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Background: Lung diseases are common in Patients with End Stage Kidney Disease (ESKD) making the differential diagnosis with COVID-19 a challenge. This study describes pulmonary chest tomography (CT) findings in hospitalized ESKD on renal replacement therapy (RRT) patients with clinical suspicion of COVID-19 and compare image characteristics of positive versus negative cases. Method(s): ESKD individuals referred to Emergency Department older than 18 with clinical suspicion of COVID-19 were recruited. Epidemiological, baseline clinical information was extracted from electronic health records. Pulmonary CT was classified as typical, indeterminate, atypical or negative. We then compare CT findings of positive and negative COVID-19 patients. Result(s): We recruited 109 patients (62,3% COVID-19 positive) between March and December 2020. Mean age was 60 +/- 12.5 years-old, 43% were female and the most common etiology of ESKD was diabetes. Median time on dialysis was 36 months, Interquartile range=12-84. The most common pulmonary lesion on CT was ground glass opacities. Typical CT pattern was more common in COVID-19 patients (40(61%) vs 0(0%), p<0.001). Sensitivity was 60.61% (40/66) and specificity was 100% (40/40). Positive predictive value and negative predictive value were 100% and 62.3%, respectively. Atypical CT pattern was more frequent in COVID-19 negative patients (9(14%) vs 24(56%), p<0.001), while the indeterminate pattern was similar in both groups (13(20%) vs 6(14%), p=0.606), and the negative pattern was more common in COVID-19 negative patients (4(6%) vs 12(28%), p=0.002). Conclusion(s): In hospitalized patients with ESKD on RRT an atypical chest CT pattern cannot adequately rule out the diagnosis of COVID-19.
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Background. The COVID-19 pandemic created the most severe global education disruption in history. According to UNESCO, at the peak of the crisis over 1.6 billion learners in more than 190 countries were out of school. After one year, half of the world's student population is still affected by full or partial school closures. Here we investigated whether or not it is possible to build a multivariate score for dynamic school decision-making specially in scenarios without population-scale RT-PCR tests. Methods. Normality rate is based on a COVID-19 risk matrix (Table 1). Total score (TS) is obtained by summing the risk scores for COVID-19, considering the six parameters of the pandemic in a city. The COVID-19 Normality Rate (CNR) is obtained by linear interpolation in such a way that a total score of 30 points is equivalent to a 100% possibility of normality and, in a city with only six total points would have zero percent chance of returning to normality: CNR = (TS - 6)/24 (%). The criteria for opening and closing schools can be defined based on the percentages of return to normality (Table 2). Results. at June 3rd, 2021, we evaluated all 5,570 Brazilian cities (Figure 1): 2,708 cities (49%) with COVID-19 normality rate less than 50% (full schools closure), 2,223 cities (40%) with normality rate between 50% and 70% (in-person learning only for 5 years and 8 months-old children), 583 with normality rate between 71% and 80% (in-person learning extended to children age 12 years and less), 583 cities (1%) with normality rate between 81% to 90% (in-person learning extended to the student population age 18 years), and just one city with 92% COVID-19 normality rate (in-person learning extended to all the student population). We calculated the COVID-19 normality rate between January and May, 2021, in four countries: Brazil, USA, UK, and Italy (Figure 2). At Jun, 3rd, 2021, percentage of people fully vaccinated in Brazil varied from 0% to 69%, an average of 11%. Conclusion. COVID-19 vaccination programs take several months to implement. Besides fully vaccination of the population, it is important to check if people became really safe from the virus. The COVID-19 Normality Rate is a double check multivariate score that can be used as a criteria for optimal time to return to in-person learning safely.
