Yellow fever and orthotopic liver transplantation: new insights from the autopsy room for an old but re-emerging disease.

AIMS: The clinical spectrum of yellow fever (YF) ranges from asymptomatic to fulminant hepatitis. During the sylvatic YF epidemic in the metropolitan area of São Paulo, Brazil in 2018, seven orthotopic liver transplantations (OLTs) were performed in our institution to treat fulminant YF hepatitis. Three patients recovered, while four patients died following OLT. The autopsy findings of all these cases are presented herein as the first description of YF in transplanted patients. METHODS AND RESULTS: All patients were men, aged 16-40 years, without vaccination to YF virus (YFV). All organs were examined, with tissue sampling for histopathological analysis. Detection of YF virus antigens (YFV Ag) was performed with two primary antibodies (mouse polyclonal anti-YFV antibody directed to wild strain and a goat anti-YF virus antibody), and RT-PCR assays were utilised to detect YFV-RNA. All the cases depicted typical findings of YF hepatitis in the engrafted liver. The main extrahepatic findings were cerebral oedema, pulmonary haemorrhage, pneumonia, acute tubular necrosis and ischaemic/reperfusion pancreatitis. Of the four cases, the YVF Ag was detected in the heart in one case, liver and testis in three cases, and the kidney and spleen in all four cases. All four cases had YF virus RNA detected by RT-PCR in the liver and in other organs. CONCLUSIONS: Infection of the engrafted liver and other organs by YFV, possibly combined with major ischaemic systemic lesions, may have led to the death of four of the seven patients undergoing OLT.

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential.

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

Early detection of respiratory disease outbreaks through primary healthcare data.

Background: The emergence of coronavirus disease 2019 (COVID-19) in 2020 highlighted the relevance of surveillance systems in detecting early signs of potential outbreaks, thus enabling public health authorities to act before the pathogen becomes widespread. Syndromic digital surveillance through web applications has played a crucial role in monitoring the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. However, this approach requires expensive infrastructure, which is not available in developing countries. Pre-existing sources of information, such as encounters in primary health care (PHC), can provide valuable data for a syndromic surveillance system. Here we evaluated the utility of PHC data to identify early warning signals of the first COVID-19 outbreak in Bahia-Brazil in 2020. Methods: We compared the weekly counts of PHC encounters due to respiratory complaints and the number of COVID-19 cases in 2020 in Bahia State - Brazil. We used the data from December 2016 to December 2019 to predict the expected number of encounters in 2020. We analysed data aggregated by geographic regions (n = 34) and included those where historical PHC data was available for at least 70% of the population. Results: Twenty-one out of 34 regions met the inclusion criteria. We observed that notification of COVID-19 cases was preceded by at least two weeks with an excess of encounters of respiratory complaints in 18/21 (86%) of the regions analysed and four weeks or more in 10/21 (48%) regions. Conclusions: Digital syndromic surveillance systems based on already established PHC databases may add time to preparedness and response to emerging epidemics.

Discrepancies Between Clinical and Autopsy Diagnoses in Rapid Response Team-Assisted Patients: What Are We Missing?

OBJECTIVES: The rapid response team (RRT) assists hospitalized patients with sudden clinical deterioration. There is scarce evidence of diagnostic accuracy in this scenario, but it is possible that a considerable rate of misdiagnosis exists. Autopsy remains a valuable tool for assessing such question. This study aimed to compare clinical (premortem) and autopsy (postmortem) diagnoses in patients assisted by the RRT and describe major discrepancies. METHODS: We reviewed 104 clinical data and autopsies from patients assisted by the RRT during a cardiac arrest event in a tertiary care hospital in Brazil. Clinical and autopsy diagnostic discrepancies were classified using the Goldman criteria. Other clinical and pathological data were described, and the group with major diagnostic discrepancies was further analyzed. RESULTS: We found 39 (37.5%) patients with major diagnostic discrepancies. Most frequent immediate causes of death in this group determined by autopsy were sepsis (36%), pulmonary embolism (23%) and hemorrhagic shock (21%). Pulmonary embolism was the cause of death significantly more frequent in the major discrepancy group than in the minor discrepancy group (23% versus 3%, P = 0.002). We individually described all major diagnostic discrepancies. CONCLUSIONS: We found a high rate (37.5%) of major misdiagnosis in autopsies from patients assisted by the RRT in a tertiary teaching hospital. Pulmonary embolism was the most inaccurate fatal diagnosis detected by autopsy.

Mortality over time among COVID-19 patients hospitalized during the first surge of the pandemic: A large cohort study.

