Gamut of cardiac manifestations and complications of COVID-19: a contemporary review.
José Benjamín Cruz-RodriguezRichard A LangeDebabrata MukherjeePublished in: Journal of investigative medicine : the official publication of the American Federation for Clinical Research (2020)
COVID-19 has posed an extraordinary burden on health and the economy worldwide. Patients with cardiovascular diseases are more likely to have severe illness due to COVID-19 and are at increased risk for complications and mortality. We performed a narrative literature review to assess the burden of COVID-19 and cardiovascular morbidity and mortality. Myocardial injury has been reported in 20%-30% of patients hospitalized due to COVID-19 and is associated with a worse prognosis and high mortality (~50%-60%). Proposed mechanisms of myocardial injury include inflammation within the myocardium (due to direct viral infection or cytokine storm), endotheliitis, coronary vasculitis, myocarditis, demand ischemia, plaque destabilization and right ventricular failure. The right ventricle is particularly vulnerable to injury and failure in COVID-19-infected patients, given the hypoxic pulmonary vasoconstriction, pulmonary microthrombi or pulmonary embolism. Echocardiography is an effective and accessible tool to evaluate left and right ventricular functions and risk stratify patients with COVID-19 infection. Cardiac MRI has detected and characterized myocardial injury, with changes compatible with other inflammatory cardiomyopathies. The long-term consequences of these inflammatory changes are unknown, but accumulating data will provide insight regarding the longitudinal impact of COVID-19 infection on cardiovascular morbidity and mortality.
Keyphrases
- coronavirus disease
- sars cov
- pulmonary embolism
- pulmonary hypertension
- oxidative stress
- left ventricular
- healthcare
- cardiovascular disease
- coronary artery disease
- cardiovascular events
- respiratory syndrome coronavirus
- heart failure
- coronary artery
- computed tomography
- ejection fraction
- machine learning
- electronic health record
- big data
- prognostic factors
- metabolic syndrome
- pulmonary artery
- inferior vena cava
- social media
- deep learning
- pulmonary arterial hypertension
- chronic kidney disease
- patient reported