Red cell distribution width, platelet distribution width, and plateletcrit as indicators of prognosis in COVID-19 patients - A single-center study
Keywords:
COVID-19; Red blood cells; Red cell distribution width; Platelet distribution width; PlateletcritAbstract
Background: COVID-19 is still present in the world, though the extent varies by region and country. According to the World Health Organization, there have been over 617 million confirmed cases of COVID-19 and over 13 million deaths worldwide since the pandemic began on March 10, 2023.
Aims and Objectives: This is a study conducted with the aim of providing biomarkers to predict COVID-19 disease progression and mortality based on red cell indices and platelet indices which are commonly measured as part of a complete blood count (CBC).
Materials and Methods: A prospective study was conducted during the peak of the second wave of COVID-19 from March 2021 to June 2021. The study included 540 patients who were admitted to the Government General Hospital, Nizamabad, and had tested positive for COVID-19 by RT-PCR. Red Blood Cell (RBC), Hematocrit (HCT), Red cell indices like Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Haemoglobin Concentration (MCHC), Red Cell Distribution width (RDW) and Platelet indices like Mean Platelet Volume (MPV), Platelet Distribution Width (PDW), Plateletcrit (PCT), Platelet–Large Cell Ratio were taken from CBC analyzer Sysmex XN-1000 and analyzed statistically. The patients were then followed up for a period of 14 days to track their outcomes.
Results: In the data, majority were male n=334 (62%) and n=280 (38%) were female. 70.37% (n=380) were survivors and 29.63% (n=160) were non-survivors. Red blood cell, red cell indices such as RDW-CV and RDW-SD, and platelet indices such as PCT and PDW were significantly higher in non-survivors compared to survivors with P<0.05.
Conclusion: Non-survivors had significantly higher levels of RDW-CV, RDW-SD, PCT, and PDW compared to survivors. These parameters in combination can be useful for predicting COVID-19 mortality at early stage in forthcoming waves.
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