The Association of Hematological and Biochemical Parameters With Mortality Among COVID-19 Patients: A Retrospective Study From North India

Background There have been reports published in the Indian setting that describe demographics, clinical characteristics, hospital course, morbidity, and death in coronavirus disease 2019 (COVID-19) patients; however, they are based on small numbers of cases. The current analysis of patients with known outcomes allowed us to gain a better understanding of the disease process and progression in COVID-19 patients, as well as correlate the factors that influence the outcome. Methods This was a record-based, retrospective observational study of patients admitted to a COVID-19 hospital (All India Institute of Medical Sciences (AIIMS), Raipur, India). Between June 1 and August 31, 2021, we gathered medical records of all hospitalized patients having a laboratory-confirmed COVID-19 diagnosis and a known outcome (discharged or died). The extracted data included basic demographics, signs and symptoms, duration of hospitalization, laboratory parameters, and outcomes. Categorical variables were analyzed using either the chi-square test or Fisher's exact test. The level of significance was set at p < 0.05. Results The mean age of the patients was 53.77 +/- 15.85 years. Of the patients, 84.2% have moderate to severe disease, and 15.8% of the patients have mild disease. Furthermore, 26.3% of the subjects were deceased, while 73.7% were discharged. The laboratory parameters that were significantly (p < 0.05) raised among the dead compared to discharged patients included serum total bilirubin (mg/dL), serum direct bilirubin (mg/dL), serum indirect bilirubin (mg/dL), serum urea (mg/dL), serum uric acid (mg/dL), hematocrit (%), total leukocyte counts (/mm(3)), neutrophils (%), serum sodium (Na) (mEq/L), serum chloride (Cl) (mEq/L), and phosphate (mg/L). Conclusion Clinical and laboratory features reflect disease pathophysiology and hence assist doctors in determining the severity of medical sickness. They also help in the creation of clinical care management algorithms that may improve patient outcomes.