Background We aimed to determine the characteristics, risk factors, and outcomes associated with readmission in COVID-19 patients. Methods PubMed, Embase, Web of Science, and Scopus databases were searched to retrieve articles on readmitted COVID-19 patients, available up to September 25, 2021. All studies comparing characteristics of readmitted and non-readmitted COVID-19 patients were included. We also included articles reporting the reasons for readmission in COVID-19 patients. Data were pooled and meta-analyzed using random or fixed-effect models, as appropriate. Subgroup analyses were conducted based on the place and duration of readmission. Results Our meta-analysis included 4823 readmitted and 63,413 non-readmitted COVID-19 patients. The re-hospitalization rate was calculated at 9.3% with 95% Confidence Interval (CI) [5.5%–15.4%], mostly associated with respiratory or cardiac complications (48% and 14%, respectively). Comorbidities including cerebrovascular disease (Odds Ratio (OR) = 1.812; 95% CI [1.547–2.121]), cardiovascular (2.173 [1.545–3.057]), hypertension (1.608 [1.319–1.960]), ischemic heart disease (1.998 [1.495–2.670]), heart failure (2.556 [1.980–3.300]), diabetes (1.588 [1.443–1.747]), cancer (1.817 [1.526–2.162]), kidney disease (2.083 [1.498–2.897]), chronic pulmonary disease (1.601 [1.438–1.783]), as well as older age (1.525 [1.175–1.978]), male sex (1.155 [1.041–1.282]), and white race (1.263 [1.044–1.528]) were significantly associated with higher readmission rates ( P < 0.05 for all instances). The mortality rate was significantly lower in readmitted patients (OR = 0.530 [0.329–0.855], P = 0.009). Conclusions Male sex, white race, comorbidities, and older age were associated with a higher risk of readmission among previously admitted COVID-19 patients. These factors can help clinicians and policy-makers predict, and conceivably reduce the risk of readmission in COVID-19 patients.

Background: Elevated concentrations of serum uric acid (SUA) are associated with several conditions, including cardiovascular disease. The present study aimed to estimate the impact of statin therapy on SUA levels through a systematic review and meta-analysis of clinical trials. Methods: PubMed, Embase, Web of Science, and Scopus were searched on January 14, 2022, to identify eligible clinical trials. The intervention group received statins as monotherapy or in combination with other drugs, and the control group received non-statins or placebo. Studies reporting SUA levels before and after treatment were selected for further analysis. Finally, the data were pooled, and the mean changes in SUA, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides were reported. Results: Out of 1269 identified studies, 23 were included in the review. A total of 3928 participants received statin therapy, and 1294 were included in control groups. We found a significant reduction in SUA levels following statin therapy (mean difference (MD) = -26.67 μmol/L with 95% confidence interval (CI) [-44.75, -8.60] (P = 0.004)). Atorvastatin (MD = -37.93 μmol/L [-67.71, -8.15]; P < 0.0001), pravastatin (MD = -12.64 μmol/L [-18.64, -6.65]; P < 0.0001), and simvastatin (MD = -5.95 μmol/L [-6.14, -5.80]; P < 0.0001), but not rosuvastatin, were significantly associated with a reduction in SUA levels. An analysis comparing different types of statins showed that pravastatin 20-40 mg/day could significantly reduce SUA when compared to simvastatin 10-20 mg/day (-21.86 μmol/L [-36.33,-7.39]; P = 0.003). Conclusion: Statins were significantly associated with a decrease in SUA levels, particularly atorvastatin, which was found to be most effective in lowering SUA. Atorvastatin may be the most appropriate cholesterol-lowering agent for patients with or at risk of hyperuricemia.

Introduction Up to now, numerous randomized controlled trials (RCTs) have examined various drugs as possible treatments for Coronavirus Disease 2019 (COVID-19), but the results were diverse and occasionally even inconsistent with each other. To this point,we performed a systematic review and meta-analysis to assess the comparative effectiveness of pharmacological agents in published RCTs. Areas covered A literature search was performed using PubMed, SCOPUS, EMBASE, and Web of Science databases. RCTs evaluating mortality and the average length of hospital stay to standard of care (SOC)/placebo/control were included. RCTs mainly were classified into five categories of drugs, including anti-inflammatory, antiviral, antiparasitic, antibody and antibiotics. Meta-analysis was done on 5 drugs classes and sub-group meta-analysis was done on single drugs and moderate or severe stage of disease. Expert opinion Mortality and the average length of hospital stay of COVID-19 patients were significantly reduced with anti-inflammatory drugs (odds ratio [OR]: 0.77, 95% confidence interval [CI]: 0.69 to 0.85, P<0.00001, and mean difference [MD]: −1.41, CI:-1.75 to −1.07, P<0.00001, respectively) compared to SOC/control/placebo. Furthermore, antiparasitic was associated with reduced length of hospital stay (MD: −0.65, CI: −1.26 to −0.03, P<0.05) in comparison to SOC/placebo/control. However, no effectiveness was found in other pharmacological interventions.

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Background: The new coronavirus is an agent of respiratory infections associated with thrombosis in vital organs. This study aimed to propose a better diagnosis and treatment of coagulation disorders caused by the new coronavirus (Covid-19). Materials and Methods: Search in Cochrane central, Web of Science, PubMed, Scopus, and Ovid will be done. Also, according to the inclusion criteria, cross-sectional studies, cohort, clinical trial, and case-control will be included without gender and language restriction. Participants will also be Covid-19 patients with coagulation disorders. Any disagreement in the stages of screening, selection, and extraction of data between the two reviewers will be resolved by discussion, then if not resolved, the opinion of expert reviewers will be used. The risk of bias will be assessed using the NOS (Newcastle–Ottawa scale) tool for cross-sectional study, cohort and case-control, and the Cochrane checklist for clinical trials study. Metaanalysis of included studies that are similar based on the methodology will be done. Also, a fixed or random-effect model will be used for this it. Heterogeneity indices (I2), odds ratio (OR), risk ratio (RR), mean difference, and %95 confidence interval will also be calculated by Stata V.13.0 (Corporation, College Station TX). Results: Treatment with anticoagulants will reduce the severity of thrombosis and lung disease in patients. D-dimer measurement will also be a diagnosis indicator of thrombosis. Conclusions: Simultaneous study of coagulation disorders and thrombosis in patients and development of a Godliness based on it will play a treatment role in the follow-up of the coronavirus disease.