Association of Peripheral Blood Parameters With Outcomes of COVID-19 Infection in a Tertiary Care Setting of Eastern India: An Institute-Based Study.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the Coronavirus DISEASE 2019 (COVID-19) pandemic. Blood investigations play a vital role in providing information regarding the inflammatory process. Previous studies have shown that complete blood count parameters have clinical importance in predicting disease outcomes. However, there is a scarcity of literature published from our region in India. Aims The present study was conducted to describe the epidemiological, clinical, and hematological characteristics and outcomes of COVID-19 confirmed cases. Material and methods All real-time reverse transcriptase-polymerase chain reaction (RT-PCR) confirmed SARS-CoV-2 cases admitted in our institute over three months, from July to September 2020, were included in the study population. The blood samples of SARS-CoV-2 positive cases were analyzed for complete blood counts and coagulation profile on admission and at the time of discharge (most recent in case of mortality). Results A total of 252 RT-PCR confirmed SARS-CoV-2 cases were included in the study. The most common age group affected was 46 to 60 years, and the male-to-female ratio was 2.45:1. The most common clinical symptom was dyspnea, and the commonest comorbidity was hypertension. The statistical analysis showed a significant association of age, absolute neutrophil count (ANC) D-dimer, neutrophil-to-lymphocyte ratio (NLR), and platelets-to-lymphocyte ratio (PLR) with intensive care unit (ICU) admission and death. Gender, dyspnea, and absolute eosinophil count (AEC) showed significant association with ICU patients only, while liver disease and absolute lymphocyte count (ALC) had a significant association with death. Conclusion There are many notable clinical and hematological manifestations of COVID-19. Age, gender, dyspnea, comorbid liver disease, ANC, ALC AEC, NLR, PLR, and D- dimer may help clinicians predict the disease progression and reduce mortality risk.