Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

ABSTRACT

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

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Non-contact cardiopulmonary signal monitoring based on magnetic eddy current induction

The magnetic eddy current induction method has become an excellent solution for building home cardiopulmonary monitoring systems because of its non-contact and unobtrusive characteristics, but it has problems such as low precision and complex extraction of cardiopulmonary signals. Therefore, this paper designs a magnetic eddy current sensing system based on a Field Programmable Gate Array that can realize simultaneous real-time monitoring of cardiopulmonary signals. This system adopts a magnetic eddy current sensor design scheme that can improve the amount of cardiopulmonary information in the sensing signal. In addition, it uses a signal acquisition scheme that combines an inductance-to-digital converter (LDC) and oversampling technology to improve the resolution and signal-to-noise ratio of the sensing signal. Moreover, an optimized adaptive discrete wavelet transform algorithm is proposed in this system, which can realize the effective separation and extraction of cardiopulmonary signals in different respiration states. Comparing this system with the medical monitor, the cardiopulmonary signals obtained by the two have good consistency in the time-frequency domain. Under low motion, respiration rate and heart rate detected by this system are within the confidence interval of the 95% limit of agreement; the relative errors are less than 2.63% and 1.37%, respectively; and the accuracy rates are greater than 99.30% and 99.60%, respectively. In addition, an experiment with an asthmatic patient showed that the system still has good detection performance under pathological conditions and can monitor abnormal conditions such as coughing.

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.

Sensor technology usage in orthopedic trauma

Medicine in general is quickly transitioning to a digital presence. Orthopaedic surgery is also being impacted by the tenets of digital health but there are also direct efforts with trauma surgery. Sensors are the pen and paper of the next wave of data acquisition. Orthopaedic trauma can and will be part of this new wave of medicine. Early sensor products that are now coming to market, or are in early development, will directly change the way we think about surgical diagnosis and outcomes. Sensor development for biometrics is already here. Wellness devices, pressure, temperature, and other parameters are already being measured. Data acquisition and analysis is going to be a fruitful addition to our research armamentarium with the volume of information now available. A combination of broadband internet, micro electrical machine systems (MEMS), and new wireless communication standards is driving this new wave of medicine. The Internet of Things (IoT) [1] now has a subset which is the Internet of Medical Devices [2-5] permitting a much more in-depth dive into patient procedures and outcomes. IoT devices are now being used to enable remote health monitoring, in hospital treatment, and guide therapies. This article reviews current sensor technology that looks to impact trauma care.

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

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

Feature Importance Analysis for Compensatory Reserve to Predict Hemorrhagic Shock

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

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.

Military Applications of Machine Learning: A Bibliometric Perspective

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.

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.

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

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

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

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

Central Hypovolemia Detection During Environmental Stress-A Role for Artificial Intelligence?

The first step to exercise is preceded by the required assumption of the upright body position, which itself involves physical activity. The gravitational displacement of blood from the chest to the lower parts of the body elicits a fall in central blood volume (CBV), which corresponds to the fraction of thoracic blood volume directly available to the left ventricle. The reduction in CBV and stroke volume (SV) in response to postural stress, post-exercise, or to blood loss results in reduced left ventricular filling, which may manifest as orthostatic intolerance. When termination of exercise removes the leg muscle pump function, CBV is no longer maintained. The resulting imbalance between a reduced cardiac output (CO) and a still enhanced peripheral vascular conductance may provoke post-exercise hypotension (PEH). Instruments that quantify CBV are not readily available and to express which magnitude of the CBV in a healthy subject should remains difficult. In the physiological laboratory, the CBV can be modified by making use of postural stressors, such as lower body "negative" or sub-atmospheric pressure (LBNP) or passive head-up tilt (HUT), while quantifying relevant biomedical parameters of blood flow and oxygenation. Several approaches, such as wearable sensors and advanced machine-learning techniques, have been followed in an attempt to improve methodologies for better prediction of outcomes and to guide treatment in civil patients and on the battlefield. In the recent decade, efforts have been made to develop algorithms and apply artificial intelligence (AI) in the field of hemodynamic monitoring. Advances in quantifying and monitoring CBV during environmental stress from exercise to hemorrhage and understanding the analogy between postural stress and central hypovolemia during anesthesia offer great relevance for healthy subjects and clinical populations.

