Circulation
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The coronavirus disease 2019 (COVID-19) pandemic has exposed longstanding racial and ethnic inequities in health risks and outcomes in the United States. We aimed to identify racial and ethnic differences in presentation and outcomes for patients hospitalized with COVID-19. ⋯ Although in-hospital mortality and major adverse cardiovascular events did not differ by race/ethnicity after adjustment, Black and Hispanic patients bore a greater burden of mortality and morbidity because of their disproportionate representation among COVID-19 hospitalizations.
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European data support the use of low high-sensitivity troponin (hs-cTn) measurements or a 0/1-hour (0/1-h) algorithm for myocardial infarction to exclude major adverse cardiac events (MACEs) among patients in the emergency department with possible acute coronary syndrome. However, modest US data exist to validate these strategies. This study evaluated the diagnostic performance of an initial hs-cTnT measure below the limit of quantification (LOQ: 6 ng/L), a 0/1-h algorithm, and their combination with history, ECG, age, risk factors, and initial troponin (HEART) scores for excluding MACE in a multisite US cohort. ⋯ In a prospective multisite US cohort, an initial hs-cTnT below the LOQ combined with a low-risk HEART score has a 99% NPV for 30-day MACEs. The 0/1-h hs-cTnT algorithm did not achieve an NPV >99% for 30-day MACEs when used alone or with a HEART score. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02984436.
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Opioid overdose is the leading cause of death for Americans 25 to 64 years of age, and opioid use disorder affects >2 million Americans. The epidemiology of opioid-associated out-of-hospital cardiac arrest in the United States is changing rapidly, with exponential increases in death resulting from synthetic opioids and linear increases in heroin deaths more than offsetting modest reductions in deaths from prescription opioids. The pathophysiology of polysubstance toxidromes involving opioids, asphyxial death, and prolonged hypoxemia leading to global ischemia (cardiac arrest) differs from that of sudden cardiac arrest. ⋯ Opioid education and naloxone distributions programs have been developed to teach people who are likely to encounter a person with opioid poisoning how to administer naloxone, deliver high-quality compressions, and perform rescue breathing. Current American Heart Association recommendations call for laypeople and others who cannot reliably establish the presence of a pulse to initiate cardiopulmonary resuscitation in any individual who is unconscious and not breathing normally; if opioid overdose is suspected, naloxone should also be administered. Secondary prevention, including counseling, opioid overdose education with take-home naloxone, and medication for opioid use disorder, is important to prevent recurrent opioid overdose.
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Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. ⋯ Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.
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Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. ⋯ Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.