A Real Pandora’s Box in Pandemic Times: A Narrative Review on the Acute Cardiac Injury Due to COVID-19

Abstract

The intricate relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the cardiovascular system is an extensively studied pandemic topic, as there is an ever-increasing amount of evidence that reports a high prevalence of acute cardiac injury in the context of viral infection. In patients with Coronavirus disease 2019, COVID-19, a significant increase in serum levels of cardiac troponin or other various biomarkers was observed, suggesting acute cardiac injury, thus predicting both a severe course of the disease and a poor outcome. Pathogenesis of acute cardiac injury is not yet completely elucidated, though several mechanisms are allegedly involved, such as a direct cardiomyocyte injury, oxygen supply-demand inequity caused by hypoxia, several active myocardial depressant factors during sepsis, and endothelial dysfunction due to the hyperinflammatory status. Moreover, the increased levels of plasma cytokines and catecholamines and a significantly enhanced prothrombotic environment may lead to the destabilization and rupture of atheroma plaques, subsequently triggering an acute coronary syndrome. In the present review, we focus on describing the epidemiology, pathogenesis, and role of biomarkers in the diagnosis and prognosis of patients with acute cardiac injury in the setting of the COVID-19 pandemic. We also explore some novel therapeutic strategies involving immunomodulatory therapy, as well as their role in preventing a severe form of the disease, with both the short-term outcome and the long-term cardiovascular sequelae being equally important in patients with SARS-CoV-2 induced acute cardiac injury.

A Rising Star of the Multimarker Panel: Growth Differentiation Factor-15 Levels Are an Independent Predictor of Mortality in Acute Heart Failure Patients Admitted to an Emergency Clinical Hospital from Eastern Europe

“…Even if the study was conducted during a peak of the COVID-19 pandemic, we did not notice relevant correlations between GDF-15 serum levels and a positive COVID-19 status. The limited number of infected patients and the rather mild forms of the disease have possibly contributed to this finding, in contrast with the vastly incriminated role of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection not only in the intrinsic pathophysiology of cardiovascular diseases [48,49], but also in increasing the serum levels of cardiac biomarkers [50].…”

Riesgo cardiovascular en pacientes recuperados de COVID-19 a corto y mediano plazo: ¿qué concluye la evidencia actual?

“…Esta situación agrava la hipertensión y hará que la respuesta inflamatoria sea aún más intensa, como se expuso en el apartado anterior (9) . Por lo tanto, los principales factores de riesgo para la lesión cardíaca aguda inducida por el SARS-CoV-2 incluyen el tabaquismo, el sexo masculino y las comorbilidades, especialmente la diabetes mellitus, la hipertensión arterial y la enfermedad coronaria (22) .…”

“…Dentro de los desenlaces a corto plazo destaca la insuficiencia cardiaca, que se reporta hasta en un 24 % de los pacientes, seguida por el tromboembolismo venoso, que se ha identificado en el 21 % de los casos, y en menor medida, las arritmias y miocarditis en un 17 % y 7 % de los pacientes, respectivamente (22) . Ahora bien, el infarto agudo de miocardio, facilitado por la interacción entre la ateroesclerosis de las coronarias y el estado proinflamatorio de la COVID-19, se ha reportado hasta en el 9 % de los pacientes, y el riesgo es 5 veces mayor durante la primera semana de síntomas de la enfermedad viral (13,22) .…”

nn-TransUNet: An Automatic Deep Learning Pipeline for Heart MRI Segmentation

“…The number of mortality caused by CVD in 2030 is estimated to be 23.6 million people per year [ 2 ]. Nowadays, with the spread of covid-19, it is observed that the cases of acute cardiac injury related to covid-19 is increasing rapidly [ 3 ], which gives the diagnosis of CVD more importance. In recent years, deep learning-based algorithms have been implemented for medical images processing and assisting diagnosis [ 4 , 5 , 6 , 7 , 8 ].…”