Initial Resuscitation and Management of the Hemodynamically Unstable Patient

Schwartz, David R.; Holcomb, John B.

doi:10.1007/978-3-319-42792-8_1

Cited by 3 publications

(2 citation statements)

References 136 publications

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“…To generate synthetic vital-sign data for this study, we created variable sets of hemorrhage and treatment scenarios within defined injury, time, and fluid resuscitation limits. We created casualties corresponding to Class II and III hemorrhage ( Schwartz and Holcomb, 2017 ), followed by a combination of tourniquet application and fluid transfusions commensurate with pre-hospital treatments documented in recent combat casualty care guidelines ( Voller et al, 2021 ). The terms “bleeding” and “hemorrhage” with respect to the CR model correspond to a loss of fluid at a specific fixed rate for a specific length of time; to stop the bleeding, a “tourniquet” can be applied, corresponding to the bleeding rate set to zero in the CR model.…”

Section: Methodsmentioning

confidence: 99%

“… The range of bleeding times and blood-volume losses used to create different hemorrhage scenarios. The pentagon-shaded area defines the range of bleeding parameters used to create Class II and III hemorrhage cases ( Schwartz and Holcomb, 2017 ) for this study, compatible with bleeding times of 5–15 min and blood-volume losses of 0.75–2.00 L. The blue dash-dotted edge of the pentagon represents the 0.22 L/min maximum rate of hemorrhage for these scenarios. …”

Section: Methodsmentioning

confidence: 99%

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AI algorithm for personalized resource allocation and treatment of hemorrhage casualties

Jin,

Frock,

Nagaraja

et al. 2024

Front. Physiol.

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A deep neural network-based artificial intelligence (AI) model was assessed for its utility in predicting vital signs of hemorrhage patients and optimizing the management of fluid resuscitation in mass casualties. With the use of a cardio-respiratory computational model to generate synthetic data of hemorrhage casualties, an application was created where a limited data stream (the initial 10 min of vital-sign monitoring) could be used to predict the outcomes of different fluid resuscitation allocations 60 min into the future. The predicted outcomes were then used to select the optimal resuscitation allocation for various simulated mass-casualty scenarios. This allowed the assessment of the potential benefits of using an allocation method based on personalized predictions of future vital signs versus a static population-based method that only uses currently available vital-sign information. The theoretical benefits of this approach included up to 46% additional casualties restored to healthy vital signs and a 119% increase in fluid-utilization efficiency. Although the study is not immune from limitations associated with synthetic data under specific assumptions, the work demonstrated the potential for incorporating neural network-based AI technologies in hemorrhage detection and treatment. The simulated injury and treatment scenarios used delineated possible benefits and opportunities available for using AI in pre-hospital trauma care. The greatest benefit of this technology lies in its ability to provide personalized interventions that optimize clinical outcomes under resource-limited conditions, such as in civilian or military mass-casualty events, involving moderate and severe hemorrhage.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

AI algorithm for personalized resource allocation and treatment of hemorrhage casualties

Jin,

Frock,

Nagaraja

et al. 2024

Front. Physiol.

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Coagulopathy in Cirrhotic Patients: Evaluation and Management

Smith

2019

Multidisciplinary Approaches to Common Surgical Problems

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Baixas doses de hidrocortisona possuem ação protetora no choque hemorrágico em ratos

Tavares¹,

Monteiro²,

Feijó³

et al. 2017

PRMJ

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CirurgiaEste é um artigo publicado em acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições desde que o trabalho original seja corretamente citado. Descritores: hidrocortisona; solução salina hipertônica; choque hemorrágico; isquemia; reperfusão; inflamação; gasometria; ratos. ARTIGO DESummary Purpose: To evaluate the hydrocortisone low-dose effects in hemorrhagic shock in rats. Methods: Twenty male Wistar rats were randomly distributed into 04 groups: Sham Group (GS); Shock group (CG), animals subjected to hemorrhagic shock; Group Hypertonic Saline 7.5% (GSH 7.5%), with volume replacement of saline solution at 7.5% after shock; Hydrocortisone Group (GH), animals received hydrocortisone after shock. Arterial blood gas samples and variations of the mean arterial blood pressure (MAP) were analyzed before the shock induction and after treatments. Intestinal samples were collected for histological analysis. Results: MAP was statistical significantly increased after treatment of the shock in GSH 7,5% (p=0.0001) and GH (p=0.0058) when compared to the CG. Regarding the arterial blood gas analysis, hydrocortisone increased pH of arterial blood in comparison to the GC (P=0.0372), lowered PaCO2 (p=0.0008), maintained higher levels of bicarbonate ions (p=0.0395) and lower levels of lactate (p=0.002). GH (p=0.02), as GSH7.5% (p=0.0147), showed less histological damage than the GC. Conclusion: Low dosages of hydrocortisone had protective effects against hemorrhagic shock in rats.

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Initial Resuscitation and Management of the Hemodynamically Unstable Patient

Cited by 3 publications

References 136 publications

AI algorithm for personalized resource allocation and treatment of hemorrhage casualties

AI algorithm for personalized resource allocation and treatment of hemorrhage casualties

Coagulopathy in Cirrhotic Patients: Evaluation and Management

Baixas doses de hidrocortisona possuem ação protetora no choque hemorrágico em ratos

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