Impact Of The Vaccination Against Covid-19 On Frontline Health Workers.

INTRODUCTION: Brazil has been facing the pandemic of COVID-19 since march 2020. To date, more than 540,000 people have died from this disease in the country. There are estimates that indicate that the population exposed to SARS-CoV-2 represents 1 to 20%, however, these data are questionable due to the number of asymptomatic and untested individuals. As a result, vaccination for COVID-19 has become the main means of achieving herd immunity. OBJECTIVES: To demonstrate, through local sampling, that broad and rapid vaccination may decrease the rate of COVID-19 detection in individuals potentially exposed to the SARS-CoV-2 virus. RESULTS: A total of 1,128 individuals were studied, including students and health professionals from Centro Universitário FMABC, who received the two doses of the vaccine for COVID-19 (Oxford/Astrazeneca ® and CoronaVac®). There was a 41% reduction in the demand for RT-PCR test after vaccination, in the studied period. And a 78.3% reduction in positive results after vaccination started Conclusion: The results of this study showed that, even vaccinating a population with higher exposure to the risk of contamination, there was a significant reduction in test positivity and in the demand to perform these tests. Emphasizing that vaccination is the best strategy to achieve herd immunity and to reduce the spread of the disease.

Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data

Introduction Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. Methods In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. Results The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU;1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. Discussion and conclusions The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.

Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data.

INTRODUCTION: Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. METHODS: In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. RESULTS: The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. DISCUSSION AND CONCLUSIONS: The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.

Correlating drug prescriptions with prognosis in severe COVID-19: first step towards resource management.

BACKGROUND: Optimal COVID-19 management is still undefined. In this complicated scenario, the construction of a computational model capable of extracting information from electronic medical records, correlating signs, symptoms and medical prescriptions, could improve patient management/prognosis. METHODS: The aim of this study is to investigate the correlation between drug prescriptions and outcome in patients with COVID-19. We extracted data from 3674 medical records of hospitalized patients: drug prescriptions, outcome, and demographics. The outcome evaluated was hospital outcome. We applied correlation analysis using a Logistic Regression algorithm for machine learning with Lasso and Matthews correlation coefficient. RESULTS: We found correlations between drugs and patient outcomes (death/discharged alive). Anticoagulants, used very frequently during all phases of the disease, were associated with good prognosis only after the first week of symptoms. Antibiotics very frequently prescribed, especially early, were not correlated with outcome, suggesting that bacterial infections may not be important in determining prognosis. There were no differences between age groups. CONCLUSIONS: In conclusion, we achieved an important result in the area of Artificial Intelligence, as we were able to establish a correlation between concrete variables in a real and extremely complex environment of clinical data from COVID-19. Our results are an initial and promising contribution in decision-making and real-time environments to support resource management and forecasting prognosis of patients with COVID-19.

Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records.

BACKGROUND: COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. METHODS: We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clínicas (São Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient's outcome. RESULTS: Time series-based machine learning models are capable of predicting a COVID-19 patient's outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). CONCLUSIONS: Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.

SARS-CoV-2/COVID-19: Clinical course among subjects HIV-1-infected in Sao Paulo.

INTRODUCTION: People living with Human Immunodeficiency Virus (HIV) are ander risk for co-infection with SARS-CoV-2. This population may be more prone to complications from COVID-19 due to persistent inflammation caused by HIV and higher incidence of metabolic syndromes, cardiovascular diseases, and malignancies, as well as being considered elderly at 50 years of age. The objective of this study was to report SARS-CoV-2 infection frequency, clinical evolution, and mortality in HIV-positive patients on antiretroviral therapy. METHODS: The period of inquiry ranged from January to September 2020. Due to the social distance and the suspension of in-person medical care during the time of the investigation, we sent electronic questions about demographic, epidemiological, and clinical data, to 403 patients HIV-infected. RESULTS: Among 260 patients who answered the questionnaire, thirty-nine patients (15%) had suggestive symptoms and were tested for SARS-CoV-2 infection. Of this, 11 had positive results (32.4%), no patient died of COVID-19 complications. Nine are male (3.4%), and the mean age of the patients with positive results was 43.2 years (± 9.6). 107 patients (41.1%) were over 50 years of age and their mean T-CD4+ cell count was 768 cells. Eleven patients (4.2%) had a detectable HIV RNA viral load and 127 (48.8%) had comorbidities. These variables were not associated with increased risk for infection. CONCLUSION: The frequency of Sars-Cov2 infection among HIV-infected is similar to the general population, and the clinical course is associated with the presence of comorbidities and not due the HIV infection. However, new studies shoud bem done to access if this vulnerable population could be answer to the vaccine anti-SARS-Cov2.

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.