COVID-19 Outcomes in Patients Hospitalised with Acute Myocardial Infarction (AMI): A Protocol for Systematic Review and Meta-Analysis

Abstract

Background: Patients with cardiovascular disease and risk factors for cardiovascular illness are more likely to acquire severe 2019 novel coronavirus (2019-nCoV) infection (COVID-19). COVID-19 infection is more common in patients with cardiovascular illness, and they are more likely to develop severe symptoms. Nevertheless, whether COVID-19 patients are more likely to develop cardiovascular disorders such as acute myocardial infarction (AMI) is still up for debate. Methods: We will follow the preferred reporting items for systematic review and meta-analysis (PRISMA) to report our final study, including a systematic search of the bibliographic database using the appropriate combination of search terms or keywords. The choice of search terms is discussed in more detail later in this paper. The obtained results will be screened, and the data extracted from the studies selected for systematic review will be based on the predefined inclusion and exclusion criteria. Using the obtained data, we will then perform the associated Meta-analysis to generate the forest plot (pooled estimated effect size Hazard Ratio (HR) and 95% Confidence Intervals (CI) values) using the random-effects model. Any publication bias will be assessed using the funnel plot symmetry, Orwin and Classic Fail-Safe N Test and Begg and Mazumdar Rank Correlation Test and Egger’s Test of the intercept. In cases where insufficient data occur, we will also perform a qualitative review. Discussion: This systematic review will explore COVID-19 clinical outcomes, especially survival in patients hospitalised with Acute Myocardial Infarction, by utilising a collection of previously published data on hospitalised COVID-19 patients and Myocardial Infarction. Highlighting these prognostic survival analyses of COVID-19 patients with AMI will have significant clinical implications by allowing for better overall treatment strategies and patient survival estimates by offering clinicians a method of quantitatively analysing the pattern of COVID-19 cardiac complications.