Candida parapsilosis has emerged as a prominent cause of nosocomial candidemia, particularly among critically ill patients. The increasing prevalence of fluconazole-resistant C. parapsilosis (FR-Cp) poses major therapeutic challenges, especially in resource-limited settings. We conducted a retrospective study of 144 patients with C. parapsilosis candidemia admitted to two post-surgical ICUs at a Brazilian tertiary cardiothoracic hospital between 2016 and August 2024. Demographic, clinical, microbiological, and therapeutic data were analyzed. Predictors of 30-day mortality were identified through multivariate logistic regression. The incidence density of C. parapsilosis candidemia ranged from 2.93 to 8.31 per 1000 hospitalizations. Fluconazole resistance was identified in 81% of isolates. Overall 30-day mortality was 55%. Independent risk factors for mortality included cardiopathy (OR: 19.36, p = 0.006), higher SOFA scores (OR: 1.54, p = 0.003), parenteral nutrition (OR: 29.77, p = 0.013), and dialysis (OR: 6.59, p = 0.043), while longer treatment duration was protective (OR: 0.81, p < 0.001). Fluconazole resistance was not independently associated with increased mortality. In this cohort of critically ill patients, C. parapsilosis candidemia was associated with high mortality and a high prevalence of fluconazole resistance. Clinical outcomes were mainly driven by host-related and therapeutic factors rather than antifungal resistance alone.

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.

Candidemia is a severe healthcare-associated infection with persistently high mortality, particularly in critically ill patients. Beyond host severity, inadequate implementation of recommended management strategies may substantially influence outcomes. This retrospective observational study included adult patients with at least one episode of candidemia in a Brazilian tertiary hospital between 2018 and 2024. Clinical, microbiological, and therapeutic variables were analyzed, including adherence to an eight-item candidemia care bundle. In-hospital mortality was the primary outcome. Univariate, multivariate logistic regression, and Kaplan-Meier survival analyses were performed. Among 136 patients (mean age 62.4-years), 87.5% required ICU admission. Overall mortality reached 76.5%. Candida albicans (32.4%) and the C. parapsilosis complex (29.4%) were the most frequent species. Non-optimal treatment (OR = 5.52; p = 0.031) and failure to remove the central venous catheter (OR = 6.60; p = 0.004) were independent predictors of death. Catheter removal significantly improved survival (p < 0.001). Survivors showed greater adherence to the care bundle (3.66 vs. 1.98 items; p = 0.001) and more frequent Infectious Diseases consultation (50%vs. 28.8%; p = 0.027). Mortality did not differ between catheter-drawn and peripheral blood cultures. In this cohort, candidemia mortality was strongly associated with failure in early vascular source control and poor adherence to recommended management, underscoring quality of care as a key determinant of survival.

The COVID-19 pandemic highlighted the urgent need for rapid and accurate SARS-CoV-2 detection. Current diagnostic methods often suffer from discomfort, slow results, and limited accuracy in early infection stages. This study proposes a solution: a noninvasive, rapid, and accurate detection approach for point-of-care settings using a metal-oxide-sensor-based electronic nose. This innovative electronic nose uses an array of off-the-shelf gas sensors. These sensors detect and analyze the volatile organic compounds present in saliva and exhaled breath, which change based on the presence of the SARS-CoV-2 virus. We evaluated the discriminatory power of the electronic nose using a suite of machine learning algorithms, specifically K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Networks (NN), and Random Forest, to differentiate between SARS-CoV-2 infected and non-infected samples. Accuracy metrics ranged from 76% to 89% for exhaled breath samples and from 75% to 86% for saliva samples. Optimal accuracy was achieved with the KNN algorithm, yielding an Area Under the Curve (AUC) of 0.861 (95% CI 0.825‒0.897) for saliva and 0.895 (95% CI 0.850‒0.940) for exhaled breath. These results support the feasibility and proof-of-concept performance of a low-cost electronic nose for SARS-CoV-2 detection in a real-world hospital cohort.

Infective endocarditis (IE) remains a life-threatening condition with high mortality, particularly in middle-income countries where access to advanced diagnostics and timely surgery is unequal. Real-world data from such settings remain limited. This study describes the clinical and microbiological characteristics of patients with suspected IE and identifies independent predictors of in-hospital mortality at a Brazilian tertiary referral center. We conducted a retrospective cohort including adults hospitalized with suspected IE between 2012 and 2022. Cases were classified according to modified Duke criteria. Demographic, clinical, laboratory, microbiological, and echocardiographic data were collected, and independent predictors of in-hospital mortality were identified using multivariable logistic regression. Of the 112 patients evaluated, 66 met the criteria for definite or possible IE. Median age was 55 years, 59.1% were male, and 34.8% were ≥60 years. Structural heart disease was present in 87.8%, predominantly involving the mitral valve. Blood cultures were positive in 57.1%, with Staphylococcus aureus as the leading pathogen (25%), whereas 42.9% were culture-negative. Echocardiographic evidence of IE was documented in 80.9%, and valve surgery was performed in 51.5%. In-hospital mortality was 36.6%. Independent predictors of death included age ≥60 years (OR 7.0), hypertension (OR 3.9), new valvular regurgitation (OR 6.3), thrombocytopenia ≤150×10³/mm³ (OR 3.5), total bilirubin ≥1.2 mg/dL (OR 15.4), and arterial lactate >1.6 mmol/L (OR 5.2). In this real-world cohort, IE was characterized by diagnostic limitations, a high proportion of culture-negative cases, and high in-hospital mortality. Readily available clinical and laboratory parameters showed prognostic value and may support early risk stratification in resource-limited settings.