International journal of cardiology
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With the recent emergence of SARS-CoV-2 and COVID-19, healthcare facilities and personnel are expected to rapidly triage and care for patients with even the most complex medical conditions. Adults with congenital heart disease (ACHD) represent an often-intimidating group of complex cardiovascular disorders. Given that general internists and general cardiologists will often be asked to evaluate this group during the pandemic, we propose here an abbreviated triage algorithm that will assist in identifying the patient's overarching ACHD phenotype and baseline cardiac status. The strategy outlined allows for rapid triage and groups various anatomic CHD variants into overarching phenotypes, permitting care teams to quickly review key points in the management of moderate to severely complex ACHD patients.
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Editorial Comment
Interventional cardiology in the neonate: Still a macgyver procedure?
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The novel coronavirus disease, affecting ~9 million people in the past five months and causing >460,000 deaths worldwide, is completely new to mankind. More than 2,000 research projects registered at ClinTrials.gov are aiming at finding effective treatments for rapid transfer to clinical practice. Unfortunately, just few studies have a sufficiently valid design to provide reliable information for clinical practice.
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Patient delay is a worldwide unsolved problem in ST-segment elevated myocardial infarction (STEMI). An accurate warning system based on electrocardiogram (ECG) may be a solution for this problem, and artificial intelligence (AI) may offer a path to improve its accuracy and efficiency. ⋯ In a comparative test with cardiologists, the algorithm had an AUC of 0.9740 (95% CI, 0.9419 to 1), and its sensitivity (recall), specificity, accuracy, precision, and F1 score were 90%, 98% and 94%, 97.82% and 0.9375 respectively, while the medical doctors had sensitivity (recall), specificity, accuracy, precision and F1 score of 71.73%, 89.33%, 80.53%, 87.05% and 0.8817 respectively. This study developed an AI-based, cardiologist-level algorithm for identifying STEMI.
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Deep learning (DL) has shown promising results in improving atrial fibrillation (AF) detection algorithms. However, these models are often criticized because of their "black box" nature. ⋯ The morphology based DL model developed in this study was able to discriminate AF from SR with a very high accuracy. DL model visualization may help clinicians gain insights into which (unrecognized) ECG features are most sensitive to discriminate AF from SR.