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Objectives: To evaluate the effect and safety of corticosteroid (CS) treatment in patients with severe fever with thrombocytopenia syndrome (SFTS). Methods: Patients with and without CS were retrospectively compared by Cox regression and 1:1 propensity score matching analysis to evaluate the effects of CS on mortality and secondary infections in patients with SFTS. Results: A total of 467 patients with SFTS were enrolled in the cohort study, there were 52 fatal cases and 415 nonfatal cases, the overall fatality rate was 11.1%. The mortality was observed in 36/144 (25%) and 16/323 (5%) patients in the CS-treated and non-CS-treated groups, respectively (P < 0.001). Multi variate Cox regression analysis showed that the difference was not statistically significant for CS treatment in fatality (P > 0.05, aHR 0.767, 95% CI 0.360-1.634). Difference in survival time between the CS-treated and non-CS-treated groups after propensity score matching had no statistical significance (Log-rank test P = 0.390), whereas there was a significant difference in secondary infections between the CS-treated and non-CS-treated groups (P = 0.007). Conclusion: Although the CS treatment had no impact on fatality in patients with SFTS, it increased the risk of secondary infections. Administration of CS in patients with SFTS should be carefully considered and evaluated the balance between therapeutic efficacy and adverse effects.

Abstract Background Patients with severe Coronavirus Disease 2019 (COVID-19) will progress rapidly to acute respiratory failure or death. We aimed to develop a quantitative tool for early predicting mortality risk of patients with COVID-19. Methods 301 patients with confirmed COVID-19 admitted to Main District and Tumor Center of the Union Hospital of Huazhong University of Science and Technology (Wuhan, China) between January 1, 2020 to February 15, 2020 were enrolled in this retrospective two-centers study. Data on patient demographic characteristics, laboratory findings and clinical outcomes was analyzed. A nomogram was constructed to predict the death probability of COVID-19 patients. Results Age, neutrophil-to-lymphocyte ratio, d-dimer and C-reactive protein obtained on admission were identified as predictors of mortality for COVID-19 patients by LASSO. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.921 and 0.975 for the derivation and validation cohort, respectively. An integrated score (named ANDC) with its corresponding death probability was derived. Using ANDC cut-off values of 59 and 101, COVID-19 patients were classified into three subgroups. The death probability of low risk group (ANDC 101) was more than 50%, respectively. Conclusion The prognostic nomogram exhibited good discrimination power in early identification of COVID-19 patients with high mortality risk, and ANDC score may help physicians to optimize patient stratification management.