Dynamic Changes in Parameters of Complete Blood Count Predict Disease Severity and Prognosis in Patients with COVID-19; A Prospective Study

Background: The pandemic of the coronavirus disease 2019 (COVID-19) is a major cause of death worldwide; thus, disease prediction is important. This study aimed to evaluate dynamic changes of complete blood count parameters in adult patients to predict disease severity. Materials and Methods: Data from 980 consecutive hospitalized patients diagnosed with COVID-19 were analyzed prospectively. Patients were categorized into moderate disease-cured (n = 682), severe disease-cured (n = 136), and deceased (n = 162) groups. Clinical conditions at the admission and blood samples every other day were collected for each patient from hospital admission to discharge or death. Mean values of serum parameters were compared among the three groups; the Hazard ratio of different indices for death was calculated, and repeated measured ANOVA was employed to assess the prognostic importance of dynamic changes in blood parameters during the disease. Results: Univariable and multivariable regression analysis showed that the only important clinical risk factor associated with death was needing invasive ventilation at admission (HR of 18.97 and 23.82 in univariable and multivariable regression analysis, respectively). Considering dynamic changes in blood elements, repeated measured ANOVA showed patients who survived had a decrease in WBC and Neutrophil count as well as Neutrophil to lymphocyte ratio (NLR) compared to expired patients; in contrast, platelet and lymphocyte count increased in survivors while dropped in deceased ones. Conclusion: Dynamic changes in blood indices are prognostic indicators of an unfavorable prognosis for COVID-19 infection.