BACKGROUND: Capacity strain negatively impacts patient outcome, and the effects of patient surge are a continuous threat during the COVID-19 pandemic. Evaluating changes in mortality over time enables evidence-based resource planning, thus improving patient outcome. Our aim was to describe baseline risk factors associated with mortality among COVID-19 hospitalized patients and to compare mortality rates over time. METHODS: We conducted a retrospective cohort study in the largest referral hospital for COVID-19 patients in Sao Paulo, Brazil. We investigated risk factors associated with mortality during hospitalization. Independent variables included age group, sex, the Charlson Comorbidity Index, admission period according to the stage of the first wave of the epidemic (early, peak, and late), and intubation. RESULTS: We included 2949 consecutive COVID-19 patients. 1895 of them were admitted to the ICU, and 1473 required mechanical ventilation. Median length of stay in the ICU was 10 (IQR 5-17) days. Overall mortality rate was 35%, and the adjusted odds ratios for mortality increased with age, male sex, higher Charlson Comorbidity index, need for mechanical ventilation, and being admitted to the hospital during the wave peak of the epidemic. Being admitted to the hospital during the wave peak was associated with a 33% higher risk of mortality. CONCLUSIONS: In-hospital mortality was independently affected by the epidemic period. The recognition of modifiable operational variables associated with patient outcome highlights the importance of a preparedness plan and institutional protocols that include evidence-based practices and allocation of resources.

Distinct Outcomes in COVID-19 Patients with Positive or Negative RT-PCR Test.

Identification of the SARS-CoV-2 virus by RT-PCR from a nasopharyngeal swab sample is a common test for diagnosing COVID-19. However, some patients present clinical, laboratorial, and radiological evidence of COVID-19 infection with negative RT-PCR result(s). Thus, we assessed whether positive results were associated with intubation and mortality. This study was conducted in a Brazilian tertiary hospital from March to August of 2020. All patients had clinical, laboratory, and radiological diagnosis of COVID-19. They were divided into two groups: positive (+) RT-PCR group, with 2292 participants, and negative (-) RT-PCR group, with 706 participants. Patients with negative RT-PCR testing and an alternative most probable diagnosis were excluded from the study. The RT-PCR(+) group presented increased risk of intensive care unit (ICU) admission, mechanical ventilation, length of hospital stay, and 28-day mortality, when compared to the RT-PCR(-) group. A positive SARS-CoV-2 RT-PCR result was independently associated with intubation and 28 day in-hospital mortality. Accordingly, we concluded that patients with a COVID-19 diagnosis based on clinical data, despite a negative RT-PCR test from nasopharyngeal samples, presented more favorable outcomes than patients with positive RT-PCR test(s).

Timing to Intubation COVID-19 Patients: Can We Put It Off until Tomorrow?

BACKGROUND: The decision to intubate COVID-19 patients receiving non-invasive respiratory support is challenging, requiring a fine balance between early intubation and risks of invasive mechanical ventilation versus the adverse effects of delaying intubation. This present study analyzes the association between intubation day and mortality in COVID-19 patients. METHODS: We performed a unicentric retrospective cohort study considering all COVID-19 patients consecutively admitted between March 2020 and August 2020 requiring invasive mechanical ventilation. The primary outcome was all-cause mortality within 28 days after intubation, and a Cox model was used to evaluate the effect of time from onset of symptoms to intubation in mortality. RESULTS: A total of 592 (20%) patients of 3020 admitted with COVID-19 were intubated during study period, and 310 patients who were intubated deceased 28 days after intubation. Each additional day between the onset of symptoms and intubation was significantly associated with higher in-hospital death (adjusted hazard ratio, 1.018; 95% CI, 1.005-1.03). CONCLUSION: Among patients infected with SARS-CoV-2 who were intubated and mechanically ventilated, delaying intubation in the course of symptoms may be associated with higher mortality. TRIAL REGISTRATION: The study protocol was approved by the local Ethics Committee (opinion number 3.990.817; CAAE: 30417520.0.0000.0068).

Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil.

INTRODUCTION: Little evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. METHODS: We conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities. RESULTS: Throughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45). CONCLUSIONS: Low-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.

