Streptococcus gordonii is an infrequent cause of infective endocarditis (IE); associated spondylodiskitis has not yet been described in the literature. We describe 2 patients who presented with new-onset, severe back pain; blood cultures revealed S. gordonii bacteremia, which led to the diagnosis of spondylodiskitis and IE. We review our 2-decade experience with S. gordonii bacteremia to describe the clinical and epidemiological characteristics of these patients. In our hospital over the last 20 years (1998–2017), a total of 15 patients with S. gordonii bacteremia were diagnosed, including 11 men and 4 women, and the mean age was 65 ± 22 (range 23–95). The most common diagnosis was IE (9 patients), spondylodiskitis (the presented 2 patients, who in addition were diagnosed with endocarditis), necrotizing fasciitis (1), sternitis (1), septic arthritis (1) and pneumonia (1). The 11 patients with IE were treated with penicillin ± gentamicin, or ceftriaxone for 6 weeks, 5 required valve surgery and 10/11 (91%) attained complete cure. The 2 patients with diskitis required 2–3 months of intravenous antibiotics to achieve complete cure. Spondylodiskitis was the presenting symptom of 2/11 (18%) patients with S. gordonii endocarditis. Spondylodiskitis should probably be looked for in patients diagnosed with S. gordonii endocarditis and back pain as duration of antibiotic treatment to achieve complete cure may be considerably longer.

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.

Multi-casualty incidents (MCIs) continue to occur throughout the world, whether they be mass shootings or natural disasters. Prehospital emergency services have done a professional job at stabilizing and transporting the victims to local hospitals. When there are multiple casualties, there may not be enough professional responders to care for the injured. Bystanders and organized volunteer first responders have often helped in extricating the victims, stopping the bleeding, and aiding in the evacuation of the victims. Magen David Adom (MDA translated as “Red Shield of David”), the national Emergency Medical Services (EMS) provider for Israel, has successfully introduced a program for volunteer first responders that includes both a mobile-phone-based application and appropriate life-saving equipment. Most of the responders, known as Life Guardians, are already medical professionals such as physicians, nurses, or off-duty medics. They are notified by a global positioning system application if there is a nearby life-threatening incident such as respiratory or cardiac arrest, major trauma, or an MCI. They are given a kit that includes a bag-valve mask device, oropharyngeal airways, tourniquets, and bandages. There are currently 17,000 Life Guardians, and in the first-half of 2017, they responded to 253 events. The Life Guardians are essentially an out-of-hospital manpower multiplier using a simple crowdsourcing application who have the necessary skills and equipment to treat those in cardiopulmonary arrest, or victims of trauma, including MCIs. Such a model can be integrated into other systems throughout the world to save lives. Jaffe E , Dadon Z , Alpert EA . Wisdom of the crowd in saving lives: the Life Guardians app. Prehosp Disaster Med. 2018;33(5):550–552.

Objectives: Incorporating artificial intelligence (AI) into echocardiography operated by clinicians working in the emergency department to accurately assess left‐ventricular ejection fraction (LVEF) may lead to better diagnostic decisions. This randomized controlled pilot study aimed to evaluate AI use as a didactic tool to improve noncardiologist clinicians’ assessment of LVEF from the apical 4‐chamber (A4ch) view.Methods: This prospective randomized controlled pilot study tested the feasibility and acceptability of the incorporation of AI as a didactic tool by comparing the ability of 16 clinicians who work in the emergency department to assess LVEF before and after the introduction of an AI‐based ultrasound application. Following a brief didactic course, participants were randomly equally divided into an intervention and a control group. In each of the first and second sessions, both groups were shown 10 echocardiography A4ch clips and asked to assess LVEF. Following each clip assessment, only the intervention group was shown the results of the AI‐based tool. For the final session, both groups were presented with a new set of 40 clips and asked to evaluate the LVEF.Results: In the “normal‐abnormal” category evaluation, as related to own baseline accuracy assessment, the intervention group had an improvement in accuracy on 50 consecutive clip assessments compared with a decline in the control group (0.10 vs. −0.12, respectively, p = 0.038). In the “significantly reduced LVEF” category, the intervention group showed significantly less decline in clip assessment as compared to the control group (−0.03 vs. −0.12, respectively, p = 0.050).Conclusions: A study involving AI incorporation as a didactic tool for clinicians working in the emergency department appears feasible and acceptable. The introduction of an AI‐based tool to clinicians working in the emergency department improved the assessment accuracy of LVEF as compared to the control group.

Introduction: Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. Aim: To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. Methods: Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. Results: The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083–6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. Conclusion: AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.

Background: The association between COVID‐19 infection and the cardiovascular system necessitates the use of an echocardiogram in this setting. Information on the utilization, safety, and quality of point‐of‐care cardiac and lung ultrasound using a hand‐held device in these patients is scarce.Aims: To investigate the safety, technical aspects, quality indices, and interpretive accuracy of a hand‐held echocardiogram in patients with COVID‐19.Methods: From April‐28 through July‐27, 2020, consecutive patients with COVID‐19 underwent hand‐held echocardiogram and lung ultrasound evaluation (Vscan Extend™; GE Healthcare) within 48‐h of admission. The operators recorded a series of technical parameters and graded individual experiences. The examinations were further analyzed by a blinded fellowship‐trained echocardiographer for general quality, proper acquisition, and right ventricular (RV) demonstration.Results: Among 103 patients, 66 (64.1%) were male. Twenty‐nine (28.2%) patients could not turn on their left side and 23 (22.3%) could not maintain effective communication. The mean length of each echocardiogram study was 8.5 ± 2.9 min, battery usage was 14 ± 5%, and mean operator‐to‐patient proximity was 59 ± 11 cm. Ninety‐five (92.2%) examinations were graded as fair/good quality. A fair agreement was demonstrated between the operator and the echocardiographer for general ultrasound quality (Kappa = 0.329,p< 0.001). A fair‐good correlation (r= 0.679,p< 0.001) and substantial agreement (Kappa = 0.612,p< 0.001) were demonstrated between the operator and echocardiographer for left ventricular ejection fraction (LVEF), whereas a fair agreement was demonstrated for RV systolic function (Kappa = 0.308,p= 0.002). LVEF agreement was also assessed using the Bland‐Altman analysis revealing a mean bias of −0.96 (95% limits of agreement 9.43 to −11.35;p= 0.075).Conclusions: Among patients with COVID‐19, echocardiography with a hand‐held ultrasound is a safe and reasonable alternative for a complete formal study (<10% poor‐quality indices). Echocardiogram assessment by the operators during the exam acquisition is reliable for LVEF, while RV systolic function should be subsequently offline reassessed.