Blood Tests May Predict Increased COVID-19 Mortality

Jan, 2021 - By SMI

Blood Tests May Predict Increased COVID-19 Mortality

The objective was to identify particular molecular patterns that could predict disease severity.

One of the major health challenges in this global pandemic is identifying COVID-19 patients with the highest risk of severe illness and death. The novel coronavirus is more dangerous in older individuals or those with pre-existing medical conditions (such as asthma, heart disease, and diabetes) are at high risk of contracting the novel coronavirus. However, this prevents the 96-year old from presenting without any symptoms, or a healthy 21-year old losing life. As of September 24, the virus has infected more than 31 million folks and killed more than 974,000 people across the globe.

The research team from Massachusetts General Hospital has explored some methods to help doctors better evaluate those who are suffering the worst effects of the disease. According to the researchers, a simple blood test can determine which patients who are hospitalized with COVID-19 face a high risk of becoming critically ill and dying. During this research, the team examined blood samples from around 1,641 individuals hospitalized (in early 2020) in Boston hospitals with COVID-19. The objective was to identify a particular molecular pattern that could predict disease severity.

The team looked at the most routine blood tests biomarkers before looking at more complex blood-based biomarkers. Moreover, the researchers found that patients displayed an elevated red cell distribution width or RDW at the time of admission were 2.7 times more likely to die from COVID-19. The research also noted that patients who had seen an increase in RDW were more likely to suffer worse outcomes. This indicates that RDW may be a useful biomarker for tracking a patient's progress during hospitalization. However, further validation is needed, if confirmed, it can be used to detect COVID-19 patients who need clinical care. The research was published in the JAMA Open Network journal.

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