Abstract

Objective: Serum biomarkers are important for accurately predicting clinical outcomes in coronavirus disease 2019 (COVID-19) patients. Although previous studies showed that lymphopenia in patients is related to disease severity, it is unclear how other serum biomarkers improve the prognostic accuracy of lymphopenia. Changes in urea, and lactate dehydrogenase (LDH) were noted to have considerable predictive value in determining the severity of disease in COVID-19 patients. Therefore, the purpose of this study is to determine whether increases in urea, and LDH are linked to worse outcomes in COVID-19 patients and whether the urea/lymphocyte and LDH/lymphocyte ratios improve the prognostic accuracy of lymphopenia.
 Methods: The data of confirmed COVID-19 patients in our emergency department (ED) between March 2020, and January 2021, were analyzed retrospectively. The area under the curve (AUC) and logistic regression analysis were used to evaluate the discriminative power of the urea/lymphocyte and LDH/lymphocyte ratios in estimating 30-day mortality.
 Results: The study included 795 confirmed COVID-19 patients admitted to the ED. Twenty-three patients (2.9%) died, and 772 (97.1%) survived in 30 days. The median age of the patients was 51. The number of males (n: 447, 56.2%) was higher than females (n: 348, 43.8%). The ratios of urea/lymphocyte and LDH/lymphocyte were significantly higher in non-survivors (median: 71.21 and 754.1, respectively) compared to survivors (median: 19.51 and 297.42, respectively) (P<0.001). The AUC for 30-day mortality for the urea/lymphocyte and LDH/lymphocyte ratios was 0.864 and 0.840, respectively. Multivariate logistic regression adjustment found the urea/lymphocyte ratio to be an independent and significant predictor of mortality (P=0.007). The optimum cut-off point for the urea/lymphocyte ratio was 28.07, which had a 91.3% sensitivity and a 68.6% specificity.
 Conclusion: The urea/lymphocyte and LDH/lymphocyte ratios are useful markers that can be evaluated independently to identify high-risk patients and predict the prognosis of COVID-19.

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