Patients hospitalized with active tuberculosis and Covid-19 coinfection: A matched case-control from the Brazilian Covid-19 Registry.

Although control of Covid-19 has improved, the virus continues to cause infections, such as tuberculosis, that is still endemic in many countries, representing a scenario of coinfection. To compare Covid-19 clinical manifestations and outcomes between patients with active tuberculosis infection and matched controls. This is a matched case-control study based on data from the Brazilian Covid-19 Registry, in hospitalized patients aged 18 or over with laboratory confirmed Covid-19 from March 1, 2020, to March 31, 2022. Cases were patients with tuberculosis and controls were Covid-19 patients without tuberculosis. From 13,636 Covid-19, 36 also had active tuberculosis (0.0026%). Pulmonary fibrosis (5.6% vs 0.0%), illicit drug abuse (30.6% vs 3.0%), alcoholism (33.3% vs 11.9%) and smoking (50.0% vs 9.7%) were more common among patients with tuberculosis. They also had a higher frequency of nausea and vomiting (25.0% vs 10.4%). There were no significant differences in in-hospital mortality, mechanical ventilation, need for dialysis and ICU stay. Patients with TB infection presented a higher frequency of pulmonary fibrosis, abuse of illicit drugs, alcoholism, current smoking, symptoms of nausea and vomiting. The outcomes were similar between them.

Clinical characteristics and outcomes in COVID-19 in kidney transplant recipients: a propensity score matched cohort study.

Patients with chronic kidney disease (CKD), especially those on dialysis or who have received a kidney transplant (KT), are considered more vulnerable to severe COVID-19. This susceptibility is attributed to advanced age, a higher frequency of comorbidities, and the chronic immunosuppressed state, which may exacerbate their susceptibility to severe outcomes. Therefore, our study aimed to compare the clinical characteristics and outcomes of COVID-19 in KT patients with those on chronic dialysis and non-CKD patients in a propensity score-matched cohort study. This multicentric retrospective cohort included adult COVID-19 laboratory-confirmed patients admitted from March/2020 to July/2022, from 43 Brazilian hospitals. The primary outcome was in-hospital mortality. Propensity score analysis matched KT recipients with controls - patients on chronic dialysis and those without CKD (within 0.25 standard deviations of the logit of the propensity score) - according to age, sex, number of comorbidities, and admission year. This study included 555 patients: 163 KT, 146 on chronic dialysis, and 249 non-CKD patients (median age 57 years, 55.2% women). With regards to clinical outcomes, chronic dialysis patients had a higher prevalence of acute heart failure, compared to KT recipients, furthermore, both groups presented high in-hospital mortality, 34.0 and 28.1%, for KT and chronic dialysis patients, respectively. When comparing KT and non-CKD patients, the first group had a higher incidence of in-hospital dialysis (26.4% vs. 8.8%, p < 0.001), septic shock (24.1% vs. 12.0%, p = 0.002), and mortality (32.5% vs. 23.3%, p = 0.039), in addition to longer time spent in the intensive care unit (ICU). In this study, chronic dialysis patients presented a higher prevalence of acute heart failure, compared to KT recipients, whereas KT patients had a higher frequency of complications than those without CKD, including septic shock, dialysis during hospitalization, and in-hospital mortality as well as longer time spent in the ICU.

Assessment of the ABC2-SPH risk score to predict invasive mechanical ventilation in COVID-19 patients and comparison to other scores.

Background: Predicting the need for invasive mechanical ventilation (IMV) is important for the allocation of human and technological resources, improvement of surveillance, and use of effective therapeutic measures. This study aimed (i) to assess whether the ABC2-SPH score is able to predict the receipt of IMV in COVID-19 patients; (ii) to compare its performance with other existing scores; (iii) to perform score recalibration, and to assess whether recalibration improved prediction. Methods: Retrospective observational cohort, which included adult laboratory-confirmed COVID-19 patients admitted in 32 hospitals, from 14 Brazilian cities. This study was conducted in two stages: (i) for the assessment of the ABC2-SPH score and comparison with other available scores, patients hospitalized from July 31, 2020, to March 31, 2022, were included; (ii) for ABC2-SPH score recalibration and also comparison with other existing scores, patients admitted from January 1, 2021, to March 31, 2022, were enrolled. For both steps, the area under the receiving operator characteristic score (AUROC) was calculated for all scores, while a calibration plot was assessed only for the ABC2-SPH score. Comparisons between ABC2-SPH and the other scores followed the Delong Test recommendations. Logistic recalibration methods were used to improve results and adapt to the studied sample. Results: Overall, 9,350 patients were included in the study, the median age was 58.5 (IQR 47.0-69.0) years old, and 45.4% were women. Of those, 33.5% were admitted to the ICU, 25.2% received IMV, and 17.8% died. The ABC2-SPH score showed a significantly greater discriminatory capacity, than the CURB-65, STSS, and SUM scores, with potentialized results when we consider only patients younger than 80 years old (AUROC 0.714 [95% CI 0.698-0.731]). Thus, after the ABC2-SPH score recalibration, we observed improvements in calibration (slope = 1.135, intercept = 0.242) and overall performance (Brier score = 0.127). Conclusion: The ABC2-SPHr risk score demonstrated a good performance to predict the need for mechanical ventilation in COVID-19 hospitalized patients under 80 years of age.

