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.

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.

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.

Tracking Peripheral Artery Motion and Vascular Resistance With a Multimodal Wearable Sensor Under Pressure Perturbations.

The status of peripheral arteries is known to be a key physiological indicator of the body's response to both acute and chronic medical conditions. In this paper, peripheral artery deformation is tracked by wearable photoplethysmograph (PPG) and piezo-electric (polyvinylidene difluoride, PVDF) sensors, under pressure-varying cuff. A simple mechanical model for the local artery and intervening tissue captures broad features present in the PPG and PVDF signals on multiple swine subjects, with respect to varying cuff pressure. These behaviors provide insight into the robustness of cardiovascular property identification by noninvasive wearable sensing. This is found to help refine noninvasive blood pressure measurements and estimation of systemic vascular resistance (SVR) using selected features of sensor amplitude versus applied pressure.

Trans-Ocular Brain Impedance Indices Predict Pressure Reactivity Index Changes in a Porcine Model of Hypotension and Cerebral Autoregulation Perturbation.

BACKGROUND: Cerebrovascular autoregulation (CA) is a protective mechanism that enables the cerebral vasculature to automodulate tone in response to changes in cerebral perfusion pressure to ensure constant levels of cerebral blood flow (CBF) and oxygen delivery. CA can be impaired after neurological injury and contributes to secondary brain injury. In this study, we report novel impedance indices using trans-ocular brain impedance (TOBI) during controlled systemic hemorrhage and hypotension to assess CA in comparison with pressure reactivity index (PRx). METHODS: Yorkshire swine were instrumented to record intracranial pressure (ICP), mean arterial pressure (MAP), and CBF. TOBI was recorded using electrocardiographic electrodes placed on the closed eyelids. Impedance changes (dz) were recorded in response to introducing an alternating current (0.4 mA) through the electrodes. MAP, ICP, and CBF were also measured. Animals were subjected to a controlled hemorrhage to remove 30-40% of each animal's total blood volume over 25-35 min. Hemorrhage was titrated to reach an MAP of approximately 35 mm Hg and end-tidal carbon dioxide above 28 mm Hg. PRx was calculated as a moving Pearson correlation between MAP and ICP. TOBI indices were calculated as the amplitude of the respiratory-induced changes in dz. DZx was calculated as a moving Pearson correlation between dz and MAP. TOBI indices (dz and DZx) were compared with hemodynamic indicators and PRx. RESULTS: dz was shown to be highly correlated with MAP, ICP, cerebral perfusion pressure, and CBF (r = - 0.823, - 0.723, - 0.813, and - 0.726), respectively (p < 0.0001). During hemorrhage, cerebral perfusion pressure and CBF had a mean percent decrease (standard deviation) from baseline of - 54.2% (12.5%) and - 28.3% (14.7%), respectively, whereas dz increased by 277% (268%). Receiver operator characteristics and precision-recall curves demonstrated high predictive performance of DZx when compared with PRx with an area under the curve above 0.82 and 0.89 for receiver operator characteristic and precision-recall curves, respectively, with high sensitivity and positive predictive power. CONCLUSIONS: TOBI indices appear to track changes in PRx and hemodynamics that affect CA during hemorrhage-induced hypotension. TOBI may offer a suitable, less invasive surrogate to PRx for monitoring and assessing CA.

Evidence-Based and Clinically Relevant Outcomes for Hemorrhage Control Trauma Trials.

