Abstract
The COVID-19 pandemic has caused huge socio-economic losses and continues to threat humans worldwide. With more than 4.5 million deaths and more than 221 million confirmed COVID-19 cases, the impact on physical, mental, social and economic resources is immeasurable. During any novel disease outbreak, one of the primary requirements for effective mitigation is the knowledge of clinical manifestations of the disease. However, in absence of any unique identifying characteristics, diagnosis/prognosis becomes difficult. It intensifies misperception and leads to delay in containment of disease spread. Numerous clinical research studies, systematic reviews and meta-analyses have generated considerable data on the same. However, identification of some of the distinct clinical signs and symptoms, disease progression biomarkers and the risk factors leading to adverse COVID-19 outcomes warrant in-depth understanding. In view of this, we assessed 20 systematic reviews and meta-analyses with an intent to understand some of the potential independent predictors/biomarkers/risk factors of COVID-19 severity and mortality.
Highlights
Coronaviruses belong to Coronaviridae family of viruses
It is imperative to categorize COVID-19 patients based on their comorbidities, like impaired kidney or liver functions or cardiac injury, etc
As discussed in the present work, acute kidney injury (AKI) is a critical complication of COVID-19 and calls for immediate care and monitoring (Shao et al, 2020) to minimize the risk of severity and poor prognosis
Summary
The degree of disease caused by coronaviruses can vary from mild like common cold to severe like severe acute respiratory syndrome (SARS) and the middle east respiratory syndrome (MERS). These viruses have been successful in crossing inter-species barriers. The recent emergence of the novel SARS Coronavirus 2 (SARS-CoV-2) is another incidence of zoonotic transmission of coronaviruses. As per the genomic sequence analysis, the source of novel SARSCoV-2 is speculated to be a previously identified bat coronavirus strain RaTG13 (96.2- 97.41% identity match) (Shi, 2021; Malaiyan et al, 2021) or pangolin-CoV (91.02- 92.22% genomic identity match) (Zhang T. et al, 2020; Malaiyan et al, 2021). Further investigations are being undertaken in this regard (WHO News release, 2021)
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