Radon exposure: a major cause of lung cancer in nonsmokers.

Exposure to radon can impact human health. This is a nonsystematic review of articles written in English, Spanish, French, or Portuguese published in the last decade (2013-2023), using databases such as PubMed, Google Scholar, EMBASE, and SciELO. Search terms selected were radon, human health, respiratory diseases, children, and adults. After analyzing the titles and abstracts, the researchers initially identified 47 studies, which were subsequently reduced to 40 after excluding reviews, dissertations, theses, and case-control studies. The studies have shown that enclosed environments such as residences and workplaces have higher levels of radon than those outdoors. Moreover, radon is one of the leading causes of lung cancer, especially in nonsmokers. An association between exposure to radon and development of other lung diseases, such as asthma and COPD, was also observed. It is crucial to increase public awareness and implement governmental control measures to reduce radon exposure. It is essential to quantify radon levels in all types of buildings and train professionals to conduct such measurements according to proven efficacy standards. Health care professionals should also be informed about this threat and receive adequate training to deal with the effects of radon on human health.

Artificial intelligence to predict the need for mechanical ventilation in cases of severe COVID-19.

Objective: To determinate the accuracy of computed tomography (CT) imaging assessed by deep neural networks for predicting the need for mechanical ventilation (MV) in patients hospitalized with severe acute respiratory syndrome due to coronavirus disease 2019 (COVID-19). Materials and Methods: This was a retrospective cohort study carried out at two hospitals in Brazil. We included CT scans from patients who were hospitalized due to severe acute respiratory syndrome and had COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR). The training set consisted of chest CT examinations from 823 patients with COVID-19, of whom 93 required MV during hospitalization. We developed an artificial intelligence (AI) model based on convolutional neural networks. The performance of the AI model was evaluated by calculating its accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Results: For predicting the need for MV, the AI model had a sensitivity of 0.417 and a specificity of 0.860. The corresponding area under the ROC curve for the test set was 0.68. Conclusion: The high specificity of our AI model makes it able to reliably predict which patients will and will not need invasive ventilation. That makes this approach ideal for identifying high-risk patients and predicting the minimum number of ventilators and critical care beds that will be required.

Objetivo: Determinar a acurácia da tomografia computadorizada (TC), avaliada por redes neurais profundas, na ventilação mecânica, de pacientes hospitalizados por síndrome respiratória aguda grave por COVID-19. Materiais e Métodos: Trata-se de estudo de coorte retrospectivo, realizado em dois hospitais brasileiros. Foram incluídas TCs de pacientes hospitalizados por síndrome respiratória aguda grave e COVID-19 confirmada por RT-PCR. O treinamento consistiu em TC de tórax de 823 pacientes com COVID-19, dos quais 93 foram submetidos a ventilação mecânica na hospitalização. Nós desenvolvemos um modelo de inteligência artificial baseado em redes de convoluções neurais. A avaliação do desempenho do uso da inteligência artificial foi baseada no cálculo de acurácia, sensibilidade, especificidade e área sob a curva ROC. Resultados: A sensibilidade do modelo foi de 0,417 e a especificidade foi de 0,860. A área sob a curva ROC para o conjunto de teste foi de 0,68. Conclusão: Criamos um modelo de aprendizado de máquina com elevada especificidade, capaz de prever de forma confiável pacientes que não precisarão de ventilação mecânica. Isso significa que essa abordagem é ideal para prever com antecedência pacientes de alto risco e um número mínimo de equipamentos de ventilação e de leitos críticos.

Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset.

The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48-71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

Artificial intelligence to predict the need for mechanical ventilation in cases of severe COVID-19 / Inteligência artificial na predição de necessidade de ventilação mecânica em casos graves de COVID-19

Abstract Objective: To determinate the accuracy of computed tomography (CT) imaging assessed by deep neural networks for predicting the need for mechanical ventilation (MV) in patients hospitalized with severe acute respiratory syndrome due to coronavirus disease 2019 (COVID-19). Materials and Methods: This was a retrospective cohort study carried out at two hospitals in Brazil. We included CT scans from patients who were hospitalized due to severe acute respiratory syndrome and had COVID-19 confirmed by reverse transcriptionpolymerase chain reaction (RT-PCR). The training set consisted of chest CT examinations from 823 patients with COVID-19, of whom 93 required MV during hospitalization. We developed an artificial intelligence (AI) model based on convolutional neural networks. The performance of the AI model was evaluated by calculating its accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Results: For predicting the need for MV, the AI model had a sensitivity of 0.417 and a specificity of 0.860. The corresponding area under the ROC curve for the test set was 0.68. Conclusion: The high specificity of our AI model makes it able to reliably predict which patients will and will not need invasive ventilation. That makes this approach ideal for identifying high-risk patients and predicting the minimum number of ventilators and critical care beds that will be required.

Resumo Objetivo: Determinar a acurácia da tomografia computadorizada (TC), avaliada por redes neurais profundas, na ventilação mecânica, de pacientes hospitalizados por síndrome respiratória aguda grave por COVID-19. Materiais e Métodos: Trata-se de estudo de coorte retrospectivo, realizado em dois hospitais brasileiros. Foram incluídas TCs de pacientes hospitalizados por síndrome respiratória aguda grave e COVID-19 confirmada por RT-PCR. O treinamento consistiu em TC de tórax de 823 pacientes com COVID-19, dos quais 93 foram submetidos a ventilação mecânica na hospitalização. Nós desenvolvemos um modelo de inteligência artificial baseado em redes de convoluções neurais. A avaliação do desempenho do uso da inteligência artificial foi baseada no cálculo de acurácia, sensibilidade, especificidade e área sob a curva ROC. Resultados: A sensibilidade do modelo foi de 0,417 e a especificidade foi de 0,860. A área sob a curva ROC para o conjunto de teste foi de 0,68. Conclusão: Criamos um modelo de aprendizado de máquina com elevada especificidade, capaz de prever de forma confiável pacientes que não precisarão de ventilação mecânica. Isso significa que essa abordagem é ideal para prever com antecedência pacientes de alto risco e um número mínimo de equipamentos de ventilação e de leitos críticos.

Cannabis and sleep disorders: not ready for prime time? A qualitative scoping review.

STUDY OBJECTIVES: To perform a qualitative scoping literature review for studies involving the effects of cannabis on sleep and sleep disorders. METHODS: Two electronic databases, MEDLINE and EMBASE, searched for comprehensive published abstracted studies that involved human participants. Inclusion criteria were article of any type, published in English, a target population of cannabis users, and reported data on cannabis effect on sleep and sleep disorders. The Joanna Briggs Institute's (JBI) approach was elected as the methodology framework guidance in the scoping review process. RESULTS: A total of 40 unique publications were found. The majority (82.5%) were from the Americas with 60% published in the last decade. Of the 40 studies, only 25% were randomized control trials, and the sleep outcome measurements were similar and comparable in only 20%. Cannabis users studied were reported either 73% frequent users or 27% sporadic users. The utilization of cannabis showed improved sleep (21%), worse sleep (48%), mixed results (14%), or no impact at all (17%) in the studies published in the last 5 decades. CONCLUSIONS: Our findings summarize the lack of robust evidence to support the use of cannabis for sleep disorders. The varied cannabis user-related characteristics may account for the inconsistent results identified. Further studies assessing cannabis and sleep are needed to discern what works in what context, how it works, and for whom. CITATION: Amaral C, Carvalho C, Scaranelo A, Chapman K, Chatkin J, Ferreira I. Cannabis and sleep disorders: not ready for prime time? A qualitative scoping review. J Clin Sleep Med. 2023;19(5):975-990.