OBJECTIVE: To address the clinical and regulatory challenges of optimal primary endpoints for bleeding patients by developing consensus-based recommendations for primary clinical outcomes for pivotal trials in patients within 6 categories of significant bleeding, (1) traumatic injury, (2) intracranial hemorrhage, (3) cardiac surgery, (4) gastrointestinal hemorrhage, (5) inherited bleeding disorders, and (6) hypoproliferative thrombocytopenia. BACKGROUND: A standardized primary outcome in clinical trials evaluating hemostatic products and strategies for the treatment of clinically significant bleeding will facilitate the conduct, interpretation, and translation into clinical practice of hemostasis research and support alignment among funders, investigators, clinicians, and regulators. METHODS: An international panel of experts was convened by the National Heart Lung and Blood Institute and the United States Department of Defense on September 23 and 24, 2019. For patients suffering hemorrhagic shock, the 26 trauma working-group members met for almost a year, utilizing biweekly phone conferences and then an in-person meeting, evaluating the strengths and weaknesses of previous high quality studies. The selection of the recommended primary outcome was guided by goals of patient-centeredness, expected or demonstrated sensitivity to beneficial treatment effects, biologic plausibility, clinical and logistical feasibility, and broad applicability. CONCLUSIONS: For patients suffering hemorrhagic shock, and especially from truncal hemorrhage, the recommended primary outcome was 3 to 6-hour all-cause mortality, chosen to coincide with the physiology of hemorrhagic death and to avoid bias from competing risks. Particular attention was recommended to injury and treatment time, as well as robust assessments of multiple safety related outcomes.

Civilian walking blood bank emergency preparedness plan.

BACKGROUND: The current global pandemic has created unprecedented challenges in the blood supply network. Given the recent shortages, there must be a civilian plan for massively bleeding patients when there are no blood products on the shelf. Recognizing that the time to death in bleeding patients is less than 2 h, timely resupply from unaffected locations is not possible. One solution is to transfuse emergency untested whole blood (EUWB), similar to the extensive military experience fine-tuned over the last 19 years. While this concept is anathema in current civilian transfusion practice, it seems prudent to have a vetted plan in place. METHODS AND MATERIALS: During the early stages of the 2020 global pandemic, a multidisciplinary and international group of clinicians with broad experience in transfusion medicine communicated routinely. The result is a planning document that provides both background information and a high-level guide on how to emergently deliver EUWB for patients who would otherwise die of hemorrhage. RESULTS AND CONCLUSIONS: Similar plans have been utilized in remote locations, both on the battlefield and in civilian practice. The proposed recommendations are designed to provide high-level guidance for experienced blood bankers, transfusion experts, clinicians, and health authorities. Like with all emergency preparedness, it is always better to have a well-thought-out and trained plan in place, rather than trying to develop a hasty plan in the midst of a disaster. We need to prevent the potential for empty shelves and bleeding patients dying for lack of blood.

Using redox potential as a feasible marker for banked blood quality and the state of oxidative stress in stored red blood cells.

BACKGROUND: Stored red blood cells (RBCs) may undergo oxidative stress over time, with functional changes affecting oxygen delivery. Central to these changes are oxidation-reduction (redox) reactions and redox potential (RP) that must be maintained for cell function. RP imbalance can lead to oxidative stress that may contribute to storage lesions. This study's purpose was to identify changes in RP over time in banked RBCs, and among RBCs of similar age. METHODS: Multiple random RBC segments from RBC units were tested (n = 32), ranging in age from 5 to 40 days, at 5-day intervals. RP was recorded by measuring open circuit potential of RBCs using nanoporous gold electrodes with Ag/AgCl reference. RP measures were also performed on peripheral venous blood from 10 healthy volunteers. RP measures were compared between RBC groups, and with volunteer blood. RESULTS: Stored RBCs show time-dependent RP increases. There were significant differences in Day 5 RP compared to all other groups (p ≤ 0.005), Day 10-15 vs. ages ≥ Day 20 (p ≤ 0.025), Day 20-25 vs. Day 40 (p = 0.039), and all groups compared to healthy volunteers. RP became more positive over time suggesting ongoing oxidation as RBCs age; however, storage time alone was not predictive of RP measured in a particular unit/segment. CONCLUSIONS: There are significant differences in RP between freshly stored RBCs and all others, with RP becoming more positive over time. However, storage time alone does not predict RP, indicating RP screening may be an important measure of RBC oxidative stress and serve as an RBC quality marker.

Detection of Low Cardiac Index using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks.

