Life Over Limb: Why Not Both? Revisiting Tourniquet Practices Based on Lessons Learned From the War in Ukraine.

The use of tourniquets for life-threatening limb hemorrhage is standard of care in military and civilian medicine. The United States (U.S.) Department of Defense (DoD) Committee on Tactical Combat Casualty Care (CoTCCC) guidelines, as part of the Joint Trauma System, support the application of tourniquets within a structured system reliant on highly trained medics and expeditious evacuation. Current practices by entities such as the DoD and North Atlantic Treaty Organization (NATO) are supported by evidence collected in counter-insurgency operations and other conflicts in which transport times to care rarely went beyond one hour, and casualty rates and tactical situations rarely exceeded capabilities. Tourniquets cause complications when misused or utilized for prolonged durations, and in near-peer or peer-peer conflicts, contested airspace and the impact of high-attrition warfare may increase time to definitive care and limit training resources. We present a series of cases from the war in Ukraine that suggest tourniquet practices are contributing to complications such as limb amputation, overall morbidity and mortality, and increased burden on the medical system. We discuss factors that contribute to this phenomenon and propose interventions for use in current and future similar contexts, with the ultimate goal of reducing morbidity and mortality.

Blood failure: traumatic hemorrhage and the interconnections between oxygen debt, endotheliopathy, and coagulopathy.

This review explores the concept of "blood failure" in traumatic injury, which arises from the interplay of oxygen debt, the endotheliopathy of trauma (EoT), and acute traumatic coagulopathy (ATC). Traumatic hemorrhage leads to the accumulation of oxygen debt, which can further exacerbate hemorrhage by triggering a cascade of events when severe. Such events include EoT, characterized by endothelial glycocalyx damage, and ATC, involving platelet dysfunction, fibrinogen depletion, and dysregulated fibrinolysis. To manage blood failure effectively, a multifaceted approach is crucial. Damage control resuscitation strategies such as use of permissive hypotension, early hemorrhage control, and aggressive transfusion of blood products including whole blood aim to minimize oxygen debt and promote its repayment while addressing endothelial damage and coagulation. Transfusions of red blood cells, plasma, and platelets, as well as the use of tranexamic acid, play key roles in hemostasis and countering ATC. Whole blood, whether fresh or cold-stored, is emerging as a promising option to address multiple needs in traumatic hemorrhage. This review underscores the intricate relationships between oxygen debt, EoT, and ATC and highlights the importance of comprehensive, integrated strategies in the management of traumatic hemorrhage to prevent blood failure. A multidisciplinary approach is essential to address these interconnected factors effectively and to improve patient outcomes.

Automated Gas Chromatography Peak Alignment: A Deep Learning Approach using Greedy Optimization and Simulation.

The clinical significance of volatile organic compounds (VOC) in detecting diseases has been established over the past decades. Gas chromatography (GC) devices enable the measurement of these VOCs. Chromatographic peak alignment is one of the important yet challenging steps in analyzing chromatogram signals. Traditional semi-automated alignment algorithms require manual intervention by an operator which is slow, expensive and inconsistent. A pipeline is proposed to train a deep-learning model from artificial chromatograms simulated from a small, annotated dataset, and a postprocessing step based on greedy optimization to align the signals.Clinical Relevance- Breath VOCs have been shown to have a significant diagnostic power for various diseases including asthma, acute respiratory distress syndrome and COVID-19. Automatic analysis of chromatograms can lead to improvements in the diagnosis and management of such diseases.

A Comprehensive Comparison of Six Publicly Available Algorithms for Localization of QRS Complex on Electrocardiograph.

The QRS complex is the most prominent feature of the electrocardiogram (ECG) that is used as a marker to identify the cardiac cycles. Identification of QRS complex locations enables arrhythmia detection and heart rate variability estimation. Therefore, accurate and consistent localization of the QRS complex is an important component of automated ECG analysis which is necessary for the early detection of cardiovascular diseases. This study evaluates the performance of six popular publicly available QRS complex detection methods on a large dataset of over half a million ECGs in a diverse population of patients. We found that a deep-learning method that won first place in the 2019 Chinese physiological challenge (CPSC-1) outperforms the remaining five QRS complex detection methods with an F1 score of 98.8% and an absolute sdRR error of 5.5 ms. We also examined the stratified performance of the studied methods on various cardiac conditions. All six methods had a lower performance in the detection of QRS complexes in ECG signals of patients with pacemakers, complete atrioventricular block, or indeterminate cardiac axis. We also concluded that, in the presence of different cardiac conditions, CPSC-1 is more robust than Pan-Tompkins which is the most popular model for QRS complex detection. We expect that this study can potentially serve as a guide for researchers on the appropriate QRS detection method for their target applications.Clinical Relevance-This study highlights the overall performance of publicly available QRS detection algorithms in a large dataset of diverse patients. We showed that there are specific cardiac conditions that are associated with the poor performance of QRS detection algorithms and may adversely influence the performance of algorithms that rely on accurate and reliable QRS detection.