Radon exposure: a major cause of lung cancer in nonsmokers / Exposição ao radônio: uma importante causa de câncer de pulmão em não fumantes

ABSTRACT Exposure to radon can impact human health. This is a nonsystematic review of articles written in English, Spanish, French, or Portuguese published in the last decade (2013-2023), using databases such as PubMed, Google Scholar, EMBASE, and SciELO. Search terms selected were radon, human health, respiratory diseases, children, and adults. After analyzing the titles and abstracts, the researchers initially identified 47 studies, which were subsequently reduced to 40 after excluding reviews, dissertations, theses, and case-control studies. The studies have shown that enclosed environments such as residences and workplaces have higher levels of radon than those outdoors. Moreover, radon is one of the leading causes of lung cancer, especially in nonsmokers. An association between exposure to radon and development of other lung diseases, such as asthma and COPD, was also observed. It is crucial to increase public awareness and implement governmental control measures to reduce radon exposure. It is essential to quantify radon levels in all types of buildings and train professionals to conduct such measurements according to proven efficacy standards. Health care professionals should also be informed about this threat and receive adequate training to deal with the effects of radon on human health.

RESUMO A exposição ao radônio pode ter impacto na saúde humana. Esta é uma revisão não sistemática de artigos escritos em inglês, espanhol, francês ou português publicados na última década (2013-2023), utilizando bancos de dados como PubMed, Google Acadêmico, EMBASE e SciELO. Os termos de busca selecionados foram radônio, saúde humana, doenças respiratórias, crianças e adultos. Após a análise dos títulos e resumos, os pesquisadores inicialmente identificaram 47 estudos, que foram posteriormente reduzidos para 40 após a exclusão de revisões, dissertações, teses e estudos de caso-controle. Os estudos mostraram que ambientes fechados como residências e locais de trabalho apresentam maiores níveis de radônio do que ambientes externos. Além disso, o radônio é uma das principais causas de câncer de pulmão, especialmente em não fumates. Também foi observada associação entre exposição ao radônio e desenvolvimento de outras doenças pulmonares, como asma e DPOC. É crucial aumentar a conscientização do público e implementar medidas governamentais de controle para reduzir a exposição ao radônio. É fundamental quantificar os níveis de radônio em todos os tipos de edifícios e treinar profissionais para realizar essas medições segundo padrões de eficácia comprovada. Os profissionais de saúde também devem ser informados sobre essa ameaça e receber formação adequada para lidar com os efeitos do radônio na saúde humana.

Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry.

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (ß = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (ß = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (ß = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (ß = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.

Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients.

BACKGROUND: Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. METHODS: This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS: The median age of the model-derivation cohort was 59 (IQR 47-70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918-0.939) and validation (temporal AUROC 0.927, 95% CI 0.911-0.941; geographic AUROC 0.819, 95% CI 0.792-0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). CONCLUSIONS: The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.

The Normal Lung Index From Quantitative Computed Tomography for the Evaluation of Obstructive and Restrictive Lung Disease.

PURPOSE: Our objective was to evaluate whether the normal lung index (NLI) from quantitative computed tomography (QCT) analysis can be used to predict mortality as well as pulmonary function tests (PFTs) in patients with chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD). MATERIALS AND METHODS: Normal subjects (n=20) and patients with COPD (n=172) and ILD (n=114) who underwent PFTs and chest CT were enrolled retrospectively in this study. QCT measures included the NLI, defined as the ratio of the lung with attenuation between -950 and -700 Hounsfield units (HU) over the total lung volume (-1024 to -250 HU, mL), high-attenuation area (-700 to -250 HU, %), emphysema index (>6% of pixels < -950 HU), skewness, kurtosis, and mean lung attenuation. Coefficients of correlation between QCT measurements and PFT results in all subjects were calculated. Univariate and multivariate survival analyses were performed to assess mortality prediction by disease. RESULTS: The Pearson correlation analysis showed that the NLI correlated moderately with the forced expiratory volume in 1 second in subjects with COPD (r=0.490, P<0.001) and the forced vital capacity in subjects with ILD (r=0.452, P<0.001). Multivariate analysis revealed that the NLI of <70% was a significant independent predictor of mortality in subjects with COPD (hazard ratio=3.14, P=0.034) and ILD (hazard ratio=2.72, P=0.005). CONCLUSION: QCT analysis, specifically the NLI, can also be used to predict mortality in individuals with COPD and ILD.

Quantitative Computed Tomography: What Clinical Questions Can it Answer in Chronic Lung Disease?

Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customization of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.

