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.

Understanding Sabiá virus infections (Brazilian mammarenavirus).

BACKGROUND: Only two naturally occurring human Sabiá virus (SABV) infections have been reported, and those occurred over 20 years ago. METHODS: We diagnosed two new cases of SABV infection using metagenomics in patients thought to have severe yellow fever and described new features of histopathological findings. RESULTS: We characterized clinical manifestations, histopathology and analyzed possible nosocomial transmission. Patients presented with hepatitis, bleeding, neurological alterations and died. We traced twenty-nine hospital contacts and evaluated them clinically and by RT-PCR and neutralizing antibodies. Autopsies uncovered unique features on electron microscopy, such as hepatocyte "pinewood knot" lesions. Although previous reports with similar New-World arenavirus had nosocomial transmission, our data did not find any case in contact tracing. CONCLUSIONS: Although an apparent by rare, Brazilian mammarenavirus infection is an etiology for acute hemorrhagic fever syndrome. The two fatal cases had peculiar histopathological findings not previously described. The virological diagnosis was possible only by contemporary techniques such as metagenomic assays. We found no subsequent infections when we used serological and molecular tests to evaluate close contacts.