The American journal of emergency medicine
-
Healthcare facilities and medical providers are not immune to aggression and threat from terrorists, criminals and rogue states. The concept of Hybrid Warfare is often described as a mix of conventional warfare, irregular warfare, terrorism, criminality and different types of other hybrid threats such as cyberattacks and drone technology. Healthcare systems can either be primary or secondary targets of hybrid warfare with potentially devastating consequences. ⋯ Clinicians and healthcare managers of all levels should have a basic knowledge of the different components of hybrid warfare so as to mitigate effects of an attack. It is suggested that an emergency department do not aim to create totally new solutions for hybrid threats but use an all hazards approach and the available guidelines for handling generic threats. However, there must be a preparedness for the different ways hybrid warfare can play out, how the threats can be combined in synergistic ways and the potential compounding effects on healthcare and society.
-
Observational Study
Trauma center designation level and survival of patients with chest wall instability.
Chest wall instability is a potentially life-threatening condition that should be evaluated at a trauma center. While patients with chest wall instability are sent to different trauma center levels, the impact of this on outcomes has not been evaluated yet. This study examines survival to hospital discharge of patients with chest wall instability treated at different trauma center levels. ⋯ Survival rates for patients having chest wall instability were similar when transported to level II or level III versus level I centers. This finding can help guide pre-hospital field triage criteria for this specific type of injury and highlights the need for more outcome research in organized trauma systems.
-
Out-of-hospital cardiac arrest (OHCA) is a leading cause of death, and research has identified limitations in analyzing the factors related to the incidence of cardiac arrest and the frequency of bystander cardiopulmonary resuscitation. This study conducts a cluster analysis of the correlation between location-related factors and the outcome of patients with OHCA using two machine learning methods: variational autoencoder (VAE) and the Dirichlet process mixture model (DPMM). ⋯ This methodology can facilitate the development of a regionalization strategy that can improve the survival rate of cardiac arrest patients in different regions.