Objectives The majority of available scores to assess mortality risk of coronavirus disease 19 (COVID-19) patients in the emergency department have high risk of bias. Therefore, our aim was to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients, and to compare this score with other existing ones. Methods Consecutive patients (≥18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Results Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO 2 /FiO 2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833 to 0.885]) and Spanish (0.894 [95% CI 0.870 to 0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.

Summary Population‐based prevalence surveys of Covid‐19 contribute to establish the burden of infection, the role of asymptomatic and mild infections in transmission, and allow more precise decisions about reopen policies. We performed a systematic review to evaluate qualitative aspects of these studies, assessing their reliability and compiling practices that can influence the methodological quality. We searched MEDLINE, EMBASE, bioRxiv and medRxiv, and included cross‐sectional studies using molecular and/or serological tests to estimate the prevalence of Covid‐19 in the general population. Survey quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. A correspondence analysis correlated methodological parameters of each study to identify patterns related to higher, intermediate and lower risks of bias. The available data described 37 surveys from 19 countries. The majority were from Europe and America, used antibody testing, and reached highly heterogeneous sample sizes and prevalence estimates. Minority communities were disproportionately affected by Covid‐19. Important risk of bias was detected in four domains: sample size, data analysis with sufficient coverage, measurements in standard way and response rate. The correspondence analysis showed few consistent patterns for high risk of bias. Intermediate risk of bias was related to American and European studies, municipal and regional initiatives, blood samples and prevalence >1%. Low risk of bias was related to Asian studies, nationwide initiatives, reverse‐transcriptase polymerase chain reaction tests and prevalence <1%. We identified methodological standards applied worldwide in Covid‐19 prevalence surveys, which may assist researchers with the planning, execution and reporting of future population‐based surveys.

Population-based prevalence surveys of COVID-19 contribute to establish the burden and epidemiology of infection, the role of asymptomatic and mild infections in transmission, and allow more precise decisions about reopen policies. We performed a systematic review to evaluate qualitative aspects of these studies, their reliability, and biases. The available data described 37 surveys from 19 countries, mostly from Europe and America and using antibody testing. They reached highly heterogeneous sample sizes and prevalence estimates. Disproportional prevalence was observed in minority communities. Important risk of bias was detected in four domains: sample size, data analysis with sufficient coverage, measurements in standard way, and response rate. The correspondence analysis showed few consistent patterns for high risk of bias. Intermediate risk of bias was related to American and European studies, blood samples and prevalence >1%. Low risk of bias was related to Asian studies, RT-PCR tests and prevalence <1%.

Objective: To develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones. Design: Cohort study Setting: The Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Participants: Consecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay. Main outcome measures: In-hospital mortality Results: Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.

Oculo-auriculo-vertebral spectrum (hemifacial microsomia/OAVS, OMIM #164210) is a heterogenous and congenital condition caused by a morphogenesis defect of the first and second pharyngeal arches. Etiology includes unknown genetic, environmental factors and chromosomal alterations, which 22q11.2 region is the most frequently reported. Several candidate genes for OAVS have been proposed; however, none has been confirmed as causative of the phenotype. This review aims to sum up all clinical and molecular findings in 22q region of individuals diagnosed with OAVS and to investigate genes that may be involved in the development of the spectrum. A search was performed in PubMed using all entry terms to OAVS and Chromosome 22q11. After screening, 11 papers were eligible for review. Deletions and duplications in the q11.2 region were the most frequent (18/22) alterations reported and a total of 68 genes were described. Our systematic review reinforces the hypothesis that 22q11 region is a candidate locus for OAVS as well as CLTCL1, GSC2, HIRA, MAPK1, TBX1, and YPEL1 as potential candidates genes for genotype-phenotype correlation. Complementary studies regarding genes interaction involved in the 22q11 region are still necessary in the search for a genotype-phenotype association, since the diagnosis of OAVS is a constant medical challenge.