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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 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers., Results: The study included 88 patients (aged 58.3 ± 16.3 years). 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., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Ziv Dadon et al.)

Objectives: A significant proportion of COVID-19 patients may have cardiac involvement including arrhythmias. Although arrhythmia characterisation and possible predictors were previously reported, there are conflicting data regarding the exact prevalence of arrhythmias. Clinically applicable algorithms to classify COVID patients' arrhythmic risk are still lacking, and are the aim of our study., Methods: We describe a single-centre cohort of hospitalised patients with a positive nasopharyngeal swab for COVID-19 during the initial Israeli outbreak between 1/2/2020 and 30/5/2020. The study's outcome was any documented arrhythmia during hospitalisation, based on daily physical examination, routine ECG's, periodic 24-hour Holter, and continuous monitoring. Multivariate analysis was used to find predictors for new arrhythmias and create classification trees for discriminating patients with high and low arrhythmic risk., Results: Out of 390 COVID-19 patients included, 28 (7.2%) had documented arrhythmias during hospitalisation, including 23 atrial tachyarrhythmias, combined atrial fibrillation (AF), and ventricular fibrillation, ventricular tachycardia storm, and 3 bradyarrhythmias. Only 7/28 patients had previous arrhythmias. Our study showed a significant correlation between disease severity and arrhythmia prevalence (P < .001) with a low arrhythmic prevalence amongst mild disease patients (2%). Multivariate analysis revealed background heart failure (CHF) and disease severity are independently associated with overall arrhythmia while age, CHF, disease severity, and arrhythmic symptoms are associated with tachyarrhythmias. A novel decision tree using age, disease severity, CHF, and troponin levels was created to stratify patients into high and low risk for developing arrhythmia., Conclusions: Dominant arrhythmia amongst COVID-19 patients is AF. Arrhythmia prevalence is associated with age, disease severity, CHF, and troponin levels. A novel simple Classification tree, based on these parameters, can discriminate between high and low arrhythmic risk patients., (© 2021 John Wiley & Sons Ltd.)

Background: The liberal administration of hydroxychloroquine-sulphate (HCQ) to COVID-19 patients has raised concern regarding the risk of QTc prolongation and cardiac arrhythmias, particularly when prescribed with azithromycin. We evaluated the incidence of QTc prolongation among moderately and severely ill COVID-19 patients treated with HCQ and of the existence of concomitant alternative causes., Methods: All COVID-19 patients treated with HCQ (between Mar 1 and Apr 14, 2020) in a tertiary medical centre were included. Clinical characteristics and relevant risk factors were collected from the electronic medical records. Individual patient QTc intervals were determined before and after treatment with HCQ. The primary outcome measure sought was a composite end point comprised of either an increase ≥60 milliseconds (ms) in the QTc interval compared with pre-treatment QTc, and/or a maximal QTc interval >500 ms RESULTS: Ninety patients were included. Median age was 65 years (IQR 55-75) and 57 (63%) were male. Thirty-nine patients (43%) were severely or critically ill. Hypertension and obesity were common (n = 23 each, 26%). QTc prolongation evolved in 14 patients (16%). Age >65 years, congestive heart failure, severity of disease, C-reactive protein level, hypokalaemia and furosemide treatment, were all associated with QTc prolongation. Adjusted analysis showed that QTc prolongation was five times more likely with hypokalaemia [OR 5, (95% CI, 1.3-20)], and three times more likely with furosemide treatment [OR 3 (95% CI, 1.01-13.7)]., Conclusion: In patients treated with HCQ, QTc prolongation was associated with the presence of traditional risk factors such as hypokalaemia and furosemide treatment., (© 2020 John Wiley & Sons Ltd.)