Circulation. Arrhythmia and electrophysiology
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Circ Arrhythm Electrophysiol · Oct 2020
Letter Observational StudyElectrocardiographic Changes and Arrhythmias in Hospitalized Patients With COVID-19.
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Circ Arrhythm Electrophysiol · Aug 2020
Comparative StudyCharacterization of Lead Adherence Using Intravascular Ultrasound to Assess Difficulty of Transvenous Lead Extraction.
Clinical factors associated with development of intravascular lead adherence (ILA) are unreliable predictors. Because vascular injury in the superior vena cava-right atrium during transvenous lead extraction is more likely to occur in segments with higher degrees of ILA, reliable and accurate assessment of ILA is warranted. We hypothesized that intravascular ultrasound (IVUS) could accurately visualize and quantify ILA and degree of ILA correlates with transvenous lead extraction difficulty. ⋯ IVUS is a feasible imaging modality that may be useful in characterizing ILA in the superior vena cava-right atrium region. An ILA grading system using imaging correlates with extraction difficulty. Most patients with clinical factors associated with higher extraction difficulty may exhibit lower ILA and extraction difficulty based on IVUS imaging. Graphic Abstract: A graphic abstract is available for this article.
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Circ Arrhythm Electrophysiol · Aug 2020
Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.
Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). ⋯ The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.