Use and misuse of biomarkers and the role of D-dimer and C-reactive protein in the management of COVID-19: A post-hoc analysis of a prospective cohort study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is associated with high mortality among hospitalized patients and incurs high costs. Severe acute respiratory syndrome coronavirus 2 infection can trigger both inflammatory and thrombotic processes, and these complications can lead to a poorer prognosis. This study aimed to evaluate the association and temporal trends of D-dimer and C-reactive protein (CRP) levels with the incidence of venous thromboembolism (VTE), hospital mortality, and costs among inpatients with COVID-19. METHODS: Data were extracted from electronic patient records and laboratory databases. Crude and adjusted associations for age, sex, number of comorbidities, Sequential Organ Failure Assessment score at admission, and D-dimer or CRP logistic regression models were used to evaluate associations. RESULTS: Between March and June 2020, COVID-19 was documented in 3,254 inpatients. The D-dimer level ≥4,000 ng/mL fibrinogen equivalent unit (FEU) mortality odds ratio (OR) was 4.48 (adjusted OR: 1.97). The CRP level ≥220 mg/dL OR for death was 7.73 (adjusted OR: 3.93). The D-dimer level ≥4,000 ng/mL FEU VTE OR was 3.96 (adjusted OR: 3.26). The CRP level ≥220 mg/dL OR for VTE was 2.71 (adjusted OR: 1.92). All these analyses were statistically significant (p<0.001). Stratified hospital costs demonstrated a dose-response pattern. Adjusted D-dimer and CRP levels were associated with higher mortality and doubled hospital costs. In the first week, elevated D-dimer levels predicted VTE occurrence and systemic inflammatory harm, while CRP was a hospital mortality predictor. CONCLUSION: D-dimer and CRP levels were associated with higher hospital mortality and a higher incidence of VTE. D-dimer was more strongly associated with VTE, although its discriminative ability was poor, while CRP was a stronger predictor of hospital mortality. Their use outside the usual indications should not be modified and should be discouraged.

Confronting the Multidimensional Challenges of Research in the Context of Emerging Infectious Diseases in Brazil: The Example of Yellow Fever.

In the most recent Brazilian yellow fever (YF) outbreak, a group of clinicians and researchers initiated in mid-January 2018 a considerable effort to develop a multicenter randomized controlled clinical trial to evaluate the effect of sofosbuvir on YF viremia and clinical outcomes (Brazilian Clinical Trials Registry: RBR-93dp9n). The approval of this protocol had urgency given the seasonal/short-lived pattern of YF transmission, large number of human cases, and epidemic transmission at the outskirts of a large urban center. However, many intricacies in the research regulatory and ethical submission systems in Brazil were indomitable even under such pressing conditions. By April 2018, we had enrolled 29 patients for a target sample size of 90 participants. Had enrollment been initiated 3 weeks earlier, an additional 31 patients could have been enrolled, reaching the prespecified sample size for the interim analysis. This recent experience highlights the urgent need to improve local preparedness for research in the setting of explosive outbreaks, as has been seen in the last few years in different countries.

Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil.

The first case of COVID-19 was detected in Brazil on 25 February 2020. We report and contextualize epidemiological, demographic and clinical findings for COVID-19 cases during the first 3 months of the epidemic. By 31 May 2020, 514,200 COVID-19 cases, including 29,314 deaths, had been reported in 75.3% (4,196 of 5,570) of municipalities across all five administrative regions of Brazil. The R0 value for Brazil was estimated at 3.1 (95% Bayesian credible interval = 2.4-5.5), with a higher median but overlapping credible intervals compared with some other seriously affected countries. A positive association between higher per-capita income and COVID-19 diagnosis was identified. Furthermore, the severe acute respiratory infection cases with unknown aetiology were associated with lower per-capita income. Co-circulation of six respiratory viruses was detected but at very low levels. These findings provide a comprehensive description of the ongoing COVID-19 epidemic in Brazil and may help to guide subsequent measures to control virus transmission.

Quantifying the impact of air pollution on the urban population of Brazil.

This study aimed to quantify air pollution impact on morbidity and mortality in the Brazilian urban population using locally generated impact factors. Concentration-response coefficients were used to estimate the number of hospitalizations and deaths attributable to air pollution in seven Brazilian cities. Poisson regression coefficients (beta) were obtained from time-series studies conducted in Brazil. The study included individuals 65 years old and over and children under five. More than 600 deaths a year from respiratory causes in the elderly and 47 in children were attributable to mean air pollution levels, corresponding to 4.9% and 5.5% of all deaths from respiratory causes in these age groups. More than 4,000 hospital admissions for respiratory conditions were also attributable to air pollution. These results quantitatively demonstrate the currently observed contribution of air pollution to mortality and hospitalizations in Brazilian cities. Such assessment is thought to help support the planning of surveillance and control activities for air pollution in these and similar areas.

