BackgroundCoronavirus Disease-2019 (COVID-19) is an emerging acute infectious disease that was first discovered in Wuhan, Hubei Province, China. Since then, it has quickly spread to over one hundred cities around the world. Therefore, it is crucial to identify the risk factors of in-hospital mortality and disease severity for COVID-19 patients. MethodsWe firstly proposed a biomarker ratio, lactate dehydrogenase(LDH) to albumin ratio (LAR) may be more reliable to assess the predictive value of LAR for in-hospital mortality and early identification of critical COVID-19 patients. A retrospective study was conducted including patients (≥18 years old) with laboratory-confirmed COVID-19 infection who had been discharged or had died from 1 February to 29 February, 2020. ResultsThe study included 321 patients with COVID-19. The median age of the 321 patients was 63.0 (IQR 51.0-70.0), ranging from 19 to 95 years old and 180 (56.1%) patients were male. 142 (44.2%) patients had 1 or more coexisting comorbidity. The most common symptoms on admission were fever(289[90%]) and cough(258[80.4%]). In multivariable logistic regression, only older age (OR, 1.11; 95% CI, 1.05-1.16), WBC count (OR, 1.26; 95% CI, 1.11-1.44), lymphocyte count (OR, 0.78; 95% CI, 0.62-0.99) and LAR (OR, 1.29; 95% CI, 1.18-1.40) were found to be significantly associated with in-hospital death. ROC analysis showed that LAR had a higher AUC (0.917) and the highest specificity(84.0%) and sensitivity(84.6%). Furthermore, the results showed that LAR had a higher AUC (0.931) to differentiate critical from mild patients and had a sensitivity of 87.7% and a specificity of 82.1%. Besides, LAR had an AUC (0.861) to differentiate critical from severe patients and had a sensitivity of 86.0% and a specificity of 73.8% and the role of LAR to distinguish severe from mild patients was the worst. ConclusionsTo the best of our knowledge, this study is the first for us to explore the predictive value of LAR for in-hospital mortality and disease severity. A high LAR appears to predict higher odds of mortality and differentiate critical patients from mild or severe COVID-19 patients.
