Analysis of Biochemical and Inflammatory Markers for Predicting COVID-19 Severity: Insights From a Tertiary Healthcare Institution of Eastern India

Background Coronavirus disease-19 (COVID-19) patients often deteriorate rapidly based on viral infection-related inflammation and the subsequent cytokine storm. The clinical symptoms were found to be inconsistent with laboratory findings. There is a need to develop biochemical severity score to clos...

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Vydáno v:Curēus (Palo Alto, CA) Ročník 15; číslo 1; s. e33893
Hlavní autoři: Kumari, Suchitra, Nayak, Saurav, Tripathy, Swagata, Bhuniya, Sourin, Mangaraj, Manaswini, Ramadass, Balamurugan, Sahu, Suchanda, Bandyopadhyay, Debapriya, Dash, Prakruti, Saharia, Gautom K
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Springer Nature B.V 17.01.2023
Cureus
Témata:
Algorithms
Automation
Biochemistry
COVID-19
Creatinine
Cytokine storm
Dehydrogenases
Enzymes
Epidemiology/Public Health
Fatalities
Granulocytes
Growth factors
Immunoassay
Infectious Disease
Intensive care
Internal Medicine
Liver
Medical laboratories
Mortality
Patients
Phosphatase
Population
Potassium
Proteins
Regression analysis
Tumor necrosis factor-TNF
Uric acid
India
predictive modelling
covid 19
predictive risk factors
severity scoring
medical icu
ISSN:2168-8184, 2168-8184
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Shrnutí:Background Coronavirus disease-19 (COVID-19) patients often deteriorate rapidly based on viral infection-related inflammation and the subsequent cytokine storm. The clinical symptoms were found to be inconsistent with laboratory findings. There is a need to develop biochemical severity score to closely monitor COVID-19 patients. Methods This study was conducted in the department of biochemistry at All India Institute of Medical Sciences (AIIMS) Bhubaneswar in collaboration with the intensive care unit. Laboratory data of 7,395 patients diagnosed with COVID-19 during the first three waves of the pandemic were analyzed. The serum high sensitivity high-sensitivity C-reactive protein (hs-CRP, immuno-turbidity method), lactate dehydrogenase (LDH, modified Wacker et al. method), and liver enzymes (kinetic-UV method) were estimated by fully automated chemistry analyzer. Serum ferritin and interleukin-6 (IL-6) were measured by one-step immunoassay using chemiluminescence technology. Three models were used in logistic regression to check for the predictive potential of biochemical parameters, and a COVID-19 biochemical severity score was calculated using a non-linear regression algorithm. Results The receiver operating characteristic curve found age, urea, uric acid, CRP, ferritin, IL6, and LDH with the highest odds of predicting ICU admission for COVID-19 patients. COVID-19 biochemical severity scores higher than 0.775 were highly predictive (odds ratio of 5.925) of ICU admission (AUC=0.740, p<0.001) as compared to any other individual parameter. For the validation, 30% of the total dataset was used as testing data (n=2095) with a sensitivity of 68.3%, specificity of 74.5%, and odds ratio of 6.304. Conclusion Age, urea, uric acid, ferritin, IL6, LDH, and CRP-based predictive probability algorithm calculating COVID-19 severity was found to be highly predictive of ICU admission for COVID-19 patients.
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ISSN:2168-8184
2168-8184
DOI:10.7759/cureus.33893