[Respiratory and cardiovascular hospitalizations associated with air pollution in the city of São Paulo, Brazil]. / Hospitalizações por causas respiratórias e cardiovasculares associadas à contaminação atmosférica no Município de São Paulo, Brasil.

The recognition that current air pollution levels cause harmful health effects makes the definition of the air quality regulatory process imperative. This study examines the association between exposure to air pollution and hospital admissions in the city of São Paulo, aiming to support the development of measures to reduce such health risks. The authors conducted an ecological time series study of hospital admissions for respiratory and cardiovascular diseases in children and the elderly in relation to daily air pollution levels, using generalized additive Poisson regression models. All air pollutants except ozone showed a statistically significant association with admissions for respiratory and cardiovascular diseases. An increase of 10 microg/m3 in fine particulate matter was associated with a 4.6% increase in asthma admissions in children and a 4.3% increase in admissions for chronic obstructive pulmonary disease and 1.5% for ischemic heart disease in the elderly. These associations are consistent with a large body of literature in this area and indicate that the current air pollution levels in São Paulo have an important negative impact on the population's health.

Use and misuse of biomarkers and the role of D-dimer and C-reactive protein in the management of COVID-19: A post-hoc analysis of a prospective cohort study

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is associated with high mortality among hospitalized patients and incurs high costs. Severe acute respiratory syndrome coronavirus 2 infection can trigger both inflammatory and thrombotic processes, and these complications can lead to a poorer prognosis. This study aimed to evaluate the association and temporal trends of D-dimer and C-reactive protein (CRP) levels with the incidence of venous thromboembolism (VTE), hospital mortality, and costs among inpatients with COVID-19. METHODS: Data were extracted from electronic patient records and laboratory databases. Crude and adjusted associations for age, sex, number of comorbidities, Sequential Organ Failure Assessment score at admission, and D-dimer or CRP logistic regression models were used to evaluate associations. RESULTS: Between March and June 2020, COVID-19 was documented in 3,254 inpatients. The D-dimer level ≥4,000 ng/mL fibrinogen equivalent unit (FEU) mortality odds ratio (OR) was 4.48 (adjusted OR: 1.97). The CRP level ≥220 mg/dL OR for death was 7.73 (adjusted OR: 3.93). The D-dimer level ≥4,000 ng/mL FEU VTE OR was 3.96 (adjusted OR: 3.26). The CRP level ≥220 mg/dL OR for VTE was 2.71 (adjusted OR: 1.92). All these analyses were statistically significant (p<0.001). Stratified hospital costs demonstrated a dose-response pattern. Adjusted D-dimer and CRP levels were associated with higher mortality and doubled hospital costs. In the first week, elevated D-dimer levels predicted VTE occurrence and systemic inflammatory harm, while CRP was a hospital mortality predictor. CONCLUSION: D-dimer and CRP levels were associated with higher hospital mortality and a higher incidence of VTE. D-dimer was more strongly associated with VTE, although its discriminative ability was poor, while CRP was a stronger predictor of hospital mortality. Their use outside the usual indications should not be modified and should be discouraged.

Forecasting daily emergency department visits using calendar variables and ambient temperature readings.

OBJECTIVES: This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy. METHODS: The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable. RESULTS: The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance). CONCLUSIONS: This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources.

Adult mortality from leukemia, brain cancer, amyotrophic lateral sclerosis and magnetic fields from power lines: a case-control study in Brazil.

Recent publications renewed interest in assessing potential health risks for subjects living close to transmission lines. This study aimed at evaluating the association of both distance of home address to the nearest overhead transmission line and of the calculated magnetic fields from the power lines and mortality from leukemia, brain cancer, and amyotrophic lateral sclerosis. We carried out a death certificate based case-control study accessing adult mortality in the Metropolitan Region of São Paulo, in Brazil. Analysis included 1,857 cases of leukemia, 2,357 of brain cancer, 367 of amyotrophic lateral sclerosis, and 4,706 as controls. An increased risk for mortality from leukemia among adults living at closer distances to transmission lines compared to those living further then 400 m was found. Risk was higher for subjects that lived within 50 m from power lines (OR=1.47; 95% CI=0.99-2.18). Similarly, a small increase in leukemia mortality was observed among adults living in houses with higher calculated magnetic fields (OR=1.61; 95% CI=0.91-2.86 for those exposed to magnetic fields >0.3 µT). No increase was seen for brain tumours or amyotrophic lateral sclerosis. Our findings are suggestive of a higher risk for leukemia among subjects living closer to transmission lines, and for those living at homes with higher calculated magnetic fields, although the risk was limited to lower voltage lines.