Percutaneous left ventricular assist devices in refractory cardiac arrest: The role of chest compressions.

Background: Recent studies describe an emerging role for percutaneous left ventricular assist devices such as Impella CP® as rescue therapy for refractory cardiac arrest. We hypothesized that the addition of mechanical chest compressions to percutaneous left ventricular assist device assisted CPR would improve hemodynamics by compressing the right ventricle and augmenting pulmonary blood flow and left ventricular filling. We performed a pilot study to test this hypothesis using a swine model of prolonged cardiac arrest. Methods: Eight Yorkshire swine were anesthetized, intubated, and instrumented for hemodynamic monitoring. They were subjected to untreated ventricular fibrillation for 5.75 (SD 2.90) minutes followed by mechanical chest compressions for a mean of 20.0 (SD 5.0) minutes before initiation of percutaneous left ventricular assist device. After percutaneous left ventricular assist device initiation, mechanical chest compressions was stopped (n = 4) or continued (n = 4). Defibrillation was attempted 4, 8 and 12 minutes after initiating percutaneous left ventricular assist device circulatory support. Results: The percutaneous left ventricular assist device + mechanical chest compressions group had significantly higher percutaneous left ventricular assist device flow prior to return of spontaneous heartbeat at four- and twelve-minutes after percutaneous left ventricular assist device initiation, and significantly higher end tidal CO2 at 4-minutes after percutaneous left ventricular assist device initiation, when compared with the percutaneous left ventricular assist device alone group. Carotid artery flow was not significantly different between the two groups. Conclusion: The addition of mechanical chest compressions to percutaneous left ventricular assist device support during cardiac arrest may generate higher percutaneous left ventricular assist device and carotid artery flow prior to return of spontaneous heartbeat compared to percutaneous left ventricular assist device alone. Further studies are needed to determine if this approach improves other hemodynamic parameters or outcomes after prolonged cardiac arrest.

Effect of percutaneous ventricular assisted device on post-cardiac arrest myocardial dysfunction in swine model with prolonged cardiac arrest.

BACKGROUND: It remains unclear if percutaneous left ventricular assist device (pLVAD) reduces post-cardiac arrest myocardial dysfunction. METHODS: This is a prespecified analysis of a subset of swine that achieved return of spontaneous circulation (ROSC) in a study comparing pLVAD, transient aortic occlusion (AO), or both during cardiopulmonary resuscitation (CPR). Devices were initiated after 24 minutes of ventricular fibrillation cardiac arrest (8 min no-flow and 16 min mechanical CPR). AO was discontinued post-ROSC, and pLVAD support or standard care were continued. Beginning 60 minutes post-ROSC, pLVAD support was weaned to < 1.0 L/min and subsequently removed at 240 minutes. The primary outcome was cardiac index (CI), stroke volume index (SVI), and left ventricular ejection fraction (LVEF) at 240 minutes post-ROSC. Data are shown as mean (standard error). RESULTS: Seventeen swine achieved ROSC without complication and were included in this analysis (pLVAD group, n = 11 and standard care group, n = 6). For the primary outcomes, the pLVAD group had significantly higher CI of 4.2(0.3) vs. 3.1(0.4) L/min/m2 (p = 0.043) and LVEF 60(3) vs. 49(4) % (p = 0.029) at 240 minutes after ROSC when compared with the standard care group, while SVI was not statistically significantly different (32[3] vs. 23[4] mL/min/m2, p = 0.054). During the first 60 minutes post-ROSC, the pLVAD group had significantly higher coronary perfusion pressure, lower LV stroke work index, and total pulmonary resistance index. CONCLUSION: These results suggest that early pLVAD support after ROSC is associated with better recovery myocardial function compared to standard care after prolonged cardiac arrest.

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.

Haemodynamic impact of aortic balloon occlusion combined with percutaneous left ventricular assist device during cardiopulmonary resuscitation in a swine model of cardiac arrest.

