Predictors of hemodynamic decompensation in progressive hypovolemia: Compensatory reserve versus heart rate variability

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.

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.

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.

Prehospital Hemorrhage Assessment Criteria: A Concise Review

OBJECTIVE

Early assessment of the clinical status of trauma patients is crucial for guiding the treatment strategy, and it requires a rapid and systematic approach. The aim of this report is to critically review the assessment parameters currently used in the prehospital setting to quantify blood loss in trauma.

DATA SOURCES

Studies regarding hemorrhagic shock in trauma were pooled from PubMed, EMBASE, and Cochrane databases using key words such as "hemorrhagic shock," "vital signs evaluation," "trauma," "blood loss," and "emergency medical service," alone or combined.

STUDY SELECTION

Articles published since 2009 in English and Italian were considered eligible if containing data on assessment parameters in blood loss in adults.

DATA EXTRACTION

Sixteen articles matching the inclusion criteria were considered in our study.

DATA SYNTHESIS

Current prehospital assessment measures lack precise correlation with blood loss.

CONCLUSIONS

Traditional assessment parameters such as heart rate, systolic blood pressure, shock index, and Glasgow Coma Scale score often lag in providing accurate blood loss assessment. The current literature supports the need for a noninvasive, continuously monitored assessment parameter to identify early shock in the prehospital setting.

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.

Unstable Hemodynamics is not Always Predictive of Failed Nonoperative Management in Blunt Splenic Injury

BACKGROUND

The advanced technology of interventional radiology may contribute to a rapid and timely angioembolization for hemostasis. We hypothesized that unstable hemodynamics is no longer an absolute contraindication of nonoperative management (NOM) in blunt splenic injury patients using rapid angioembolization.

METHODS

From January 2009 to December 2019, blunt splenic injury patients with unstable hemodynamics [initial pulse >120 beats/min or systolic blood pressure <90 mm Hg] were included. Either emergency surgery or angioembolization was performed for hemostasis because of their unstable status. The characteristics of patients who underwent angioembolization or surgery were compared in each group (all patients, patients with hypotension, patients without response to resuscitation and hypotensive patients without response to resuscitation).

RESULTS

A total of 73 patients were included in the current study. With respect to all patients, 68.5% (N = 50) of patients underwent NOM with angioembolization for hemostasis. Patients who underwent angioembolization for hemostasis had a significantly lower base deficit (5.3 ± 3.8 vs. 8.3 ± 5.2 mmol/L, p = 0.006) and a higher proportion of response to resuscitation (82.0% vs. 30.4%, p < 0.001) than did patients who underwent surgery. However, there was no significant difference in the proportion of hypotension (58.0% vs. 65.2%, p = 0.558) between these two groups. There were 44 patients with hypotension, and the angioembolization could be performed in 65.9% (N = 29) of them. Patients who underwent angioembolization had a significantly higher proportion of response to resuscitation than did patients who underwent surgery (89.7% vs. 33.3%, p < 0.001). In hypotensive patients without response to resuscitation (N = 13), 23.1% (N = 3) of the patients underwent angioembolization successfully. There was no significant difference in time to hemostasis procedure between patients who underwent angioembolization or surgery (24.7 ± 2.1 vs. 26.3 ± 16.7 min, p = 0.769). The demographics, vital signs, blood transfusion amount, injury severity, mortality rate and length of stay of patients who underwent angioembolization were not significantly different from patients who underwent surgery in each group.

CONCLUSIONS

With a short preparation time of angioembolization, the NOM could be performed selectively for hemodynamically unstable patients with blunt splenic injury. The base deficit serves as an early detector of the requirement of surgical treatment.

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.