Ultrasound diagnosis of fractures in mass casualty incidents

Prehospital management of earthquake crush injuries: A collective review

Earthquakes are natural disasters which can destroy the rural and urban infrastructure causing a high toll of injuries and death without advanced notice. We aim to review the prehospital medical management of earthquake crush injuries in the field. PubMed was searched using general terms including rhabdomyolysis, crush injury, and earthquake in English language without time restriction. Selected articles were critically evaluated by three experts in disaster medicine, emergency medicine, and critical care. The medical response to earthquakes includes: (1) search and rescue; (2) triage and initial stabilization; (3) definitive care; and (4) evacuation. Long-term, continuous pressure on muscles causes crush injury. Ischemia-reperfusion injury following the relieving of muscle compression may cause metabolic changes and rhabdomyolysis depending on the time of extrication. Sodium and water enter the cell causing cell swelling and hypovolemia, while potassium and myoglobin are released into the circulation. This may cause sudden cardiac arrest, acute extremity compartment syndrome, and acute kidney injury. Recognizing these conditions and treating them timely and properly in the field will save many patients. Majority of emergency physicians who have worked in the field of the recent Kahramanmaraş 2023, Turkey, earthquakes, have acknowledged their lack of knowledge and experience in managing earthquake crush injuries. We hope that this collective review will cover the essential knowledge needed for properly managing seriously crushed injured patients in the earthquake field.

Point-of-care ultrasound (POCUS) practices in the helicopter emergency medical services in Europe: results of an online survey

Background

The extent to which Point-of-care of ultrasound (POCUS) is used in different European helicopter EMS (HEMS) is unknown. We aimed to study the availability, perception, and future aspects of POCUS in the European HEMS using an online survey.

Method

A survey about the use of POCUS in HEMS was conducted by a multinational steering expert committee and was carried out from November 30, 2020 to December 30, 2020 via an online web portal. Invitations for participation were sent via email to the medical directors of the European HEMS organizations including two reminding notes.

Results

During the study period, 69 participants from 25 countries and 41 different HEMS providers took part in the survey. 96% (n = 66) completed the survey. POCUS was available in 75% (56% always when needed and 19% occasionally) of the responding HEMS organizations. 17% were planning to establish POCUS in the near future. Responders who provided POCUS used it in approximately 15% of the patients. Participants thought that POCUS is important in both trauma and non-trauma-patients (73%, n = 46). The extended focused assessment sonography for trauma (eFAST) protocol (77%) was the most common protocol used. A POCUS credentialing process including documented examinations was requested in less than one third of the HEMS organizations.

Conclusions

The majority of the HEMS organizations in Europe are able to provide different POCUS protocols in their services. The most used POCUS protocols were eFAST, FATE and RUSH. Despite the enthusiasm for POCUS, comprehensive training and clear credentialing processes are not available in about two thirds of the European HEMS organizations. Due to several limitations of this survey further studies are needed to evaluate POCUS in HEMS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13049-021-00933-y.

Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning

Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DL-based systems to improve point-of-care ultrasound (POCUS), we look at DL-based automation as a key field in expanding and improving POCUS applications in various clinical settings. A promising additional value would be the ability to automate training model selections for teaching POCUS to medical trainees and novice sonologists. The diversity of POCUS applications and ultrasound equipment, each requiring specialized AI models and domain expertise, limits the use of DL as a generic solution. In this article, we highlight the most advanced potential applications of AI in POCUS tailored to high-yield models in automated image interpretations, with the premise of improving the accuracy and efficacy of POCUS scans.