AIM: To investigate the effect of tandem use of transient balloon occlusion of the descending aorta (AO) and percutaneous left ventricular assist device (pl-VAD) during cardiopulmonary resuscitation in a large animal model of prolonged cardiac arrest. METHODS: Ventricular fibrillation was induced and left untreated for 8 minutes followed by 16 minutes of mechanical CPR (mCPR) in 24 swine, under general anesthesia. Animals were randomized to 3 treatment groups (n = 8 per group): A) pL-VAD (Impella CP®) B) pL-VAD+AO, and C) AO. Impella CP® and the aortic balloon catheter were inserted via the femoral arteries. mCPR was continued during treatment. Defibrillation was attempted 3 times starting at minute 28 and then every 4 minutes. Haemodynamic, cardiac function and blood gas measurements were recorded for up to 4 hours. RESULTS: Coronary perfusion pressure (CoPP) in the pL-VAD+AO Group increased by a mean (SD) of 29.2(13.94) mmHg compared to an increase of 7.1(12.08) and 7.1(5.95) mmHg for groups pL-VAD and AO respectively (p = 0.02). Similarly, cerebral perfusion pressure (CePP) in pL-VAD+AO increased by a mean (SD) of 23.6 (6.11), mmHg compared with 0.97 (9.07) and 6.9 (7.98) mmHg for the other two groups (p < 0.001). The rate of return of spontaneous heartbeat (ROSHB) was 87.5%, 75%, and 100% for pL-VAD+AO, pL-VAD, and AO. CONCLUSION: Combined AO and pL-VAD improved CPR hemodynamics compared to either intervention alone in this swine model of prolonged cardiac arrest.

Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome.

There is a growing gap between studies describing the capabilities of artificial intelligence (AI) diagnostic systems using deep learning versus efforts to investigate how or when to integrate AI systems into a real-world clinical practice to support physicians and improve diagnosis. To address this gap, we investigate four potential strategies for AI model deployment and physician collaboration to determine their potential impact on diagnostic accuracy. As a case study, we examine an AI model trained to identify findings of the acute respiratory distress syndrome (ARDS) on chest X-ray images. While this model outperforms physicians at identifying findings of ARDS, there are several reasons why fully automated ARDS detection may not be optimal nor feasible in practice. Among several collaboration strategies tested, we find that if the AI model first reviews the chest X-ray and defers to a physician if it is uncertain, this strategy achieves a higher diagnostic accuracy (0.869, 95% CI 0.835-0.903) compared to a strategy where a physician reviews a chest X-ray first and defers to an AI model if uncertain (0.824, 95% CI 0.781-0.862), or strategies where the physician reviews the chest X-ray alone (0.808, 95% CI 0.767-0.85) or the AI model reviews the chest X-ray alone (0.847, 95% CI 0.806-0.887). If the AI model reviews a chest X-ray first, this allows the AI system to make decisions for up to 79% of cases, letting physicians focus on the most challenging subsets of chest X-rays.

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems.

OBJECTIVES: Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. DESIGN: PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. SETTING: The general wards of two large hospitals, one an academic medical center and the other a community hospital. SUBJECTS: The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861-0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275-0.320) at the first hospital; AUROC 0.875 (0.851-0.902), AUPRC 0.339 (0.281-0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. CONCLUSIONS: Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant.

Importance: Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown. Objective: To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent. Design, Setting, and Participants: This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography. Main Outcomes and Measures: Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness. Results: Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy. Conclusions and Relevance: The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

Tandem use of gastroesophageal resuscitative occlusion of the aorta followed by resuscitative endovascular balloon occlusion of the aorta in a lethal liver laceration model.

