to presyncope. In five of the remaining nine subjects (1 was excluded) it stagnated. Classification of samples during baseline and end‐stage LBNP had the highest accuracy (95% and 50%, respectively). Baseline and first stage of LBNP demonstrated the lowest MSE (0.01 respectively 0.32). Model MSE and accuracy did not improve for C values exceeding 0.01. Adding more than five principal components did not further improve accuracy or MSE. Increment in kappa halted after 10 principal components had been added. Automated feature extraction of the blood pressure waveform allows modeling of progressive hypovolemia with a support vector machine. The model distinguishes classes between baseline and presyncope.

Trauma Hemostasis and Oxygenation Research Network position paper on the role of hypotensive resuscitation as part of remote damage control resuscitation

The Trauma Hemostasis and Oxygenation Research (THOR) Network has developed a consensus statement on the role of permissive hypotension in remote damage control resuscitation (RDCR). A summary of the evidence on permissive hypotension follows the THOR Network position on the topic. In RDCR, the burden of time in the care of the patients suffering from noncompressible hemorrhage affects outcomes. Despite the lack of published evidence, and based on clinical experience and expertise, it is the THOR Network's opinion that the increase in prehospital time leads to an increased burden of shock, which poses a greater risk to the patient than the risk of rebleeding due to slightly increased blood pressure, especially when blood products are available as part of prehospital resuscitation.The THOR Network's consensus statement is, "In a casualty with life-threatening hemorrhage, shock should be reversed as soon as possible using a blood-based HR fluid. Whole blood is preferred to blood components. As a part of this HR, the initial systolic blood pressure target should be 100 mm Hg. In RDCR, it is vital for higher echelon care providers to receive a casualty with sufficient physiologic reserve to survive definitive surgical hemostasis and aggressive resuscitation. The combined use of blood-based resuscitation and limiting systolic blood pressure is believed to be effective in promoting hemostasis and reversing shock".

Compensatory Reserve Index: Performance of A Novel Monitoring Technology to Identify the Bleeding Trauma Patient

INTRODUCTION

Hemorrhage is one of the most substantial causes of death after traumatic injury. Standard measures, including systolic blood pressure (SBP), are poor surrogate indicators of physiologic compromise until compensatory mechanisms have been overwhelmed. Compensatory Reserve Index (CRI) is a novel monitoring technology with the ability to assess physiologic reserve. We hypothesized CRI would be a better predictor of physiologic compromise secondary to hemorrhage than traditional vital signs.

METHODS

A prospective observational study of 89 subjects meeting trauma center activation criteria at a single level I trauma center was conducted from October 2015 to February 2016. Data collected included demographics, SBP, heart rate, and requirement for hemorrhage-associated, life-saving intervention (LSI) (i.e., operation or angiography for hemorrhage, local or tourniquet control of external bleeding, and transfusion >2 units PRBC). Receiver-operator characteristic (ROC) curves were formulated and appropriate thresholds were calculated to compare relative value of the metrics for predictive modeling.

RESULTS

For predicting hemorrhage-related LSI, CRI demonstrated a sensitivity of 83% and a negative predictive value (NPV) of 91% as compared with SBP with a sensitivity to detect hemorrhage of 26% (P < 0.05) and an NPV of 78%. ROC curves generated from admission CRI and SBP measures demonstrated values of 0.83 and 0.62, respectively. CRI identified significant hemorrhage requiring potentially life-saving therapy more reliably than SBP (P < 0.05).

CONCLUSION

The CRI device demonstrated superior capacity over systolic blood pressure in predicting the need for posttraumatic hemorrhage intervention in the acute resuscitation phase after injury.

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.

Blood pressure variability, heart functionality, and left ventricular tissue alterations in a protocol of severe hemorrhagic shock and resuscitation

Autonomic control of blood pressure (BP) and heart rate (HR) is crucial during bleeding and hemorrhagic shock (HS) to compensate for hypotension and hypoxia. Previous works have observed that at the point of hemodynamic decompensation a marked suppression of BP and HR variability occurs, leading to irreversible shock. We hypothesized that recovery of the autonomic control may be decisive for effective resuscitation, along with restoration of mean BP. We computed cardiovascular indexes of baroreflex sensitivity and BP and HR variability by analyzing hemodynamic recordings collected from five pigs during a protocol of severe hemorrhage and resuscitation; three pigs were sham-treated controls. Moreover, we assessed the effects of severe hemorrhage on heart functionality by integrating the hemodynamic findings with measures of plasma high-sensitivity cardiac troponin T and metabolite concentrations in left ventricular (LV) tissue. Resuscitation was performed with fluids and norepinephrine and then by reinfusion of shed blood. After first resuscitation, mean BP reached the target value, but cardiovascular indexes were not fully restored, hinting at a partial recovery of the autonomic mechanisms. Moreover, cardiac troponins were still elevated, suggesting a persistent myocardial sufferance. After blood reinfusion all the indexes returned to baseline. In the harvested heart, LV metabolic profile confirmed the acute stress condition sensed by the cardiomyocytes. Variability indexes and baroreflex trends can be valuable tools to evaluate the severity of HS, and they may represent a more useful end point for resuscitation in combination with standard measures such as mean values and biological measures.

