Hemorrhagic Shock and the Microvasculature

Filho, Ivo P. Torres

doi:10.1002/cphy.c170006

Cited by 44 publications

(38 citation statements)

References 471 publications

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“…Although the anesthesia and analgesia applied in all models had an impact, the heart rate declined in all models throughout the experiment [13]. HS was kept pressure controlled, volume controlled or uncontrolled [51]. For resuscitation, there exist several different protocols, including re-transfusion of shed blood, supplementary infusion of Ringers lactate, saline, hydroxyethyl starch (HES) or any combination thereof [52].…”

Section: Hemorrhagic Shock (Hs)mentioning

confidence: 99%

Modeling trauma in rats: similarities to humans and potential pitfalls to consider

et al. 2019

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Trauma is the leading cause of mortality in humans below the age of 40. Patients injured by accidents frequently suffer severe multiple trauma, which is life-threatening and leads to death in many cases. In multiply injured patients, thoracic trauma constitutes the third most common cause of mortality after abdominal injury and head trauma. Furthermore, 40–50% of all trauma-related deaths within the first 48 h after hospital admission result from uncontrolled hemorrhage. Physical trauma and hemorrhage are frequently associated with complex pathophysiological and immunological responses. To develop a greater understanding of the mechanisms of single and/or multiple trauma, reliable and reproducible animal models, fulfilling the ethical 3 R’s criteria (Replacement, Reduction and Refinement), established by Russell and Burch in ‘The Principles of Human Experimental Technique’ (published 1959), are required. These should reflect both the complex pathophysiological and the immunological alterations induced by trauma, with the objective to translate the findings to the human situation, providing new clinical treatment approaches for patients affected by severe trauma. Small animal models are the most frequently used in trauma research. Rattus norvegicus was the first mammalian species domesticated for scientific research, dating back to 1830. To date, there exist numerous well-established procedures to mimic different forms of injury patterns in rats, animals that are uncomplicated in handling and housing. Nevertheless, there are some physiological and genetic differences between humans and rats, which should be carefully considered when rats are chosen as a model organism. The aim of this review is to illustrate the advantages as well as the disadvantages of rat models, which should be considered in trauma research when selecting an appropriate in vivo model. Being the most common and important models in trauma research, this review focuses on hemorrhagic shock, blunt chest trauma, bone fracture, skin and soft-tissue trauma, burns, traumatic brain injury and polytrauma.

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Section: Hemorrhagic Shock (Hs)mentioning

confidence: 99%

Modeling trauma in rats: similarities to humans and potential pitfalls to consider

et al. 2019

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“…Most of our understanding regarding hemorrhagic shock-induced endothelial hyperpermeability is derived from animal studies or from the evaluation of plasma markers involved in inflammation and endothelial activation and injury. Inflammation, glycocalyx degradation, mitochondrial dysfunction and disruption of endothelial junctions are mechanisms contributing to increased endothelial permeability [6,7]. However, the direct effect of plasma from patients following traumatic hemorrhagic shock on endothelial permeability and its course remains unknown.…”

Section: Introductionmentioning

confidence: 99%

In vitro endothelial hyperpermeability occurs early following traumatic hemorrhagic shock

Leeuwen

Naumann

Dekker

et al. 2020

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BACKGROUND: Endothelial hyperpermeability is suggested to play a role in the development of microcirculatory perfusion disturbances and organ failure following hemorrhagic shock, but evidence is limited. OBJECTIVE: To study the effect of plasma from traumatic hemorrhagic shock patients on in vitro endothelial barrier function. METHODS: Plasma from traumatic hemorrhagic shock patients was obtained at the emergency department (ED), the intensive care unit (ICU), 24 h after ICU admission and from controls (n = 8). Sublingual microcirculatory perfusion was measured using incident dark field videomicroscopy at matching time points. Using electric cell-substrate impedance sensing, the effects of plasma exposure on in vitro endothelial barrier function of human endothelial cells were assessed. RESULTS: Plasma from traumatic hemorrhagic shock patients collected at ED admission induced a 19% loss of in vitro endothelial resistance compared to plasma from controls (p < 0.001). This loss was due to reduced cell-cell contacts (p < 0.01). Plasma withdrawn at later time points did not affect endothelial barrier function (p > 0.99). Interestingly, in vitro endothelial resistance showed a positive association with in vivo microcirculatory perfusion (r = 0.56, p < 0.01). CONCLUSIONS: Plasma from traumatic hemorrhagic shock patients obtained following ED admission, but not at later stages, induced in vitro endothelial hyperpermeability. This coincided with in vivo microcirculatory perfusion disturbances.