Hypothyroidism does not lead to worse prognosis in COVID-19: findings from the Brazilian COVID-19 registry.

BACKGROUND: It is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19. METHODS: The study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis from 37 hospitals. Matching for age, sex, number of comorbidities, and hospital was performed for the paired analysis. RESULTS: Of 7,762 patients with COVID-19, 526 had previously diagnosed hypothyroidism and 526 were matched controls. The median age was 70 years, and 68.3% were females. The prevalence of comorbidities was similar, except for coronary and chronic kidney diseases that were higher in the hypothyroidism group (p=0.015 and p=0.001). D-dimer levels were lower in patients with hypothyroid (p=0.037). In-hospital management was similar, but hospital length-of-stay (p=0.029) and mechanical ventilation requirement (p=0.006) were lower for patients with hypothyroidism. There was a trend of lower in-hospital mortality in patients with hypothyroidism (22.1% vs 27.0%; p=0.062). CONCLUSION: Patients with hypothyroidism had a lower requirement of mechanical ventilation and showed a trend of lower in-hospital mortality. Therefore, hypothyroidism does not seem to be associated with a worse prognosis.

External Environmental Pollution as a Risk Factor for Asthma.

Air pollution is a worrisome risk factor for global morbidity and mortality and plays a special role in many respiratory conditions. It contributes to around 8 million deaths/year, with outdoor exposure being responsible for more than 4.2 million deaths throughout the world, while more than 3.8 million die from situations related to indoor pollution. Pollutant agents induce several respiratory symptoms. In addition, there is a clear interference in numerous asthma outcomes, such as incidence, prevalence, hospital admission, visits to emergency departments, mortality, and asthma attacks, among others. The particulate matter group of pollutants includes coarse particles/PM10, fine particles/PM2.5, and ultrafine particles/PM0.1. The gaseous components include ground-level ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide. The timing, load, and route of allergen exposure are other items affecting allergic disease phenotypes. The complex interaction between pollutant exposures and human host factors has an implication in the development and rise of asthma as a public health problem. However, there are hiatuses in the understanding of the pathways in this disease. The routes through which pollutants induce asthma are multiple, and include the epigenetic changes that occur in the respiratory tract microbiome, oxidative stress, and immune dysregulation. In addition, the expansion of the modern Westernized lifestyle, which is characterized by intense urbanization and more time spent indoors, resulted in greater exposure to polluted air. Another point to consider is the different role of the environment according to age groups. Children growing up in economically disadvantaged neighborhoods suffer more important negative health impacts. This narrative review highlights the principal polluting agents, their sources of emission, epidemiological findings, and mechanistic evidence that links environmental exposures to asthma.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

OBJECTIVES: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed 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 (25-75th 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, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) 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: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.

The Economic Impact of COVID-19 Treatment at a Hospital-level: Investment and Financial Registers of Brazilian Hospitals.

Background: The economic impact associated with the treatment strategies of coronavirus disease-2019 (COVID-19) patients by hospitals and health-care systems in Brazil is unknown and difficult to estimate. This research describes the investments made to absorb the demand for treatment and the changes in occupation rates and billing in Brazilian hospitals. Methods: This research covers the initial findings of "COVID-19 hospital costs and the proposition of a bundled reimbursement strategy for the health-care system," which includes 10 hospitals. The chief financial officer, the chief medical officer, and hospital executives of each participating hospital provided information regarding investments attributed to COVID-19 patient treatment. The analysis included variations in occupation rates and billing from 2019 to 2020 observed in each institution, and the investments for medical equipment, individual protection materials and building construction per patient treated. Results: The majority of hospitals registered a decrease in hospitalization rates and revenue from 2019 to 2020. For intensive care units (ICUs), the mean occupancy rate ranged from 88% to 83%, and for wards, it ranged from 85% to 73%. Monthly average revenue decreased by 10%. The mean hospital investment per COVID-19 inpatient was I$6800 (standard deviation 7664), with the purchase of ventilators as the most common investment. For this item, the mean, highest and lowest acquisition cost per ventilator were, respectively, I$31 468, I$48 881 and I$17 777. Conclusion: There was significant variability in acquisition costs and investments by institution for responding to the COVID-19 pandemic. These findings highlight the importance of continuing microeconomic studies for a comprehensive assessment of hospital costs. Only with more detailed analyses, will it be possible to define and drive sustainable strategies to manage and reimburse COVID-19 treatment in health-care systems.