Markers of Futile Resuscitation in Traumatic Hemorrhage: A Review of the Evidence and a Proposal for Futility Time-Outs during Massive Transfusion.

The reduction in the blood supply following the 2019 coronavirus pandemic has been exacerbated by the increased use of balanced resuscitation with blood components including whole blood in urban trauma centers. This reduction of the blood supply has diminished the ability of blood banks to maintain a constant supply to meet the demands associated with periodic surges of urban trauma resuscitation. This scarcity has highlighted the need for increased vigilance through blood product stewardship, particularly among severely bleeding trauma patients (SBTPs). This stewardship can be enhanced by the identification of reliable clinical and laboratory parameters which accurately indicate when massive transfusion is futile. Consequently, there has been a recent attempt to develop scoring systems in the prehospital and emergency department settings which include clinical, laboratory, and physiologic parameters and blood products per hour transfused as predictors of futile resuscitation. Defining futility in SBTPs, however, remains unclear, and there is only nascent literature which defines those criteria which reliably predict futility in SBTPs. The purpose of this review is to provide a focused examination of the literature in order to define reliable parameters of futility in SBTPs. The knowledge of these reliable parameters of futility may help define a foundation for drawing conclusions which will provide a clear roadmap for traumatologists when confronted with SBTPs who are candidates for the declaration of futility. Therefore, we systematically reviewed the literature regarding the definition of futile resuscitation for patients with trauma-induced hemorrhagic shock, and we propose a concise roadmap for clinicians to help them use well-defined clinical, laboratory, and viscoelastic parameters which can define futility.

Traumatic Brain Injury as an Independent Predictor of Futility in the Early Resuscitation of Patients in Hemorrhagic Shock.

This review explores the concept of futility timeouts and the use of traumatic brain injury (TBI) as an independent predictor of the futility of resuscitation efforts in severely bleeding trauma patients. The national blood supply shortage has been exacerbated by the lingering influence of the COVID-19 pandemic on the number of blood donors available, as well as by the adoption of balanced hemostatic resuscitation protocols (such as the increasing use of 1:1:1 packed red blood cells, plasma, and platelets) with and without early whole blood resuscitation. This has underscored the urgent need for reliable predictors of futile resuscitation (FR). As a result, clinical, radiologic, and laboratory bedside markers have emerged which can accurately predict FR in patients with severe trauma-induced hemorrhage, such as the Suspension of Transfusion and Other Procedures (STOP) criteria. However, the STOP criteria do not include markers for TBI severity or transfusion cut points despite these patients requiring large quantities of blood components in the STOP criteria validation cohort. Yet, guidelines for neuroprognosticating patients with TBI can require up to 72 h, which makes them less useful in the minutes and hours following initial presentation. We examine the impact of TBI on bleeding trauma patients, with a focus on those with coagulopathies associated with TBI. This review categorizes TBI into isolated TBI (iTBI), hemorrhagic isolated TBI (hiTBI), and polytraumatic TBI (ptTBI). Through an analysis of bedside parameters (such as the proposed STOP criteria), coagulation assays, markers for TBI severity, and transfusion cut points as markers of futilty, we suggest amendments to current guidelines and the development of more precise algorithms that incorporate prognostic indicators of severe TBI as an independent parameter for the early prediction of FR so as to optimize blood product allocation.

Brodifacoum contamination of synthetic cannabinoid causing unexplained coagulopathy in multiple trauma: A case report.

An 18-year-old female presented to the emergency department after a motor vehicle collision. Initial imaging revealed a liver laceration. Subsequent labs showed significantly elevated prothrombin time, international normalized ratio, and activated partial thromboplastin time. Thromboelastography demonstrated a flatline tracing. The patient denied use of anticoagulation but admitted to synthetic cannabinoid use. It was believed the patient had taken synthetic cannabinoid contaminated by brodifacoum. She was therefore given prothrombin complex concentrate and vitamin K with blood products. The patient underwent sequential embolization, laparotomy, thoracotomy, and repair of the vena cava with a shunt. Thirty minutes postoperatively, her coagulation tests and thromboelastography were much improved. Two and a half hours postoperatively, it was determined she had sustained non-survivable injuries. The patient experienced brain death due to prolonged hypotension as a result of hemorrhagic shock with bleeding exacerbated by brodifacoum. To our knowledge, this is the first case reported of a trauma-induced coagulopathy exacerbated by brodifacoum-contaminated synthetic cannabinoid. Her coagulopathy was clearly not due to trauma alone and contributed greatly to the difficulty in controlling hemorrhage. The synthetic cannabinoid-associated coagulopathy rendered her otherwise potentially survivable injuries fatal. Given the frequency of multiple trauma and the recent increase in the prevalence of synthetic cannabinoid, it can be expected that the incidence of trauma complicated by synthetic cannabinoid-associated coagulopathy will increase in the near future. For patients that present with prolonged prothrombin time and/or activated partial thromboplastin time, it is important to inquire about recent synthetic cannabinoid use.

Quantifying the Sustainability of Water Availability for the Water-Food-Energy-Ecosystem Nexus in the Niger River Basin.

Water, food, energy, and the ecosystems they depend on interact with each other in highly complex and interlinked ways. These interdependencies can be traced particularly well in the context of a river basin, which is delineated by hydrological boundaries. The interactions are shaped by humans interacting with nature, and as such, a river basin can be characterized as a complex, coupled socioecological system. The Niger River Basin in West Africa is such a system, where water infrastructure development to meet growing water, food, and energy demands may threaten a productive and vulnerable basin ecosystem. These dynamic interactions remain poorly understood. Trade-off analyses between different sectors and at different spatial scales are needed to support solution-oriented policy analysis, particularly in transboundary basins. This study assesses the impact of climate and human/anthropogenic changes on the water, energy, food, and ecosystem sectors and characterizes the resulting trade-offs through a set of generic metrics related to the sustainability of water availability. Results suggest that dam development can mitigate negative impacts from climate change on hydropower generation and also on ecosystem health to some extent.

BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python.

The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. BindsNET is built on the PyTorch deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the BindsNET framework can be adjusted to utilize other existing computing and hardware backends; e.g., TensorFlow and SpiNNaker. We provide an interface with the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning environments. We argue that this package facilitates the use of spiking networks for large-scale machine learning problems and show some simple examples by using BindsNET in practice.