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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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

Introduction: Paracoccidioidomycosis is a type of mycosis that is endemic to Brazil and is triggered by the fungus Paracoccidioides brasiliensis. Isolated bone involvement in this disease is very rare, especially in children. To the best of our knowledge this report documents the first case of an immunocompetent pediatric patient in which paracoccidioidomycosis of the hip articulation was the sole manifestation of the disease (that is, there were no pulmonary or skin lesions)., Case Presentation: An 11-year-old Brazilian Caucasian boy from a rural area was examined in the orthopedic ward of our emergency department. Our patient reported a three-month history of pain in the right hip with intermittent claudication and also complained of recurring episodes of intense pain and an inability to walk, which he had been experiencing for the previous five days. He additionally presented with a fever that had persisted for two days. Our patient's medical history did not include any clinical respiratory manifestations, skin lesions, history of trauma or immunosuppression risk factors., Conclusions: This is one of the very few reported cases of isolated articular involvement in osteomyelitis in a pediatric immunocompetent patient. Paracoccidioidomycosis should be considered among the differential diagnoses in such cases, especially in cases of patients who reside in rural areas where the condition is considered to be endemic, in order to administer the proper course of treatment in a timely fashion and improve the chances of a favorable prognosis.