Abstract
Since the beginning of the COVID-19 pandemic, scientists have struggled significantly to understand the complexity of COVID-19 pathophysiology. COVID-19 has demonstrated a notoriously unpredictable clinical course. This unpredictability constituted a significant obstacle to clinicians in predicting the disease course among COVID-19 patients, more specifically, in predicting who would develop severe cases and possibly die from the infection. This brief report aims to assess the diagnostic value of using a complete blood count (CBC) and applying high-dimensional analysis, i.e., principal component analysis (PCA), on it to differentiate between patients with mild and severe COVID-19 infection. The data of 855 patients were retrieved from multiple centres in Saudi Arabia. Descriptive statistics, such as counts, percentages, and medians (interquartile ranges) were used to describe patients' characteristics and CBC parameters. Analytical statistics, such as the Mann-Whitney U test, were used to compare between survivors and non-survivors. PCA was applied using the CBC parameters, and the results were compared between survivors and non-survivors. Patients in this study had a median age of 41, with an almost equal ratio of men to women. Most participants were Saudis, and non-survivors were 13.22% of the total cohort. The median values of all CBC indices were within reference ranges; however, some statistically significant differences were observed between survivors and non-survivors. Non-survivors had lower hemoglobin levels and lower hematocrit, lymphocyte, and eosinophil counts but higher WBC and neutrophil counts compared to survivors. PCA on the CBC results of survivors yielded a significantly different profile than non-survivors, indicating the possibility of its use in the context of COVID-19. The diagnostic value of CBC in the clinical management of COVID-19 should be utilized in clinical guidelines for managing COVID-19 cases.