This study investigated the use of a wearable ring made of polyvinylidene fluoride film to identify a low cardiac index (≤2 L/min). The waveform generated by the ring contains patterns that may be indicative of low blood pressure and/or high vascular resistance, both of which are markers of a low cardiac index. In particular, the waveform contains reflection waves whose timing and amplitude are correlated with pulse travel time and vascular resistance, respectively. Hence, the pattern of the waveform is expected to vary in response to changes in blood pressure and vascular resistance. By analyzing the morphology of the waveform, our aim was to create a tool to identify patients with low cardiac index. This was done using a convolutional neural network which was trained on data from animal models. The model was then tested on waveforms that were collected from patients undergoing pulmonary artery catheterization. The results indicate high accuracy in classifying patients with a low cardiac index, achieving an area under the receiver operating characteristics and precision-recall curves of 0.88 and 0.71, respectively.

Trans-ocular brain impedance index for assessment of cerebral autoregulation in a porcine model of cerebral hemodynamic perturbation.

Cerebrovascular autoregulation (CA) is often impaired following traumatic brain injury. Established technologies and metrics used to assess CA are invasive and conducive for measurement, but not for continuous monitoring. We developed a trans-ocular brain impedance (TOBI) method that may provide non-invasive and continuous indices to assess CA. In this study, we monitored impedance metrics such as respiratory-induced impedance amplitude changes (dz) as well as a novel impedance index (DZx), which is a moving Pearson correlation between mean arterial pressure (MAP) and dz. Yorkshire swine were instrumented to continuously record ICP, MAP, and cerebral blood flow (CBF). TOBI was recorded by placement of standard ECG electrodes on closed eyelids and connected to a data acquisition system. MAP, ICP and CBF were manipulated utilizing an intravenous vasopressor challenge. TOBI indices (dz and DZx) were compared to the hemodynamic indicators as well as pressure reactivity index (PRx). During the vasopressor challenge, dz was highly correlated with ICP, CPP, and CBF (r = < - 0.49, p < 0.0001). ICP, CPP, and CBF had a mean percent increase (standard deviation) from baseline of 29(23.2)%, 70(25)%, and 37(72.6)% respectively while dz decreased by 31(15.6)%. Receiver operator curve test showed high predictive performance of DZx when compared to PRx with area under the curve above 0.86, with high sensitivity and specificity. Impedance indices appear to track changes in PRx and hemodynamics that affect cerebral autoregulation. TOBI may be a suitable less invasive surrogate to PRx and capable of tracking cerebral autoregulation.

Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers.

BACKGROUND: To introduce the Hemorrhage Intensive Severity and Survivability (HISS) score, based on the fusion of multi-biomarker data; glucose, lactate, pH, potassium, and oxygen tension, to serve as a patient-specific attribute in hemorrhagic trauma. MATERIALS AND METHODS: One hundred instances of Sensible Fictitious Rationalized Patient (SFRP) data were synthetically generated and the HISS score assigned by five clinically active physician experts (100 [5]). The HISS score stratifies the criticality of the trauma patient as; low(0), guarded(1), elevated(2), high(3) and severe(4). Standard classifier algorithms; linear support vector machine (SVM-L), multi-class ensemble bagged decision tree (EBDT), artificial neural network with bayesian regularization (ANN:BR) and possibility rule-based using function approximation (PRBF) were evaluated for their potential to similarly classify and predict a HISS score. RESULTS: SVM-L, EBDT, ANN:BR and PRBF generated score predictions with testing accuracies (majority vote) corresponding to 0.91 ± 0.06, 0.93 ± 0.04, 0.92 ± 0.07, and 0.92 ± 0.03, respectively, with no statistically significant difference (p > 0.05). Targeted accuracies of 0.99 and 0.999 could be achieved with SFRP data size and clinical expert scores of 147[7](0.99) and 154[9](0.999), respectively. CONCLUSIONS: The predictions of the data-driven model in conjunction with an adjunct multi-analyte biosensor intended for point-of-care continual monitoring of trauma patients, can aid in patient stratification and triage decision-making.

Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution.

