Abnormal ECG Predicts Death From Covid-19
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Abnormal ECG predicts death from Covid-19
bnormal ECG predicts death from Covid-19
A team of scientists at NYU Langone Medical Center has found that the clinical severity of hospitalized patients with the 2019 coronavirus can be predicted by analyzing elevated troponin and abnormal electrocardiogram (ECG). The study is currently available on the medRxiv* preprint server.
COVID-19 has become one of the deadliest epidemics in modern history. Since its emergence at the end of December 2019, the highly infectious acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogenic causative agent of the new coronavirus, has infected more than 55.5 million people and caused more than 1.33 million deaths. Although most COVID-19 patients remain asymptomatic or mildly symptomatic, older adults and those with comorbidities (hypertension, cardiorespiratory disorders, diabetes, and kidney disease) are highly susceptible to SARS-CoV-2 infection.
Among the various complications related to the new coronavirus, about 25% of patients developed cardiac abnormalities. This complication is strongly associated with significantly increased mortality. In most hospitalized patients with COVID-19, elevated troponin levels are a major determinant of cardiovascular abnormalities. Patients with COVID-19 may develop cardiovascular injury due to a number of factors, such as elevated levels of pro-inflammatory mediators, coagulation/thrombus-mediated microvascular damage, decreased blood oxygen levels, and disease-induced increased metabolic demands or direct viral entry into cardiomyocytes. Systematic evaluation of electrocardiogram (ECG) results may be an effective approach to gain a deeper understanding of cardiac complications in patients.
Given the good prognostic value and easy availability of ECGs, research scientists sought to understand whether the results presented on ECGs in patients with COVID-19 stratified by troponin levels could better predict cardiovascular status and prognosis in hospitalized patients.
