Serum Cystatin C Levels as a Predictor of Severity and Mortality Among Patients With COVID-19 Infection.
Cureus
; 15(7): e42003, 2023 Jul.
Article
em En
| MEDLINE
| ID: mdl-37593314
INTRODUCTION: The pandemic caused by SARS Corona Virus-2 (COVID-19) has caused widespread mortality globally. The hallmark of the disease is the "cytokine storm," which is caused due to dysregulated immune system activation. Numerous inflammatory markers are used to predict the severity and mortality of the infection. Serum Cystatin C levels are associated with immune responses to exogenous and endogenous antigens. Our study was done to assess serum cystatin C as a marker of severity and mortality among patients admitted with COVID-19 infection. METHODOLOGY: This cross-sectional study was conducted in a tertiary care center in South India. Sixty-nine patients with mild and severe COVID-19 infection admitted to the hospital were included in the study. Serum Cystatin C levels were estimated at admission. The levels were correlated with disease severity and mortality. Receiver operating characteristic curves (ROCs) was constructed for Cystatin C to predict severity and mortality. The computation of sensitivity, specificity, and positive and negative predictive values was done using optimal cut-off points. SPSS 18 was used for the statistical analysis. Version 18.0 of PASW Statistics for Windows. SPSS Inc., Chicago. RESULTS: Out of 69 patients, 28 (40.5%) had a mild illness, and 41 patients (59.4%) had severe COVID-19 illness. Mean serum Cystatin C levels measured at the time of admission among patients with mild illness was 1.83 (SD-1.53), and among patients with severe illness was 3.84 (SD- 2.59) (p<0.001). The area under receiver operating characteristic curves (ROC) for serum cystatin C for predicting COVID-19 severity and mortality was 0.904 and 0.768, respectively (p<0.001). CONCLUSION: Patients with severe COVID-19 disease had considerably higher serum levels of Cystatin C than those with mild COVID-19 illness. Cystatin C levels can be useful for predicting mortality and severity among patients admitted with COVID-19 infection.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article