When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern. To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n = 133,089 encounters). We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. When analyzing PICTURE on the testing set using all observations within a hospital encounter (event rate = 3.4%), PICTURE had an area under the receiver operating characteristic curve (AUROC) of 0.83 and an adjusted (event rate = 4%) area under the precision-recall curve (AUPR) of 0.27, while the next best tested method-regularized logistic regression-had an AUROC of 0.80 and an adjusted AUPR of 0.22. To ensure system interpretability, we applied a state-of-the-art prediction explainer that provided a ranked list of features contributing most to the prediction. Though it is currently difficult to compare machine learning-based early warning systems, a rudimentary comparison with published scores demonstrated that PICTURE is on par with state-of-the-art machine learning systems. To facilitate more robust comparisons and development of early warning systems in the future, we have released our variational autoencoder's code and weights so researchers can (a) test their models on data similar to our institution and (b) make their own synthetic datasets.

Use of Tandem Perimortem Cesarean Section and Open-Chest Cardiac Massage in the Resuscitation of Peripartum Cardiomyopathy Cardiac Arrest.

Cardiac arrest and resuscitation of the pregnant woman at gestational term is rare. Depending on the circumstances of cardiac arrest and its timing, options are limited for allowing successful resuscitation of both mother and neonate. Herein, we describe the use of tandem perimortem cesarean section and thoracotomy for open-chest cardiac massage in a young woman with newly diagnosed peripartum cardiomyopathy. We used goal-directed resuscitation including diagnostic ultrasonography and capnography to assist in decision making and successfully resuscitated both mother and neonate to hospital discharge without discernable long-term complications.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device.

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

Simplifying Arterial Coupling in Microsurgery-A Preclinical Assessment of an Everter Device to Aid with Arterial Anastomosis.

BACKGROUND: A novel arterial everter device was engineered to simplify microvascular coupling of arteries by reliably securing the stiff, muscular wall of arteries over coupler pins. We compare microvascular coupling with the everter device to manual suturing for arterial anastomoses in a live large animal model. MATERIALS AND METHODS: In this preliminary study, bilateral external femoral arteries of five male swine were exposed and sharply divided. Arteries were anastomosed using either interrupted sutures (n = 5) or the everter device and Synovis Coupler (n = 5). The efficiency in engaging coupler pins, the time taken to perform the anastomosis, and vessel patency immediately post-op and at 1-week postanastomosis were evaluated. Vessel wall injury and luminal stenosis were compared between groups using histomorphometric analyses. RESULTS: On an average, 80% of coupler pins engaged the vessel walls after a single pass of the everter. The average time to perform the anastomosis was significantly less when using the everter/coupler compared with manual suturing (6:35 minutes versus 25:09 minutes, p < 0.001). Immediately post-op, 100% patency was observed in both groups. At 1 week post-op, four of five (80%) of coupled arteries and all five (100%) of hand-sewn arteries were patent. The degree of arterial wall injury, neointimal formation, and luminal stenosis for patent arteries were similar between groups. CONCLUSIONS: Successful arterial anastomoses using the everter device with the Synovis Coupler was easier and significantly more efficient when compared with a standard hand-sewn technique. Both techniques had acceptable patency rates and similar effects on the vessel wall and intima.

Viscosity Measurements Using Microfluidic Droplet Length.

Viscosity measurements have a wide range of applications from industrial chemical production to medical diagnosis. In this work, we have developed a simple droplet-based, water-in-oil continuous viscometer capable of measuring viscosity changes in 10 s or less and consuming a total sample volume of less than 1 µL/h. The viscometer employs a flow-focusing geometry and generates droplets under constant pressure. The length of the droplets (Ld) is highly correlated to the aqueous-phase viscosity (µaq) at high ratios of aqueous-inlet to oil-inlet pressure (AIP/OIP), yielding a linear relationship between µaq and 1/(Ld - Lc) where Lc is the minimal obtainable droplet length and approximately equals to the width of the droplet-generating channel. Theoretical analysis verifies this linear relationship, and the resulting equations can be used to optimize the design of the device such as the channel width, depth, and length. The viscometer can be used for Newtonian fluids and, by accurately calculating the shear rate, for non-Newtonian fluids such as Boger fluids and shear thinning fluids. In these latter cases, the shear rates depend on the velocity of the aqueous phase and can be adjusted by varying the input pressures. The applicable range of viscosity measurements depends on the oil-phase viscosity (µoil), and viscosities within the range of 0.01-10 µoil can be measured reliably with less than 5% error.