BACKGROUND: Gastroesophageal resuscitative occlusion of the aorta (GROA) has been shown effective in creating zone II aortic occlusion capable of temporarily improving survival in animal models of lethal noncompressible torso hemorrhage. In this study, tandem application of GROA transitioning to resuscitative endovascular balloon occlusion of the aorta (REBOA) is explored to demonstrate feasibility as a potential point-of-injury bridge to more advanced care, using a swine model of lethal abdominal hemorrhage. METHODS: Swine (n = 19) were anesthetized, instrumented, and subjected to a combination of controlled and uncontrolled hemorrhage from a grade-V liver laceration. Animals were designated as intervention (n = 9; GROA to REBOA) or control (n = 10), for 60 minutes. Following intervention, devices were deactivated, and animals received blood and crystalloid resuscitation. Animals were monitored for 4 hours. RESULTS: Injury resulted in onset of class IV shock in all animals with a mean arterial pressure (SD) of 24.5 (4.11) mm Hg at the start of intervention. Nine of 10 controls died during the intervention period with a median (interquartile) survival time of 8.5 (9.25) minutes. All animals receiving the intervention survived both the 60-minute intervention period demonstrating a significant survival improvement ( p = 0.0007). Transition from GROA to REBOA was successful in all animals with a transition time ranging from 30 to 90 seconds. Mean arterial pressure significantly improved in animals receiving GROA to REBOA for the duration of intervention, regardless of the method of aortic occlusion, with a range of 70.9 (16.04) mm Hg to 101.1 (15.3) mm Hg. Additional hemodynamics, metrics of shock, and oxygenation remained stable during intervention. CONCLUSION: Less invasive technologies such as GROA may present an opportunity to control noncompressible torso hemorrhage more rapidly, with a subsequent transition to more advanced care such as REBOA.

Prediction of Postoperative Deterioration in Cardiac Surgery Patients Using Electronic Health Record and Physiologic Waveform Data.

BACKGROUND: Postoperative hemodynamic deterioration among cardiac surgical patients can indicate or lead to adverse outcomes. Whereas prediction models for such events using electronic health records or physiologic waveform data are previously described, their combined value remains incompletely defined. The authors hypothesized that models incorporating electronic health record and processed waveform signal data (electrocardiogram lead II, pulse plethysmography, arterial catheter tracing) would yield improved performance versus either modality alone. METHODS: Intensive care unit data were reviewed after elective adult cardiac surgical procedures at an academic center between 2013 and 2020. Model features included electronic health record features and physiologic waveforms. Tensor decomposition was used for waveform feature reduction. Machine learning-based prediction models included a 2013 to 2017 training set and a 2017 to 2020 temporal holdout test set. The primary outcome was a postoperative deterioration event, defined as a composite of low cardiac index of less than 2.0 ml min-1 m-2, mean arterial pressure of less than 55 mmHg sustained for 120 min or longer, new or escalated inotrope/vasopressor infusion, epinephrine bolus of 1 mg or more, or intensive care unit mortality. Prediction models analyzed data 8 h before events. RESULTS: Among 1,555 cases, 185 (12%) experienced 276 deterioration events, most commonly including low cardiac index (7.0% of patients), new inotrope (1.9%), and sustained hypotension (1.4%). The best performing model on the 2013 to 2017 training set yielded a C-statistic of 0.803 (95% CI, 0.799 to 0.807), although performance was substantially lower in the 2017 to 2020 test set (0.709, 0.705 to 0.712). Test set performance of the combined model was greater than corresponding models limited to solely electronic health record features (0.641; 95% CI, 0.637 to 0.646) or waveform features (0.697; 95% CI, 0.693 to 0.701). CONCLUSIONS: Clinical deterioration prediction models combining electronic health record data and waveform data were superior to either modality alone, and performance of combined models was primarily driven by waveform data. Decreased performance of prediction models during temporal validation may be explained by data set shift, a core challenge of healthcare prediction modeling.

Redox Potential Correlates with Changes in Metabolite Concentrations Attributable to Pathways Active in Oxidative Stress Response in Swine Traumatic Shock.

