ABSTRACT
Introduction An array of routinely accessible serum biomarkers was assessed to explore their overall impact on severity and mortality in coronavirus disease 2019. Materials and Methods A retrospective analysis of 1,233 adults was conducted. The study groups comprised 127 nonsurvivors and 1,106 survivors. Data for demographic details, clinical presentations, and laboratory reports were recorded from the medical record section. The predictors were analyzed for their influence on mortality. Results The mean (+ standard deviation) age of the patients in the nonsurvivor group was 58.8 (13.8) years. The mean age (56.4 years) was highest in severe grade patients. The odds ratio for death was 2.72 times for patients above the age of 40 years. About 46% of nonsurvivors died within 5 days of admission. Males were found to be more prone to death than females by a factor of 1.36. Serum urea depicted highest sensitivity (85%) for nonsurvival at 52.5 mg/dL. Serum albumin (3.23 g/dL), albumin-to-globulin ratio (0.97), and C-reactive protein-to albumin ratio (CAR) (2.08) showed a sensitivity of more than 70% for mortality outcomes. The high hazard ratio (HR) for deceased patients with hyperkalemia was 2.419 (95% confidence interval [CI] = 1.96-2.99; p < 0.001). The risk for nonsurvival was increased with elevated serum creatinine by 15.6% and uric acid by 21.7% ( p < 0.001). The HR for hypoalbuminemia was 0.254 (95% CI: 0.196-0.33; p < 0.001) and CAR was 1.319 (95% CI: 1.246-1.397; p < 0.001). Saturation of oxygen ( p < 0.001), lactate dehydrogenase ( p = 0.006), ferritin ( p = 0.004), hyperuricemia ( p = 0.027), hyperkalemia ( p < 0.001), hypoalbuminemia ( p = 0.002), and high CAR values (0.031) served as potential predictors for mortality. Conclusion Adjusting for all the predictor variables, serum uric acid, potassium, albumin, and CAR values at the time of admission were affirmed as the potential biomarkers for mortality.
ABSTRACT
Background and Objective: This study explored the role of various laboratory biomarkers on inflammatory indices for predicting disease progression toward severity in COVID-19 patients. Methods: This retrospective study was conducted on 1233 adults confirmed for COVID-19. The participants were grouped undermild, moderate, and severe grade disease. Serum bio-inflammatory index (SBII) and systemic inflammatory index (SII) were calculated and correlated with disease severity. The study variables, including clinical details and laboratory variables, were analyzed for impact on the inflammatory indices and severity status using a sequential multiple regression model to determine the predictors for mortality. Receiver operating characteristics defined the cut-off values for severity. Results: Among the study population, 56.2%, 20.7%, and 23.1% were categorized as mild, moderate, and severe COVID-19 cases. Diabetes with hypertension was the most prevalent comorbid condition. The odds for males to have the severe form of the disease was 1.6 times (95% CI = 1.18-2.18, P = 0.002). The median (inter-quartile-range) of SBII was 549 (387.84-741.34) and SII was 2097.6 (1113.9-4153.73) in severe cases. Serum urea, electrolytes, gamma-glutamyl transferase, red-cell distribution width-to-hematocrit ratio, monocytopenia, and eosinopenia exhibited a significant influence on the SpO2, SBII, and SII. Both SBII (r = -0.582, P < 0.001) and SII (r = -0.52, P < 0.001) strongly correlated inversely with SpO2 values [Figures 3a and 3b]. More than 80% of individuals admitted with severe grade COVID-19 had values of more than 50th percentile of SBII and SII. The sensitivity and specificity of SBII at 343.67 for severity were 81.4% and 70.1%, respectively. SII exhibited 77.2% sensitivity and 70.8% specificity at 998.72. Conclusion: Serial monitoring of the routinely available biomarkers would provide considerable input regarding inflammatory status and severity progression in COVID-19.
