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.

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.

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.

Are smoking, environmental pollution, and weather conditions risk factors for COVID-19?

ABSTRACT Coronavirus disease 2019 (COVID-19), caused by the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is probably systemic, has a major respiratory component, and is transmitted by person-to-person contact, via airborne droplets or aerosols. In the respiratory tract, the virus begins to replicate within cells, after which the host starts shedding the virus. The individuals recognized as being at risk for an unfavorable COVID-19 outcome are those >60 years of age, those with chronic diseases such as diabetes mellitus, those with hypertension, and those with chronic lung diseases, as well as those using chemotherapy, corticosteroids, or biological agents. Some studies have suggested that infection with SARS-CoV-2 is associated with other risk factors, such as smoking, external environmental pollution, and certain climatic conditions. The purpose of this narrative review was to perform a critical assessment of the relationship between COVID-19 and these potential risk factors. RESUMO A doença denominada COVID-19, causada pelo vírus altamente contagioso denominado SARS-CoV-2, é uma doença provavelmente sistêmica com importante componente respiratório e é transmitida pelo contato com uma pessoa infectada por meio de gotículas e/ou aerossóis. Após atingir o trato respiratório, o vírus inicia a multiplicação intracelular e, a seguir, sua semeadura. Os grupos de risco reconhecidos para uma evolução desfavorável são indivíduos com idade >60 anos, portadores de doenças crônicas, como diabetes mellitus, hipertensão arterial sistêmica e/ou doenças pulmonares crônicas, assim como aqueles em uso de quimioterápicos, corticosteroides ou imunobiológicos. Alguns estudos mostram uma possível associação do SARS-CoV-2 com outros fatores de risco, como tabagismo, poluição ambiental externa e determinadas condições climáticas. O objetivo desta revisão narrativa foi avaliar criticamente a relação entre COVID-19 e esses possíveis fatores de risco.

Are smoking, environmental pollution, and weather conditions risk factors for COVID-19?

Coronavirus disease 2019 (COVID-19), caused by the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is probably systemic, has a major respiratory component, and is transmitted by person-to-person contact, via airborne droplets or aerosols. In the respiratory tract, the virus begins to replicate within cells, after which the host starts shedding the virus. The individuals recognized as being at risk for an unfavorable COVID-19 outcome are those > 60 years of age, those with chronic diseases such as diabetes mellitus, those with hypertension, and those with chronic lung diseases, as well as those using chemotherapy, corticosteroids, or biological agents. Some studies have suggested that infection with SARS-CoV-2 is associated with other risk factors, such as smoking, external environmental pollution, and certain climatic conditions. The purpose of this narrative review was to perform a critical assessment of the relationship between COVID-19 and these potential risk factors.

Diretrizes para o tratamento farmacológico da COVID-19. Consenso da Associação de Medicina Intensiva Brasileira, da Sociedade Brasileira de Infectologia e da Sociedade Brasileira de Pneumologia e Tisiologia

INTRODUCTION: Different therapies are currently used, considered, or proposed for the treatment of COVID-19;for many of those therapies, no appropriate assessment of effectiveness and safety was performed. This document aims to provide scientifically available evidence-based information in a transparent interpretation, to subsidize decisions related to the pharmacological therapy of COVID-19 in Brazil. METHODS: A group of 27 experts and methodologists integrated a task-force formed by professionals from the Brazilian Association of Intensive Care Medicine (Associação de Medicina Intensiva Brasileira - AMIB), the Brazilian Society of Infectious Diseases (Sociedad Brasileira de Infectologia - SBI) and the Brazilian Society of Pulmonology and Tisiology (Sociedade Brasileira de Pneumologia e Tisiologia - SBPT). Rapid systematic reviews, updated on April 28, 2020, were conducted. The assessment of the quality of evidence and the development of recommendations followed the GRADE system. The recommendations were written on May 5, 8, and 13, 2020. RESULTS: Eleven recommendations were issued based on low or very-low level evidence. We do not recommend the routine use of hydroxychloroquine, chloroquine, azithromycin, lopinavir/ritonavir, corticosteroids, or tocilizumab for the treatment of COVID-19. Prophylactic heparin should be used in hospitalized patients, however, no anticoagulation should be provided for patients without a specific clinical indication. Antibiotics and oseltamivir should only be considered for patients with suspected bacterial or influenza coinfection, respectively. CONCLUSION: So far no pharmacological intervention was proven effective and safe to warrant its use in the routine treatment of COVID-19 patients;therefore such patients should ideally be treated in the context of clinical trials. The recommendations herein provided will be revised continuously aiming to capture newly generated evidence.

Guidelines for the pharmacological treatment of COVID-19. The task-force/consensus guideline of the Brazilian Association of Intensive Care Medicine, the Brazilian Society of Infectious Diseases and the Brazilian Society of Pulmonology and Tisiology. / Diretrizes para o tratamento farmacológico da COVID-19. Consenso da Associação de Medicina Intensiva Brasileira, da Sociedade Brasileira de Infectologia e da Sociedade Brasileira de Pneumologia e Tisiologia.

INTRODUCTION: Different therapies are currently used, considered, or proposed for the treatment of COVID-19; for many of those therapies, no appropriate assessment of effectiveness and safety was performed. This document aims to provide scientifically available evidence-based information in a transparent interpretation, to subsidize decisions related to the pharmacological therapy of COVID-19 in Brazil. METHODS: A group of 27 experts and methodologists integrated a task-force formed by professionals from the Brazilian Association of Intensive Care Medicine (Associação de Medicina Intensiva Brasileira - AMIB), the Brazilian Society of Infectious Diseases (Sociedad Brasileira de Infectologia - SBI) and the Brazilian Society of Pulmonology and Tisiology (Sociedade Brasileira de Pneumologia e Tisiologia - SBPT). Rapid systematic reviews, updated on April 28, 2020, were conducted. The assessment of the quality of evidence and the development of recommendations followed the GRADE system. The recommendations were written on May 5, 8, and 13, 2020. RESULTS: Eleven recommendations were issued based on low or very-low level evidence. We do not recommend the routine use of hydroxychloroquine, chloroquine, azithromycin, lopinavir/ritonavir, corticosteroids, or tocilizumab for the treatment of COVID-19. Prophylactic heparin should be used in hospitalized patients, however, no anticoagulation should be provided for patients without a specific clinical indication. Antibiotics and oseltamivir should only be considered for patients with suspected bacterial or influenza coinfection, respectively. CONCLUSION: So far no pharmacological intervention was proven effective and safe to warrant its use in the routine treatment of COVID-19 patients; therefore such patients should ideally be treated in the context of clinical trials. The recommendations herein provided will be revised continuously aiming to capture newly generated evidence.