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“…27,28 Corresponding to our observation, hemorrhagic shock (HS) has been widely illustrated to be significantly correlated with the occurrence of ATC in both clinical and animal experiments. [29][30][31][32] Shock Index was interpreted to be a favorable predictor of massive hemorrhage in both prehospital and ED settings, when SI !0.9 postinjury can recognize patients with critical bleeding. 27 As a primary indicator of hemodynamic monitoring, DBP may be predictive for patient mortality in cardiogenic shock and/or septic shock.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning–Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization

Pan

et al. 2020

Clin Appl Thromb Hemost

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Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presents early after trauma and impacts approximately 30% of severely injured patients who are admitted to emergency departments (EDs). Given that conventional coagulation indicators usually require more than 1 hour after admission to yield results-a limitation that frequently prevents the ability for clinicians to make appropriate interventions during the optimal therapeutic window-it is clearly of vital importance to develop prediction models that can rapidly identify ATC; such models would also facilitate ancillary resource management and clinical decision support. Using the critical care Emergency Rescue Database and further collected data in ED, a total of 1385 patients were analyzed and cases with initial international normalized ratio (INR) values >1.5 upon admission to the ED met the defined diagnostic criteria for ATC; nontraumatic conditions with potentially disordered coagulation systems were excluded. A total of 818 individuals were collected from Emergency Rescue Database as derivation cohorts, then were split 7:3 into training and test data sets. A Pearson correlation matrix was used to initially identify likely key clinical features associated with ATC, and analysis of data distributions was undertaken prior to the selection of suitable modeling tools. Both machine learning (random forest) and traditional logistic regression were deployed for prediction modeling of ATC. After the model was built, another 587 patients were further collected in ED as validation cohorts. The ATC prediction models incorporated red blood cell count, Shock Index, base excess, lactate, diastolic blood pressure, and potential of hydrogen. Of 818 trauma patients filtered from the database, 747 (91.3%) patients did not present ATC (INR 1.5) and 71 (8.7%) patients had ATC (INR > 1.5) upon admission to the ED. Compared to the logistic regression model, the model based on the random forest algorithm showed better accuracy (94.0%, 95% confidence interval [CI]: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95), precision (93.3%, 95% CI: 0.914-0.948 to 93.1%, 95% CI: 0.912-0.946), F1 score (93.4%, 95% CI: 0.915-0.949 to 92%, 95% CI: 0.9-0.937), and recall score (94.0%, 95% CI: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95) but yielded lower area under the receiver operating characteristic curve (AU-ROC) (0.810, 95% CI: 0.673-0.918 to 0.849, 95% CI: 0.732-0.944) for predicting ATC in the trauma patients. The result is similar in the validation cohort. The values for classification accuracy, precision, F1 score, and recall score of random forest model were 0.916, 0.907, 0.901, and 0.917, while the AU-ROC was 0.830. The values for classification accuracy, precision, F1 score, and recall score of logistic regression model were 0.905, 0.887, 0.883, and 0.905, while the AU-ROC was 0.858. We developed and validated a

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Hemorrhagic Shock and the Microvasculature

Cited by 44 publications

References 471 publications

Modeling trauma in rats: similarities to humans and potential pitfalls to consider

Modeling trauma in rats: similarities to humans and potential pitfalls to consider

In vitro endothelial hyperpermeability occurs early following traumatic hemorrhagic shock

A Machine Learning–Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization

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