NEW & NOTEWORTHY Autonomic control of blood pressure was highly impaired during hemorrhagic shock, and it was not completely recovered after resuscitation despite global restoration of mean pressures. Moreover, a persistent myocardial sufferance emerged from measured cardiac troponin T and metabolite concentrations of left ventricular tissue. We highlight the importance of combining global mean values and biological markers with measures of variability and autonomic control for a better characterization of the effectiveness of the resuscitation strategy.

Machine Learning for Predicting Outcomes in Trauma

To date, there are no reviews on machine learning (ML) for predicting outcomes in trauma. Consequently, it remains unclear as to how ML-based prediction models compare in the triage and assessment of trauma patients. The objective of this review was to survey and identify studies involving ML for predicting outcomes in trauma, with the hypothesis that models predicting similar outcomes may share common features but the performance of ML in these studies will differ greatly. MEDLINE and other databases were searched for studies involving trauma and ML. Sixty-five observational studies involving ML for the prediction of trauma outcomes met inclusion criteria. In total 2,433,180 patients were included in the studies. The studies focused on prediction of the following outcome measures: survival/mortality (n = 34), morbidity/shock/hemorrhage (n = 12), hospital length of stay (n = 7), hospital admission/triage (n = 6), traumatic brain injury (n = 4), life-saving interventions (n = 5), post-traumatic stress disorder (n = 4), and transfusion (n = 1). Six studies were prospective observational studies. Of the 65 studies, 33 used artificial neural networks for prediction. Importantly, most studies demonstrated the benefits of ML models. However, algorithm performance was assessed differently by different authors. Sensitivity-specificity gap values varied greatly from 0.035 to 0.927. Notably, studies shared many features for model development. A common ML feature base may be determined for predicting outcomes in trauma. However, the impact of ML will require further validation in prospective observational studies and randomized clinical trials, establishment of common performance criteria, and high-quality evidence about clinical and economic impacts before ML can be widely accepted in practice.

Development of a Non-invasive Cerebrovascular Status Algorithm to Estimate Cerebral Perfusion Pressure and Intracranial Pressure in a Porcine Model of Focal Brain Injury

Background

New tools for diagnosis, monitoring, and treatment of elevated intracranial pressure (ICP) or compromised cerebral perfusion pressure (CPP) are urgently needed to improve outcomes after brain injury. Previous success in applying advanced data analytics to build precision monitors based on large, noisy sensor datasets suggested applying the same approach to monitor cerebrovascular status. In these experiments, a new algorithm was developed to estimate ICP and CPP using the arterial pressure waveform.

Methods

Sixty-five porcine subjects were subjected to a focal brain injury to simulate a mass lesion with elevated ICP. The arterial pressure waveform and the measured ICP from these subjects during injury and treatment were then utilized to develop and calibrate an ICP and CPP estimation algorithm. These estimation algorithms were then subsequently evaluated on 14 new subjects.

Results

The root mean square difference between actual ICP and estimated ICP was 2.0961 mmHg. The root mean square difference between the actual CPP and the estimated CPP was 2.6828 mmHg.

Conclusion

A novel ICP or CPP monitor based on the arterial pressure signal produced a very close approximation to actual measured ICP and CPP and warrants further evaluation.

The Compensatory Reserve For Early and Accurate Prediction Of Hemodynamic Compromise: A Review of the Underlying Physiology

Shock is deadly and unpredictable if it is not recognized and treated in early stages of hemorrhage. Unfortunately, measurements of standard vital signs that are displayed on current medical monitors fail to provide accurate or early indicators of shock because of physiological mechanisms that effectively compensate for blood loss. As a result of new insights provided by the latest research on the physiology of shock using human experimental models of controlled hemorrhage, it is now recognized that measurement of the body's reserve to compensate for reduced circulating blood volume is the single most important indicator for early and accurate assessment of shock. We have called this function the "compensatory reserve," which can be accurately assessed by real-time measurements of changes in the features of the arterial waveform. In this paper, the physiology underlying the development and evaluation of a new noninvasive technology that allows for real-time measurement of the compensatory reserve will be reviewed, with its clinical implications for earlier and more accurate prediction of shock.