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Background. The Brazilian Football Confederation (CBF) protocol to control the spread of COVID-19 among professional soccer players is based on four cornerstone measures: (1) Tracing all symptomatic and asymptomatic COVID-19 cases by clinical monitoring and nasal swab SARS-CoV-2 RT-PCR testing up to 3 days before the soccer games;(2) Respiratory isolation of all SARS-CoV-2 positive players for at least 10 days, regardless symptoms;(3) All player with clinical suspicion of COVID-19 were immediately quarantined;(4) If a player became SARS-CoV-2 positive after the game, the other players were allowed to play the next game, if they remained asymptomatic and SARS-CoV-2 RT-PCR negative. Understanding how antibody responses to SARS-CoV-2 evolve can provide insights into therapeutic and testing approaches for COVID-19. In the present study we profile the antibody responses of players up to nine months from a SARS-CoV-2 positive RT-PCR test. Methods. Serum samples were obtained from 955 soccer players, and analyzed at the same laboratory in São Paulo city, in the Hospital Israelita Albert Einstein. It was used the cPas Technology, the sVNT kit for detecting and measuring circulating neutralizing antibodies against the SARS-CoV-2 virus. Results. Neutralizing antibody was positive for 416 samples (416/955=44%;C.I. 95%= [40%;47%]). From the 955 soccer players, 454 had RT-PCR+ previously, up to nine months until the neutralizing antibody tests. From this 454 players, 172 (38%) had neutralizing antibody below 20% (C.I. 95% = [34%;42%]), 30 (7%) between 20% and 30% (C.I. 95% = [5%;9%]), and e 252 (56%) above 30% (C.I. 95% =[51%;60%]). Antibody responses to SARS-CoV-2 were significantly higher in individuals RT-PCR+ (Table 1). There was no difference between the neutralizing antibody responses status to SARS-CoV-2 and the time between the RT-PCR+ and the neutralizing antibody test (p-value = 0.423;Figures 1 and 2, Table 2). Conclusion. This study found neutralizing activity of infection against SARSCoV-2 in 63% RT-PCR+ individuals, but only in 26% in RT-PCR(-) players. Level of neutralizing antibody responses maintained stable until up to nine months after a RT-PCR+.
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Background. Bloodstream infection (BSI) - Central and Non-Central Line Associated - and infections of the lower respiratory tract (RESP) - pneumonia and non pneumonia lower respiratory infections - are some of the main causes of unexpected death in Intensive Care Units (ICUs). Although the leading causes of these infections are already known, risk prediction models can be used to identify unexpected cases. This study aims to investigate whether or not it is possible to build multivariate models to predict BSI and RESP events. Methods. Univariate and multivariate analysis using multiple logistic regression models were built to predict BSI and RESP events. ROC curve analysis was used to validate each model. Independent variables: 29 quantitative parameters and 131 categorical variables. BSI and RESP were identified using Brazilian Health Regulatory Agency protocols with data collected between January and November 2020 from a medical-surgical ICU in a Brazilian Hospital. Definitions: if an infection is 5% or less likely to occur according to the model used and it eventually occurs, it will be classified as "unexpected", or else, if an infection is 10% or less likely to occur, it will be classified as "probably unexpected". Otherwise, infections will be classified as "expected". Patients with a 30% or more risk for BSI or RESP will be classified as "high risk". Results. A total of 1,171 patients were accessed: 70 patients with BSI (95% confidence interval [CI], 3.1%-5%), 66 patients with RESP (95% CI, 2.9%-4.7%), 235 deaths (95% CI, 11.8%-14.9%). Of the 160 potential risk factors evaluated, logistic models for BSI and RESP identified respectively five and seven predictors (Tables 1 and 2, and Figure 1). Patients admitted to the ICU with Covid-19 had a three fold BSI risk and five times more RESP risk than patients without this diagnosis. Conclusion. The built models make possible the identification of the expected infections and the unexpected ones. Three main course of actions can be taken using these models and associated data: (1) Before the occurrence of BSI and RESP: to place high risk patients under more rigorous infection surveillance. (2) After the occurrence of BSI or RESP: to investigate "unexpected" infections. (3) At discharge: to identify high risk patients with no infections for further studies.
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Background: In December 2009, a cluster of patients with pneumonia was reported in the city of Wuhan, capital of Hubei province in China, caused by a novel coronavirus: SARS-CoV-2. The epidemiological compartmental susceptible-exposed-infected-recovered (SEIR) model has been previously used during the initial wave of the H1N1 influenza pandemic in 2009. This study investigates whether the SEIR model, associated to mobility changes parameters, can determine the likelihood of establishing control over an epidemic in a city, state or country. Methods: The critical step in the prediction of COVID-19 by a SEIR model are the values of the basic reproduction number (R0) and the infectious period, in days. R0 and the infectious periods were calculated by mathematical constrained optimization, and used to determine the numerically minimum SEIR model errors in a country, based on COVID-19 data until april 11th. The Community Mobility Reports from Google Maps (https://www.google.com/COVID19/mobility/) provided mobility changes on april 5th compared to the baseline (Jan 3th to Feb 6th). The data was used to measure the non-pharmacological intervention adherence. The impact of each mobility component was made by logistic regression models. COVID-19 control was defined by R0 of the SEIR model in a country less than 1.0. Results: Residential mobility restriction presented the higher logistic coefficient (17.7), meaning higher impact on outbreak control. Workplace mobility restriction was the second most effective measure, considering a restriction minimum of 56% for a 53% chance of outbreak control. Retail and recreation mobility presented 53%, and 86% respectively. Transit stations (96% and 54%) were also assessed. Park mobility restriction demonstrated the lowest effectiveness in outbreak control, considering that absolute (100%) restriction provided the lowest chance of outbreak control (46%). Conclusion: Residential mobility restriction is the most effective measure. The degree to which mobility restrictions increase or decrease the overall epidemic size depends on the level of risk in each community and the characteristics of the disease. More research is required in order to estimate the optimal balance between mobility restriction, outbreak control, economy and freedom of movement. (Table Presented).
