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Objective The purpose of our study was to assess organ function in 102 patients with severe COVID-19 infections, using retrospective clinical analysis. Material and methods A retrospective analysis was conducted on 102 patients with severe COVID-19 infections. The patients were divided into a survival group ( n = 73) and a non-survival group ( n = 29) according to their prognosis. The age, sex, underlying diseases, clinical laboratory data within 48 h (routine blood tests, ALT, AST, TBIL, ALB, BUN, CR, D-Dimer, PT, APTT, FIB, F VIII:C, CK-MB, CK, and LDH), and ventilation status were collected. The organ functions of these severe COVID-19 patients were assessed by comparing the differences between the two groups. Results AST, BUN, CR, CK-MB, LDH, and CK in the non-survival group were higher than those in the survival group, and the differences were statistically significant ( P < 0.05). D-Dimer, PT, FIB, and F VIII:C in the non-survival group were higher than the values observed in the survival group, and the differences were statistically significant ( P < 0.05). PLT, AST, BUN, CR, D-Dimer, PT, FIB, F VIII:C, CK-MB, CK, and LDH predicted the area under the ROC curve (AUC) of the COVID19 endpoint events and were 0.721, 0.854, 0.867, 0.757, 0.699, 0.679, 0.715, 0.811, 0.935, and 0.802, respectively. Conclusion The results showed that there were different degrees of damage to the liver, kidneys, blood coagulation, and heart function in the non-survival group. In addition, PLT, AST, BUN, CR, D-Dimer, PT, FIB, F VIII:C, CK-MB, CK, and LDH had value in evaluating disease prognosis.

Background: Since December 2019, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread across the world. Age and underlying diseases have been reported as predictors of mortality in 2019-nCoV infection. Charlson's weighted index of comorbidities (WIC) and acute physiology and chronic health evaluation (APACHE) II are two frequently-used measures of comorbidity. In this study, we have assessed the performance of WIC and APACHE II in predicting the mortality of COVID-19 patients.Methods: A total of 76 adult patients with COVID-19 were admitted to a designated hospital in Huangshi province from 1 January 2020 to 29 February 2020. Clinical data including age, gender, underlying diseases, and hospital mortality were collected. The APACHE II and WIC scores were assessed within the first 24 hours of admission. Univariate and multiple logistic regression analyses were used to compare the performance of WIC, APACHE II, and joint detection. The area under the receiver operating characteristic curve (AUC) was used to predict the hospital mortality. Results: Of the 76 enrolled patients, 57 patients survived, and 19 died. The surviving patients had significantly lower WIC and APACHE II than the non-surviving patients (p-value < 0.05). The AUC for the hospital mortality was 0.814 (95% confidence interval (CI) 0.705-0.923) of WIC, 0.854 (95% CI 0.705-0.956) of APACHE II and 0.891(95% CI 0.830-0.966) for the joint detection. The diagnostic value of the joint detection was found to be better than that of WIC (p-value= 0.002) or APACHE II (p-value = 0.042). Conclusions: The WIC and APACHE II scores might serve as independent determinants for the hospital mortality associated with COVID-19 patients. The combined use of WIC and APACHE II is more predictive than individuals．