To describe the clinical characteristics, laboratory results, imaging findings, and in-hospital outcomes of COVID-19 patients admitted to Brazilian hospitals. Methods: A cohort study of laboratory-confirmed COVID-19 patients who were hospitalized from March 2020 to September 2020 in 25 hospitals. Data were collected from medical records using Research Electronic Data Capture (REDCap) tools. A multivariate Poisson regression model was used to assess the risk factors for in-hospital mortality. Results: For a total of 2,054 patients (52.6% male; median age of 58 years), the in-hospital mortality was 22.0%; this rose to 47.6% for those treated in the intensive care unit (ICU). Hypertension (52.9%), diabetes (29.2%), and obesity (17.2%) were the most prevalent comorbidities. Overall, 32.5% required invasive mechanical ventilation, and 12.1% required kidney replacement therapy. Septic shock was observed in 15.0%, nosocomial infection in 13.1%, thromboembolism in 4.1%, and acute heart failure in 3.6%. Age >= 65 years, chronic kidney disease, hypertension, C-reactive protein ! 100 mg/dL, platelet count < 100 Â 10 9 /L, oxygen saturation < 90%, the need for supplemental oxygen, and invasive mechanical ventilation at admission were independently associated with a higher risk of in-hospital mortality. The overall use of antimicrobials was 87.9%. Conclusions: This study reveals the characteristics and in-hospital outcomes of hospitalized patients with confirmed COVID-19 in Brazil. Certain easily assessed parameters at hospital admission were independently associated with a higher risk of death. The high frequency of antibiotic use points to an over-use of antimicrobials in COVID-19 patients.

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Over the last decade, there has been a 10‐fold increase in the number of published systematic reviews of prevalence. In meta‐analyses of prevalence, the summary estimate represents an average prevalence from included studies. This estimate is truly informative only if there is no substantial heterogeneity among the different contexts being pooled. In systematic reviews, heterogeneity is usually explored with I‐squared statistic (I2), but this statistic does not directly inform us about the distribution of effects and frequently systematic reviewers and readers misinterpret this result. In a sample of 134 meta‐analyses of prevalence, the median I2 was 96.9% (IQR 90.5–98.7). We observed larger I2 in meta‐analysis with higher number of studies and extreme pooled estimates (defined as <10% or >90%). Studies with high I2 values were more likely to have conducted a sensitivity analysis, including subgroup analysis but only three (2%) systematic reviews reported prediction intervals. We observed that meta‐analyses of prevalence often present high I2 values. However, the number of studies included in the meta‐analysis and the point estimate can be associated with the I2 value, and a high I2 value is not always synonymous with high heterogeneity. In meta‐analyses of prevalence, I2 statistics may not be discriminative and should be interpreted with caution, avoiding arbitrary thresholds. To discuss heterogeneity, reviewers should focus on the description of the expected range of estimates, which can be done using prediction intervals and planned sensitivity analysis.

Polycystic ovary syndrome (PCOS) is the most prevalent endocrine disorder affecting women of reproductive age. PCOS has been associated with distinct metabolic and cardiovascular diseases and with autoimmune conditions, predominantly autoimmune thyroid disease (AITD). AITD has been reported in 18–40% of PCOS women, depending on PCOS diagnostic criteria and ethnicity. The aim of this systematic review and meta-analysis was to summarize the available evidence regarding the likelihood of women with PCOS also having AITD in comparison to a reference group of non-PCOS women. We systematically searched EMBASE and MEDLINE for non-interventional case control, cross-sectional or cohort studies published until August 2017. The Ottawa–Newcastle Scale was used to assess the methodological quality of studies. Statistical meta-analysis was performed with R. Thirteen studies were selected for the present analysis, including 1210 women diagnosed with PCOS and 987 healthy controls. AITD was observed in 26.03 and 9.72% of PCOS and control groups respectively. A significant association was detected between PCOS and chance of AITD (OR = 3.27, 95% CI 2.32–4.63). Notably, after geographical stratification, the higher risk of AITD in PCOS women persisted for Asians (OR = 4.56, 95% CI 2.47–8.43), Europeans (OR = 3.27, 95% CI 2.07–5.15) and South Americans (OR = 1.86, 95% CI 1.05–3.29). AIDT is a frequent condition in PCOS patients and might affect thyroid function. Thus, screening for thyroid function and thyroid-specific autoantibodies should be considered in patients with PCOS even in the absence of overt symptoms. This systematic review and meta-analysis is registered in PROSPERO under number CRD42017079676.

Aims: The aim of this study was to assess the effects of orlistat on weight lossrelated clinical variables in overweight/obese women with polycystic ovary syndrome (PCOS) and to compare treatment with orlistat vs. metformin in this group. Methods: We conducted a systematic review and meta-analysis of the evidence about the use of orlistat in women with PCOS. We searched the literature published until May 2015 in MEDLINE, Cochrane Central Register of Controlled Trials and LILACS. Results: Of 3951 studies identified, nine were included in the systematic review (three prospective, non-randomised studies and six randomised control trials). Eight studies used the Rotterdam criteria and 1 used NIH criteria to diagnose PCOS. Data suggest that orlistat promotes a significant reduction in BMI/ weight in overweight/obese PCOS women. Eight studies evaluated orlistat impact on testosterone. Seven reported an improvement in testosterone levels. Eight studies evaluated impact on insulin resistance, and five reported improvement. Finally, five studies evaluated impact on lipid profile, and four reported improvement. Three randomised control trials were included in the fixed effects model meta-analysis for a total of 121 women with PCOS. Orlistat and metformin had similar positive effects on BMI (À0.65%, 95% CI: À2.03 to 0.73), HOMA (À3.60%, 95% CI: À16.99 to 9.78), testosterone (À2.08%, 95% CI: À13.08 to 8.93) and insulin (À5.51%, 95% CI: À22.27 to 11.26). Conclusion(s): The present results suggest that orlistat leads to significant reduction in BMI/body weight in PCOS. In addition, the available evidence indicates that orlistat and metformin have similar effects in reducing BMI, HOMA, testosterone and insulin in overweight/obese PCOS women.This study was registered in PROSPERO under number CRD42014012877. Review criteria• We conducted a systematic review and metaanalysis of the evidence about the effect of orlistat on weight, BMI, androgens and insulin resistance in women with polycystic ovary syndrome.• We systematically searched literature published until May 2015 in electronic databases MEDLINE, Cochrane Central Register of Controlled Trials and LILACS.• We conducted a descriptive systematic review and a fixed effects model meta-analysis and evaluated heterogeneity using the I 2 statistics and Cochran's Q test. Message for the clinic• Orlistat leads to significant reduction in BMI/body weight in overweight/obese PCOS.• Orlistat and metformin have similar effects in reducing BMI, testosterone and insulin/HOMA in overweight/obese PCOS women.