Background: To assess the efficacy and safety of corticosteroids in COVID-19 patients compared with standard care or placebo. Methods: Electronic databases were searched to identify relevant studies. The mortality, adverse events, and other data from studies were pooled for statistical analysis. Results: Ten randomized clinical trials were eligible for inclusion. Corticosteroid treatment in COVID-19 patients did not significantly reduce the risk of death (RR: 0.93; CI: 0.82, 1.05) and the need for mechanical ventilation (RR: 0.82; CI: 0.62, 1.08). No mortality reduction was also observed in the subgroup of patients requiring mechanical ventilation (RR: 0.90; CI: 0.79-1.03). The use of corticosteroids increased mortality in the subgroup of patients not requiring oxygen support (RR: 1.24; CI: 1.00-1.55). The survival benefit was observed in a low dosage of corticosteroids (RR: 0.90; CI: 0.84-0.97) and dexamethasone (RR: 0.90; 95% CI: 0.79-1.04). There was no difference in the rates of adverse events (RR: 1.13; CI: 0.58, 2.20) and secondary infections (RR: 0.87; CI: 0.66, 1.15). Conclusion: Corticosteroid treatment did not convincingly improve survival in severe COVID-19 patients. Low-dose dexamethasone could be considered as a drug for the treatment of COVID-19 patients. More high-quality trials are needed to further verify this conclusion. Expert Opinion: The effect of corticosteroids on patient survival highly depended on the selection of the right dosage and type and in a specific subgroup of patients. This meta-analysis, which included more RCTs, evaluated the safety and efficacy in severe COVID-19 patients and analyzed the effects of different types of corticosteroid treatments. Corticosteroid treatment did not convincingly improve survival in severe COVID-19 patients. But the low dose dexamethasone appear to have a role in the management of severe COVID-19 patients.

BACKGROUND Serum uric acid is known to be a positive association with bone mineral density, but few studies have been large enough to investigate which range of serum uric acid levels is protective against osteoporosis/osteopenia. This study showed specific ranges of serum uric acid levels are beneficial for bone health. OBJECTIVE To explore the relationship between serum uric acid and bone mineral density in people aged 18 or older. METHODS A cross-sectional study was conducted to examine the association between SUA and BMD from five cycles of NHANES (2005-2010, 2013-2014, and 2017-2020). Binary logistic regression models and restricted cubic spline models were used to evaluate the association between SUA and osteoporosis/osteopenia. RESULTS 19881 participants aged ≥ 18 years were included. Higher SUA levels were significantly associated with a lower incidence of osteoporosis/osteopenia after adjustment for potential confounders and after subgroup analysis. The results of restricted cubic spline regression analysis demonstrated that there was a U-shape non-linear relationship between SUA and osteoporosis/osteopenia in all people (P Nonlinear =0.0114), male subgroup (P Nonlinear=0.0040), female subgroup (P Nonlinear =0.0235) and aged≥60 years subgroup (P Nonlinear =0.0001). Meanwhile, a transverse S-shaped relationship was observed in the obese people subgroup (P Nonlinear =0.0486). The cutoff SUA level was higher in males (456 μmol/L) than that in females (376 μmol/L) at the lowest risk of osteoporosis/osteopenia, and cutoff SUA levels in both groups were higher than that in normal SUA levels. The optimal SUA level in the elderly population is 408 μmol/L, and the optimal SUA level in the obese population is 440 μmol/L. CONCLUSIONS A complicated relationship between SUA and BMD in different populations was observed. Maintaining SUA within a moderate range may be beneficial to bone health.

Background Accurate evaluation of mortality risk in polytrauma patients is crucial for guiding the precision treatment strategy. There are few scales designed to provide an early assessment of mortality risk. However, the complexity of available scoring systems limits their application in pre-hospital care. Here, we established a GAS-TRS system to estimate the risk of early death for individual polytrauma patients and assess the early mortality risk in the individual patient.Methods We performed a secondary analysis from public Database. RCS and Multivariate Logistic regression analyses were used to screen potential prognostic factors for nomogram model. The VIF method examined multicollinearity, and VIF ≥ 5 suggested multicollinearity in this model. CMA was used to characterize the causality relationship in nomogram model. A four-layer back-propagation artificial neural network (BP-ANN) model was built by neuralnet package on R software. AUC of ROC analysis or F1 score were used to analyze the quality of predictive performance of GAS-TRS system. DCA and precision-recall curves were used to make up for the limitations of ROC curves.Results A total of 2406 patients were included in this analysis. Logistic regression analysis predicted four independent risk factors for nomogram model, including age (OR=1.03, 95%CI:1.02~1.03), GCS (OR=0.83, 95%CI:0.79~0.86), BE (OR=0.95, 95%CI:0.91~0.99) and serum lactic acid (OR=1.30, 95%CI:1.20~1.41) with an AUC of 0.88. Causal mediation analysis performed the mediation effect that lactate, age and BE accounted for 1.7%,0.7% and 3.0% indirect effect.The calibration curve showed model has good highly consistent with actual condition after bootstrapping. DCA showed the net benefit probability was between 2% and 85% and could bring more benefits for predicting early mortality.Then the input neurons were selected step by step in BP-ANN model. An optimal BP-ANN with an AUC of 0.91and AUPRC of 0.79 was established.Conclusion We established a GAS-TRS predictive system which includes a quick prognostic nomogram model and a precise BP-ANN model to evaluate early mortality within 72 hours for polytrauma patients. This scoring system might be practical and more efficient in identifying high-risk polytrauma patients. Moreover, this system may also guide triaging and precise initial individual treatment strategy for pre-hospital medical personnel.