Scientific Developments and New Technological Trajectories in Sensor Research

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

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.

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

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

Automatic Measurement of the Carotid Blood Flow for Wearable Sensors: A Pilot Study

The assessment of the velocity of blood flowing in the carotid, in modern clinical practice, represents an important exam performed both in emergency situations and as part of scheduled screenings. It is typically performed by an expert sonographer who operates a complex and costly clinical echograph. Unfortunately, in developing countries, in rural areas, and even in crowded modern cities, the access to this exam can be limited by the lack of suitable personnel and ultrasound equipment. The recent availability of low-cost, handheld devices has contributed to solving part of the problem, but a wide access to the exam is still hampered by the lack of expert sonographers. In this work, an automated procedure is presented with the hope that, in the near future, it can be integrated into a low-cost, handheld instrument that is also suitable for self-measurement, for example, as can be done today with the finger oximeter. The operator should only place the probe on the neck, transversally with respect to the common tract of the carotid. The system, in real-time, automatically locates the vessel lumen, places the sample volume, and performs an angle-corrected velocity measurement of the common carotid artery peak velocity. In this study, the method was implemented for testing on the ULA-OP 256 scanner. Experiments on flow phantoms and volunteers show a performance in sample volume placement similar to that achieved by expert operators, and an accuracy and repeatability of 3.2% and 4.5%, respectively.

Thermoplasmonic effect onto Toad physiology signals by plasmonic microchip structure

Cardiovascular diseases are considered as the leading cause of death and almost 80% of deaths from this disease are developed in poor and less developed countries where early detection facilities are less available, along with overlooking the importance of screening. In other words, real-time monitoring of the physiological signals using flexible and wearable biosensors plays an important role in human life style. Thus, the present study aims to propose two dimensional flexible and wearable gold covered plasmonic samples as a physiological signal recorder, in which chips with nano array of resonant nanowire patterns performing in an integrated platform of plasmonic devices. The produced surface plasmon waves in our main chip were paired with an electric wave from the heart pulse and it use for recording and detecting the heartbeat of a toad with high accuracy. This measurement was performed in normal state and under external laser heating process to check the ability of signal recording and also thermoplasmonic effect onto the toad's heart signal. Our results show that our sensor was enough sensitive for detection while raising the body temperature of the toad and changing its heart rate as flatting T and P waves by thermoplasmonic effect.

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

INTRODUCTION

We conducted a prospective observational study on 205 trauma patients at a level I trauma facility to test the hypothesis that a compensatory reserve measurement (CRM) would identify higher risk for progression to shock and/or need a life-saving interventions (LSIs) earlier than systolic blood pressure (SBP) and blood lactate (LAC).

METHODS

A composite outcome metric included blood transfusion, procedural LSI, and mortality. Discrete measures assessed as abnormal (ab) were SBP <90 mmHg, CRM <60%, and LAC >2.0. A graded categorization of shock was defined as: no shock (normal [n] SBP [n-SBP], n-CRM, n-LAC); sub-clinical shock (ab-CRM, n-SBP, n-LAC); occult shock (n-SBP, ab-CRM, ab-LAC); or overt shock (ab-SBP, ab-CRM, ab-LAC).

RESULTS

Three patients displayed overt shock, 53 displayed sub-clinical shock, and 149 displayed no shock. After incorporating lactate into the analysis, 86 patients demonstrated no shock, 25 were classified as sub-clinical shock, 91 were classified as occult shock, and 3 were characterized as overt shock. Each shock subcategory revealed a graded increase requiring LSI and transfusion. Initial CRM was associated with progression to shock (odds ratio = 0.97; p < .001) at an earlier time than SBP or LAC.

