Laboratory Biomarkers as a Predictor of COVID-19 Disease Progression

Abstract

Introduction: On December 2019, Wuhan city in China, became the epicentre of unexplained cases of pneumonia. Later in January 2020, scientists identified this as a novel coronavirus, temporarily labelled as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Its name was then changed to Coronavirus Disease 2019 (COVID-19) by the World Health Organisation (WHO) in February 2020 as the disease spread worldwide. Aim: To investigate the association of different biomarkers in the COVID-19 disease progression and assess how their levels vary with presence and absence of co-morbid conditions. Materials and Methods: The present study was a retrospective cohort study conducted in November 2020 on laboratory confirmed 82 COVID-19 positive patients admitted in Dr. Pinnamaneni Sidhartha Institute of Medical Sciences and Research Foundation, Chinnaoutpally, Gannavaram, from July 12, 2020 to September 30, 2020. A confirmed case with COVID-19 was defined as patients getting a positive result to real-time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) assay for nasal and pharyngeal swab specimens. Only laboratory confirmed cases were included in the study. Suspected cases and clinically diagnosed cases were not part of this study. Data was analysed using IBM Statistical Package for Social Sciences (SPSS) version 21. Independent t-test was used to analyse the data. Results: The 82 COVID-19 patients (age range (15-80 years), 52 males and 30 females), were confirmed by RT-PCR method. On admission, 57 and 25 were divided into non severe and severe groups, respectively. Severe COVID-19 patients had high leucocyte count (3.54-21.3×109/L), neutrophil count (2.59-18.33×109/L), D-dimer (0.08-2.55 μg/mL, p-value <0.0001), C-Reactive Protein (1.47-151.84 mg/L p-value 0.001) and they had significantly lower levels of lymphocyte count (0.17-5.79×109/L, p-value <0.0001). Conclusion: To conclude, dynamically monitoring haematological and coagulation parameters association with comorbid conditions such as diabetes may provide a reliable and convenient method for classifying and predicting the severity and outcomes of patients with COVID-19. This information will be a useful tool for physicians to categorise COVID-19 patients and manage critically ill patients.