INTRODUCTION: Oxidation-reduction (redox) reactions, and the redox potential (RP) that must be maintained for proper cell function, lie at the heart of physiologic processes in critical illness. Imbalance in RP reflects systemic oxidative stress, and whole blood RP measures have been shown to correlate with oxygen debt level over time in swine traumatic shock. We hypothesize that RP measures reflect changing concentrations of metabolites involved in oxidative stress. To test this hypothesis, we compared blood and urine RP with concentrations of multiple metabolites in a swine traumatic shock model to identify meaningful RP-metabolite relationships. METHODS: Seven swine were subjected to traumatic shock. Mixed venous (MV) RP, urine RP, and concurrent MV and urine metabolite concentrations were assessed at baseline, max O 2 Debt (80 mL/kg), end resuscitation, and 2 h post-resuscitation. RP was measured at collection via open circuit potential using nanoporous gold electrodes with Ag/AgCl reference and a ParstatMC potentiostat. Metabolite concentrations were measured by quantitative 1 H-NMR spectroscopy. MV and urine RP were compared with time-matched metabolites across all swine. LASSO regression with leave-one-out cross validation was used to determine meaningful RP/metabolite relationships. Metabolites had to maintain magnitude and direction of coefficients across 6 or more swine to be considered as having a meaningful relationship. KEGG IDs of these metabolites were uploaded into Metscape for pathway identification and evaluation for physiologic function. RESULTS: Meaningful metabolite relationships (and mean coefficients across cross-validation folds) with MV RP included: choline (-6.27), ATP (-4.39), glycine (5.93), ADP (1.84), glucose (15.96), formate (-13.09), pyruvate (6.18), and taurine (-7.18). Relationships with urine RP were: betaine (4.81), urea (4.14), glycine (-2.97), taurine (10.32), 3-hydroxyisobutyrate (-7.67), N-phenylacetylglycine, PAG (-14.52), hippurate (12.89), and formate (-5.89). These meaningful metabolites were found to scavenge extracellular peroxide (pyruvate), inhibit ROS and activate cellular antioxidant defense (taurine), act as indicators of antioxidant mobilization against oxidative stress (glycine + PAG), and reflect renal hydroxyl radical trapping (hippurate), among other activities. CONCLUSIONS: Real-time RP measures demonstrate significant relationships with metabolites attributable to metabolic pathways involved in systemic responses to oxidative stress, as well as those involved in these processes. These data support RP measures as a feasible, biologically relevant marker of oxidative stress. As a direct measure of redox state, RP may be a useful biomarker and clinical tool in guiding diagnosis and therapy in states of increased oxidative stress and may offer value as a marker for organ injury in these states as well.

Detection of Hemodynamic Status Using an Analytic Based on an Electrocardiogram Lead Waveform.

OBJECTIVES: Delayed identification of hemodynamic deterioration remains a persistent issue for in-hospital patient care. Clinicians continue to rely on vital signs associated with tachycardia and hypotension to identify hemodynamically unstable patients. A novel, noninvasive technology, the Analytic for Hemodynamic Instability (AHI), uses only the continuous electrocardiogram (ECG) signal from a typical hospital multiparameter telemetry monitor to monitor hemodynamics. The intent of this study was to determine if AHI is able to predict hemodynamic instability without the need for continuous direct measurement of blood pressure. DESIGN: Retrospective cohort study. SETTING: Single quaternary care academic health system in Michigan. PATIENTS: Hospitalized adult patients between November 2019 and February 2020 undergoing continuous ECG and intra-arterial blood pressure monitoring in an intensive care setting. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One million two hundred fifty-two thousand seven hundred forty-two 5-minute windows of the analytic output were analyzed from 597 consecutive adult patients. AHI outputs were compared with vital sign indications of hemodynamic instability (heart rate > 100 beats/min, systolic blood pressure < 90 mm Hg, and shock index of > 1) in the same window. The observed sensitivity and specificity of AHI were 96.9% and 79.0%, respectively, with an area under the curve (AUC) of 0.90 for heart rate and systolic blood pressure. For the shock index analysis, AHI's sensitivity was 72.0% and specificity was 80.3% with an AUC of 0.81. CONCLUSIONS: The AHI-derived hemodynamic status appropriately detected the various gold standard indications of hemodynamic instability (hypotension, tachycardia and hypotension, and shock index > 1). AHI may provide continuous dynamic hemodynamic monitoring capabilities in patients who traditionally have intermittent static vital sign measurements.

Novel Negative Pressure Procedural Tent Reduces Aerosolized Particles in a Simulated Prehospital Setting.

BACKGROUND/OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has challenged the ability of Emergency Medical Services (EMS) providers to maintain personal safety during the treatment and transport of patients potentially infected. Increased rates of COVID-19 infection in EMS providers after patient care exposure, and notably after performing aerosol-generating procedures (AGPs), have been reported. With an already strained workforce seeing rising call volumes and increased risk for AGP-requiring patient presentations, development of novel devices for the protection of EMS providers is of great importance.Based on the concept of a negative pressure room, the AerosolVE BioDome is designed to encapsulate the patient and contain aerosolized infectious particles produced during AGPs, making the cabin of an EMS vehicle safer for providers. The objective of this study was to determine the efficacy and safety of the tent in mitigating simulated infectious particle spread in varied EMS transport platforms during AGP utilization. METHODS: Fifteen healthy volunteers were enrolled and distributed amongst three EMS vehicles: a ground ambulance, an aeromedical-configured helicopter, and an aeromedical-configured jet. Sodium chloride particles were used to simulate infectious particles and particle counts were obtained in numerous locations close to the tent and around the patient compartment. Counts near the tent were compared to ambient air with and without use of AGPs (non-rebreather mask, continuous positive airway pressure [CPAP] mask, and high-flow nasal cannula [HFNC]). RESULTS: For all transport platforms, with the tent fan off, the particle generator alone, and with all AGPs produced particle counts inside the tent significantly higher than ambient particle counts (P <.0001). With the tent fan powered on, particle counts near the tent, where EMS providers are expected to be located, showed no significant elevation compared to baseline ambient particle counts during the use of the particle generator alone or with use of any of the AGPs across all transport platforms. CONCLUSION: Development of devices to improve safety for EMS providers to allow for use of all available therapies to treat patients while reducing risk of communicable respiratory disease transmission is of paramount importance. The AerosolVE BioDome demonstrated efficacy in creating a negative pressure environment and workspace around the patient and provided significant filtration of simulated respiratory droplets, thus making the confined space of transport vehicles potentially safer for EMS personnel.

