Evaluation of cardiac biomarkers among dead and alive COVID-19 patients in Southwest Iran

Author:

Adel Seyed Mohammad Hassan12,Sardabi Ebrahim Heydari2,Akiash Nehzat2,Mohammadi Mohammad1,Sayadian Mona2,pour Sanaz Saki1,Amini Payam1

Affiliation:

1. Atherosclerosis Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

2. Department of Cardiology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Abstract

ABSTRACT Introduction: The need to understand the global burden of heart failure following the pandemic has arisen as a result of an increase in papers that support cardiac involvement in coronavirus disease 2019 (COVID-19). Therefore, the current study aims to provide a more thorough explanation of the function and use of cardiac biomarkers in dead and alive COVID-19 patients. Methods: All patients who were referred and admitted to Razi Hospital, Ahvaz, Iran, from March 2020 to March 2021 with a diagnosis of COVID-19 were included in this study. Results: During the study period, 753 patients were hospitalized with a diagnosis of COVID-19. In total, 157 cases died from the disease (case fatality rate: 20.84%). Pre-existing cerebrovascular accidents (CVAs) were more frequent in dead cases (14% vs. 6.4%). It was observed that atrial fibrillation was normal in most of the alive cases in comparison to dead patients (P value = 0.014). Moreover, it was seen that CRP, IL-6, and procalcitonin were increased in dead patients. Also, an association was found between ejection fraction (EF) value and death rate (P value = 0.035). The higher frequency of positive troponin occurring in the dead group suggested a possible adverse effect on the mortality rate (22.3% vs. 16.4%). Conclusion: Adults with COVID-19 commonly have cardiac manifestations, including symptoms of myocardial damage. In light of the recognized utility of troponin, ejection fraction, procalcitonin, IL-6, and CRP in COVID-19 patients with suspected myocardial damage, we should develop a safe and precise diagnostic algorithm that may contain patients’ clinical histories and additional variables that may facilitate the prediction of myopericarditis.

Publisher

Medknow

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