"Blood failure" time to view blood as an organ: how oxygen debt contributes to blood failure and its implications for remote damage control resuscitation.

Hemorrhagic shock is both a local and systemic disorder. In the context of systemic effects, blood loss may lead to levels of reduced oxygen delivery (DO2 ) sufficient to cause tissue ischemia. Similar to other physiologic debts such as sleep, it is not possible to incur a significant oxygen debt and suffer no consequences for lack of timely repayment. While the linkage between oxygen debt and traditional organ failure (renal, hepatic, lung, and circulation) has been long recognized, we should consider failure in two additional linked and very dynamic organ systems, the endothelium and blood. These systems are very sensitive to oxygen debt and at risk for failing, having further implications on all other organ systems. The degree of damage to the endothelium is largely modulated by the degree of oxygen debt. Thus hypoperfusion is believed to begin a cascade of events leading to acute traumatic coagulopathy (ATC). This combination of oxygen debt driven endothelial damage and ATC might be considered collectively as "blood failure" due to the highly connected networks between these drivers. This article presents the implications of oxygen debt for remote damage control resuscitation strategies, such as permissive hypotension and hemostatic resuscitation. We review the impact of whole blood resuscitation and red blood cell efficacy in mitigation of oxygen debt. At last, this article recognizes the need for simple and durable, lightweight equipment that can detect the adequacy of tissue DO2 and thus patient needs for resuscitative care. Point-of-care lactate measuring may be a predictive tool for identifying high-risk trauma patients and occult shock because it provides information beyond that of vital signs and mechanism of injury as it may help predict the level of oxygen debt accumulation and need for resuscitation. Serial measurements may also be valuable as a tool in guiding resuscitative efforts.

Oxygen saturation monitoring using resonance Raman spectroscopy.

BACKGROUND: The knowledge of hemoglobin oxygen saturation (SO2) and tissue oxygenation is critical to identify the presence of shock and therapeutic options. The resonance vibrational enhancement of hemoglobin allows measurement of oxy- and deoxy species of hemoglobin and resonance Raman spectroscopy (RRS-StO2) has been successfully used to measure aggregate microvascular oxygenation. We tested the hypothesis that noninvasive oxygen saturation measured by RRS-StO2 could serve as surrogate of systemic central venous SO2. METHODS: In anesthetized rats, measurements of RRS-StO2 made in oral mucosa, skin, muscle, and liver were compared with measurements of central venous SO2 using traditional multi-wavelength oximetry. Various oxygenation levels were obtained using a stepwise hemorrhage while over 100 paired blood samples and Raman-based measurements were performed. The relationships between RRS-StO2 and clinically important systemic blood parameters were also evaluated. RRS-StO2 measurements were made in 3-mm diameter tissue areas using a microvascular oximeter and a handheld probe. RESULTS: Significant correlations were found between venous SO2 and RRS-StO2 measurements made in the oral mucosa (r = 0.913, P < 0.001), skin (r = 0.499, P < 0.01), and liver (r = 0.611, P < 0.05). The mean difference between sublingual RRS-StO2 and blood sample SO2 values was 5.4 ± 1.6%. Sublingual RRS-StO2 also correlated with lactate (r = 0.909, P < 0.01), potassium (r = 0.757, P < 0.01), and pH (r = 0.703, P < 0.05). CONCLUSIONS: Raman-based oxygen saturation is a promising technique for the noninvasive evaluation of oxygenation in skin, thin tissues, and solid organs. Under certain conditions, sublingual RRS-StO2 measurements correlate with central venous SO2.