Diagnostic Values of Laboratory Biomarkers in Predicting a Severe Course of COVID-19 on Hospital Admission

Abstract

Objective. We intend to identify differences in the clinicodemographic and laboratory findings of COVID-19 patients to predict disease severity and outcome on admission. Methods. This single-centred retrospective study retrieved laboratory and clinical data from 350 COVID-19 patients on admission, represented as frequency tables. A multivariate regression model was used to assess the statistically significant association between the explanatory variables and COVID-19 infection outcomes, where adjusted odds ratio (AOR), p value, and 95% CI were used for testing significance. Results. Among the 350 COVID-19 patients studied, there was a significant increase in the WBC count, neutrophils, aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (dNLR), neutrophil-to-lymphocyte and platelet ratio (NLPR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), D-dimer, interleukin-6 (IL-6), ferritin, lactate dehydrogenase (LDH), prothrombin time (PT), glucose, urea, urea nitrogen, creatinine, alanine phosphatase (ALP), and aspartate aminotransferase (AST) and a significant decrease in lymphocytes, eosinophils, total protein, albumin, prealbumin serum, and albumin/globulin (A/G) ratio in the severe group when compared with the mild and moderate groups. However, after adjusting their age, gender, and comorbidities, WBC count (adjusted odds ratio AOR=6.888, 95% CI=1.590-29.839, p=0.010), neutrophils (AOR=5.912, 95% CI=2.131-16.402, p=0.001), and urea (AOR=4.843, 95% CI=1.988-11.755, p=0.001) were strongly associated with disease severity. Interpretation and Conclusion. On admission, WBC count, neutrophils, and urea, with their cut of values, can identify at-risk COVID-19 patients who could develop severe COVID-19.