Clonal outbreaks due to azole-resistant Candida parapsilosis (ARCP) isolates have been reported in numerous studies, but the environmental niche of such isolates has yet to be defined. Herein, we aimed to identify the environmental niche of ARCP isolates causing unremitting clonal outbreaks in an adult ICU from a Brazilian cancer referral center. C. parapsilosis sensu stricto isolates recovered from blood cultures, pericatheter skins, healthcare workers (HCW), and nosocomial surfaces were genotyped by multilocus microsatellite typing (MLMT). Antifungal susceptibility testing was performed by the EUCAST (European Committee for Antimicrobial Susceptibility Testing) broth microdilution reference method and ERG11 was sequenced to determine the azole resistance mechanism. Approximately 68% of isolates were fluconazole-resistant (76/112), including pericatheter skins (3/3, 100%), blood cultures (63/70, 90%), nosocomial surfaces (6/11, 54.5%), and HCW’s hands (4/28, 14.2%). MLMT revealed five clusters: the major cluster contained 88.2% of ARCP isolates (67/76) collected from blood (57/70), bed (2/2), pericatheter skin (2/3), from carts (3/7), and HCW’s hands (3/27). ARCP isolates were associated with a higher 30 day crude mortality rate (63.8%) than non-ARCP ones (20%, p = 0.008), and resisted two environmental decontamination attempts using quaternary ammonium. This study for the first time identified ARCP isolates harboring the Erg11-Y132F mutation from nosocomial surfaces and HCW’s hands, which were genetically identical to ARCP blood isolates. Therefore, it is likely that persisting clonal outbreak due to ARCP isolates was fueled by environmental sources. The resistance of Y132F ARCP isolates to disinfectants, and their potential association with a high mortality rate, warrant vigilant source control using effective environmental decontamination.

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.

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.