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Background: Mathematical models can provide insights on the spread of infectious diseases, such as the novel SARS-CoV-2 (COVID-19). This work applied a SEIR epidemiological compartmental model (susceptible-exposed-infected-recovered) with k phases to predict the actual spread of the COVID-19 virus. Methods: Four parameters of the SEIR model were obtained by international experiences: the incubation period = 3.7 in days, the proportion of critical cases = 0.05, the overall case-fatality rate = 0.023, and the asymptomatic proportion of COVID-19 = 0.18. The critical step in the prediction of COVID-19 by the model is the value of R0 (the basic reproduction number) and T-infectious (the infectious period, in days). R0 and T-infectious for each phase of the curve are calculated by mathematical constrained optimization, a numerical method. Differently from a statistical modelling, a numerical method is a type of mathematical modelling that is not dependent on a probability distribution. The objective function that measures the model error is minimized with respect to R0 and T-infectious in the presence of constraints on those variables. For R0, constraints are valid range of values (0.5 ≤ R0 ≤ 20). For T-infectious, constraints also are related to its range of values (2 ≤ T-infectious ≤ 14). A Solver from Excel or NEOS Server, for example, can be used for finding numerically minimum of a function Z, that represents the sum of absolute value of errors between COVID-19 new cases observed in one day, and COVID-19 cases predicted by the SEIR model (Fig. 2 and 3). Results: The ECDC has registered 8,142,129 COVID-19 in the world on Jun/17/2020. R0 and T-infectious calculated for a three phases curve in USA, with a stabilized scenario (Fig. 4: R0-1=1.0;T-infectious-1=2;R0-2=17.4;T-infectious- 2=2;R0-3=1.0;T-infectious-3=14), a two phases curve in Brazil (Fig. 5: R0-1=8.0;T-infectious-1=9;R0-2=1.3;T-infectious-2=6), and a three phases model for France (Fig. 6: R0-1=4.3;T-infectious-1=11;R0-2=9.3;T-infectious-2=11;R0-3=0.5;T-infectious-3=12). Conclusion: The k phases SEIR model proved to be a useful to measure the COVID-19 transmission in a City, State or Country. More phases can be applied to fit a scenario with a new second COVID wave. (Figure Presented).
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Background: Infection by SARS-CoV-2 can lead to dyspnea, edema, deposition of intra alveolar fibrin, thrombosis and hemorrhages. During the COVID-19. outbreak, several questions were raised about the risks for the pediatric population. Pediatric patients appeared to be relatively safe, with only minor symptoms and a quick recovery. However, there have been reports of a relationship between COVID 19 and a Kawasaki-like inflammatory disease in this population. Kawasaki's disease (KD) is a rheumatological vasculitis prevalent in childhood characterized mainly by diffuse inflammation of the arteries associated with skin rash, changes in the mucosa and its main complication is coronary aneurysms. Methods: A systematic literature review was performed in the PubMED database using the keywords “Kawasaki disease”, “COVID-19” and “Pediatrics”. The selected filters were “Case reports”, “Multicenter study”, “Clinical Study”, “Observational study”, “Human” and “English”. A total of 18 articles were seleted. Results: There seems to be a convergence between the literature published so far, pointing to a greater propensity for pediatric patients infected with Sars-Cov-2 to develop KD. The number of patients with KD symptoms seen at a specific center increased from 2 to 17 in 11 days (MOREIRA, 2020). In a sample space of 21 patients diagnosed with KD, 91% had previous contact with SARS-CoV-2 (TOUBIANA, 2020) whereas other studies point to a 30-fold increase in the prevalence of KD since the beginning of 2020 (VERDONI, 2020). There is already an established relationship between DK and HCoV-NH, describing that 4.5% of patients with this infection develop KD. Therefore, it was suggested that infection with another Coronavirus strain could have a similar relationship. Conclusion: Despite the relationship described between pediatric patients infected with COVID-19 being more likely to develop KD, further studies are needed to prove a statistical relationship between both condition.