CONCLUSIONS

Initial CRM uncovers a clinically relevant subset of patients who are not detected by SBP and LAC. Our results suggest CRM could be used to more expeditiously identify injured patients likely to deteriorate to shock, with requirements for blood transfusion or procedural LSI.

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

BACKGROUND

The Compensatory Reserve Measurement (CRM) is a novel method used to provide early assessment of shock based on arterial wave form morphology changes. We hypothesized that (1) CRM would be significantly lower in those trauma patients who received life-saving interventions compared with those not receiving interventions, and (2) CRM in patients who received interventions would recover after the intervention was performed.

STUDY DESIGN AND METHODS

We captured vital signs along with analog arterial waveform data from trauma patients meeting major activation criteria using a prospective study design. Study team members tracked interventions throughout their emergency department stay.

RESULTS

Ninety subjects met inclusion with 13 receiving a blood product and 10 a major airway intervention. Most trauma was blunt (69%) with motor vehicle collisions making up the largest proportion (37%) of injury mechanism. Patients receiving blood products had lower CRM values just prior to administration versus those who did not (50% versus 58%, p = .045), and lower systolic pressure (SBP; 95 versus 123 mmHg, p = .005), diastolic (DBP; 62 versus 79, p = .007), and mean arterial pressure (MAP; 75 versus 95, p = .006), and a higher pulse rate (HR; 101 versus 89 bpm, p = .039). Patients receiving an airway intervention had lower CRM values just prior to administration versus those who did not (48% versus 58%, p = .062); however, SBP, DBP, MAP, and HR were not statistically distinguishable (p ≥ .645).

CONCLUSIONS

Our results support our hypotheses that the CRM distinguished those patients who received blood or an airway intervention from those who did not, and increased appropriately after interventions were performed.

Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study

BACKGROUND

Noninvasive and cuffless approaches to monitor blood pressure (BP), in light of their convenience and accuracy, have paved the way toward remote screening and management of hypertension. However, existing noninvasive methodologies, which operate on mechanical, electrical, and optical sensing modalities, have not been thoroughly evaluated in demographically and racially diverse populations. Thus, the potential accuracy of these technologies in populations where they could have the greatest impact has not been sufficiently addressed. This presents challenges in clinical translation due to concerns about perpetuating existing health disparities.

OBJECTIVE

In this paper, we aim to present findings on the feasibility of a cuffless, wrist-worn, pulse transit time (PTT)-based device for monitoring BP in a diverse population.

METHODS

We recruited a diverse population through a collaborative effort with a nonprofit organization working with medically underserved areas in Georgia. We used our custom, multimodal, wrist-worn device to measure the PTT through seismocardiography, as the proximal timing reference, and photoplethysmography, as the distal timing reference. In addition, we created a novel data-driven beat-selection algorithm to reduce noise and improve the robustness of the method. We compared the wearable PTT measurements with those from a finger-cuff continuous BP device over the course of several perturbations used to modulate BP.

RESULTS

Our PTT-based wrist-worn device accurately monitored diastolic blood pressure (DBP) and mean arterial pressure (MAP) in a diverse population (N=44 participants) with a mean absolute difference of 2.90 mm Hg and 3.39 mm Hg for DBP and MAP, respectively, after calibration. Meanwhile, the mean absolute difference of our systolic BP estimation was 5.36 mm Hg, a grade B classification based on the Institute for Electronics and Electrical Engineers standard. We have further demonstrated the ability of our device to capture the commonly observed demographic differences in underlying arterial stiffness.

CONCLUSIONS

Accurate DBP and MAP estimation, along with grade B systolic BP estimation, using a convenient wearable device can empower users and facilitate remote BP monitoring in medically underserved areas, thus providing widespread hypertension screening and management for health equity.