A competitive games-based educational event focused on point-of-care ultrasound is an effective educational tool. SonoGames increases EM residents' knowledge, enthusiasm, and clinical use of ultrasound both during and after the event.

Ultrasound on the Frontlines of COVID-19: Report From an International Webinar T he COVID-19 pandemic has spread to 185 countries with over 2.1 million confirmed cases and 145,000 deaths, as per the Johns Hopkins University COVID-19 dashboard provided at https://coronavirus. jhu.edu/map.html. Imaging modalities such as chest radiography, thoracic and cardiovascular ultrasound, and computed tomography (CT) have roles in the diagnosis, prognosis, monitoring, and therapy of COVID-19. However, the potential benefits of imaging need to be balanced against resource utilization and infectious risk.Point-of-care ultrasound (POCUS) represents an attractive imaging modality in COVID-19 as it involves portable technology without radiation. POCUS is performed by a clinician at the patient's bedside, reducing exposure of additional personnel and avoiding virulent particle transmission during transport to other areas.On March 18, 2020, the American College of Emergency Physicians (ACEP) hosted a virtual town hall meeting to discuss the use of POCUS in COVID-19 patients. The panel of nine emergency physicians included those on the forefront of outbreaks in Spain, Italy, and Washington State, as well as POCUS leaders who are heavily involved in developing processes for their institutions.We seek to summarize available literature on imaging in COVID-19 and provide informally derived recommendations of the panel on POCUS use in COVID-19. The webinar may be accessed at https://www.acep. org/how-we-serve/sections/emergency-ultrasound/. CURRENT IMAGING APPROACHES IN COVID-19CT Computed tomography represents the most sensitive imaging modality for COVID-19 involvement of the

Objectives-To develop a consensus statement on the use of lung ultrasound (LUS) in the assessment of symptomatic general medical inpatients with known or suspected coronavirus disease 2019 .Methods-Our LUS expert panel consisted of 14 multidisciplinary international experts. Experts voted in 3 rounds on the strength of 26 recommendations as "strong," "weak," or "do not recommend." For recommendations that reached consensus for do not recommend, a fourth round was conducted to determine the strength of those recommendations, with 2 additional recommendations considered.Results-Of the 26 recommendations, experts reached consensus on 6 in the first round, 13 in the second, and 7 in the third. Four recommendations were removed because of redundancy. In the fourth round, experts considered 4 recommendations that reached consensus for do not recommend and 2 additional scenarios; consensus was reached for 4 of these. Our final recommendations consist of 24 consensus statements; for 2 of these, the strength of the recommendations did not reach consensus.Conclusions-In symptomatic medical inpatients with known or suspected COVID-19, we recommend the use of LUS to: (1) support the diagnosis of pneumonitis but not diagnose COVID-19, (2) rule out concerning ultrasound features, (3) monitor patients with a change in the clinical status, and (4) avoid unnecessary additional imaging for patients whose pretest probability of an alternative or superimposed diagnosis is low. We do not recommend the use of LUS to guide admission and discharge decisions. We do not recommend routine serial LUS in patients without a change in their clinical condition.

Background: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Methods: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%–73%), mildly-to-moderately (30%–52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians’ interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. Results: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86–0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. Conclusions: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.

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Point-of-care US program implementation may improve students' overall physical examination understanding and performance, even when US performance itself is not being tested. Introducing a POCUS curriculum may work best when designed in conjunction with the physical examination thread of a medical school curriculum.

Introduction Thoracic ultrasound is frequently used in the emergency department (ED) to determine the etiology of dyspnea, yet its use is not widespread in the prehospital setting. We sought to investigate the feasibility and diagnostic performance of paramedic acquisition and assessment of thoracic ultrasound images in the prehospital environment, specifically for the detection of B-lines in congestive heart failure (CHF). Methods This was a prospective observational study of a convenience sample of adult patients with a chief complaint of dyspnea. Paramedics participated in a didactic and hands-on session instructing them how to use a portable ultrasound device. Paramedics assessed patients for the presence of B-lines. Sensitivity and specificity for the presence of bilateral B-lines and any B-lines were calculated based on discharge diagnosis. Clips archived to the ultrasound units were reviewed and paramedic interpretations were compared to expert sonologist interpretations. Results A total of 63 paramedics completed both didactic and hands-on training, and 22 performed ultrasounds in the field. There were 65 patients with B-line findings recorded and a discharge diagnosis for analysis. The presence of bilateral B-lines for diagnosis of CHF yielded a sensitivity of 80.0% (95% confidence interval [CI], 51.4–94.7%) and specificity of 72.0% (95% CI, 57.3–83.3), while presence of any B-lines was 93.3% sensitive (95% CI, 66.0–99.7%), and 50% specific (95% CI, 35.7–64.2%) for CHF. Paramedics archived 117 ultrasound clips of which 63% were determined to be adequate for interpretation. Comparison of paramedic and expert sonologist interpretation of images showed good inter-rater agreement for detection of any B-lines (k = 0.60; 95% CI, 0.36–0.84). Conclusion This observational pilot study suggests that prehospital lung ultrasound for B-lines may aid in identifying or excluding CHF as a cause of dyspnea. The presence of bilateral B-lines as determined by paramedics is reasonably sensitive and specific for the diagnosis of CHF and pulmonary edema, while the absence of B lines is likely to exclude significant decompensated heart failure. The study was limited by being a convenience sample and highlighted some of the difficulties related to prehospital research. Larger funded trials will be needed to provide more definitive data.

ProblemThe COVID-19 pandemic significantly disrupted point-of-care ultrasound (POCUS) education. Medical schools and residency programs placed restrictions on bedside teaching and clinical scanning as part of risk mitigation. In response, POCUS faculty from 15 institutions nationwide collaborated on an alternative model of ultrasound education, A Distance-learning Approach to POCUS Training (ADAPT). ApproachADAPT was repeated monthly from April 1 through June 30, 2020. It accommodated 70 learners, who included 1-to 4-week rotators and asynchronous learners. The curriculum included assigned prework and learning objectives covering 20 core