Our objective was to determine the utility of point-of-care ultrasound (POCUS) to identify and guide treatment of tamponade or clinically significant pericardial effusions in the emergency department (ED). This was a retrospective cohort study of non-trauma patients who were diagnosed with large pericardial effusions or tamponade by the ED physician using POCUS. The control group was composed of those patients later diagnosed on the medical wards or incidentally in the ED by other means such as a computed tomography. The following data were abstracted from the patient's file: demographics, medical background, electrocardiogram results, chest radiograph readings, echocardiogram results, and patient outcomes. There were 18 patients in the POCUS arm and 55 in the control group. The POCUS arm had a decreased time to pericardiocentesis (11.3 vs. 70.2 hours, P=0.055) as well as a shorter length of stay (5.1 vs. 7.0 days, P=0.222). A decreased volume of pericardial fluid was drained (661 vs. 826 mL, P=0.139) in the group diagnosed by POCUS. This study suggests that POCUS may effectively identify pericardial effusions and guide appropriate treatment, leading to a decreased time to pericardiocentesis and decreased length of hospital stay. Pericardial tamponade or a large pericardial effusion should be considered in all patients presenting to the ED with clinical, radiographic, or electrocardiographic signs of cardiovascular compromise.

Introduction. Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim. To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department. Methods. Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers. Results. The study included 88 patients (aged 58.316.3years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students visual evaluation and students+AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students+AI and cardiologists but only a fair agreement for the students visual evaluation. Conclusion. Medical students utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers evaluation.

Objective: The scientific literature on coronavirus disease (COVID-19) is extensive, but little is written about the role of emergency medical services (EMS). The objective of this study is to describe the role of Magen David Adom (MDA), Israel’s national emergency prehospital medical organization, in the pre-exposure period, before widespread governmental action. These efforts were based on (1) phone diagnosis, dispatch, and transport; and (2) border management checkpoints.Methods: This is a descriptive study of MDA’s role in pandemic response during the pre-exposure period. Medical emergency telephone calls from either individuals or medical sources were identified by a dispatcher as “suspected COVID-19” based on symptoms and travel exposure. Data were also collected for travelers approaching the MDA border checkpoint at Ben-Gurion International Airport.Results: The total number of protected transports during this time was 121. Of these, 44 (36.3%) were referred by medical sources, and 77 (63.7%) were identified as “suspected COVID-19” by dispatchers. The checkpoint was accessed by 156 travelers: 87 were sent to home-quarantine; 12 were transported to the hospital; 18 were refused entry; and 39 required no further action.Conclusion: EMS can work effectively in the pre-exposure period through instructing home quarantine, providing protected transport, and staffing border control checkpoints.