Breath analysis for detection and trajectory monitoring of acute respiratory distress syndrome in swine.

Despite the enormous impact on human health, acute respiratory distress syndrome (ARDS) is poorly defined, and its timely diagnosis is difficult, as is tracking the course of the syndrome. The objective of this pilot study was to explore the utility of breath collection and analysis methodologies to detect ARDS through changes in the volatile organic compound (VOC) profiles present in breath. Five male Yorkshire mix swine were studied and ARDS was induced using both direct and indirect lung injury. An automated portable gas chromatography device developed in-house was used for point of care breath analysis and to monitor swine breath hourly, starting from initiation of the experiment until the development of ARDS, which was adjudicated based on the Berlin criteria at the breath sampling points and confirmed by lung biopsy at the end of the experiment. A total of 67 breath samples (chromatograms) were collected and analysed. Through machine learning, principal component analysis and linear discrimination analysis, seven VOC biomarkers were identified that distinguished ARDS. These represent seven of the nine biomarkers found in our breath analysis study of human ARDS, corroborating our findings. We also demonstrated that breath analysis detects changes 1-6 h earlier than the clinical adjudication based on the Berlin criteria. The findings provide proof of concept that breath analysis can be used to identify early changes associated with ARDS pathogenesis in swine. Its clinical application could provide intensive care clinicians with a noninvasive diagnostic tool for early detection and continuous monitoring of ARDS.

Novel Negative Pressure Helmet Reduces Aerosolized Particles in a Simulated Prehospital Setting.

BACKGROUND/OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has created challenges in maintaining the safety of prehospital providers caring for patients. Reports have shown increased rates of Emergency Medical Services (EMS) provider infection with COVID-19 after patient care exposure, especially while utilizing aerosol-generating procedures (AGPs). Given the increased risk and rising call volumes for AGP-necessitating complaints, development of novel devices for the protection of EMS clinicians is of great importance.Drawn from the concept of the powered air purifying respirator (PAPR), the AerosolVE helmet creates a personal negative pressure space to contain aerosolized infectious particles produced by patients, making the cabin of an EMS vehicle safer for providers. The helmet was developed initially for use in hospitals and could be of significant use in the prehospital setting. The objective of this study was to determine the efficacy and safety of the helmet in mitigating simulated infectious particle spread in varied EMS transport platforms during AGP utilization. METHODS: Fifteen healthy volunteers were enrolled and distributed amongst three EMS vehicles: a ground ambulance, a medical helicopter, and a medical jet. Sodium chloride particles were used to simulate infectious particles, and particle counts were obtained in numerous locations close to the helmet and around the patient compartment. Counts near the helmet were compared to ambient air with and without use of AGPs (non-rebreather mask [NRB], continuous positive airway pressure mask [CPAP], and high-flow nasal cannula [HFNC]). RESULTS: Without the helmet fan on, the particle generator alone and with all AGPs produced particle counts inside the helmet significantly higher than ambient particle counts. With the fan on, there was no significant difference in particle counts around the helmet compared to baseline ambient particle counts. Particle counts at the filter exit averaged less than one despite markedly higher particle counts inside the helmet. CONCLUSION: Given the risk to EMS providers by communicable respiratory diseases, development of devices to improve safety while still enabling use of respiratory therapies is of paramount importance. The AerosolVE helmet demonstrated efficacy in creating a negative pressure environment and provided significant filtration of simulated respiratory droplets, thus making the confined space of transport vehicles potentially safer for EMS personnel.