Noninvasive Combined Diagnosis and Monitoring of Aspergillus and Pseudomonas Infections: Proof of Concept

In acutely ill patients, particularly in intensive care units or in mixed infections, time to a microbe-specific diagnosis is critical to a successful outcome of therapy. We report the application of evolving technologies involving mass spectrometry to diagnose and monitor a patient’s course. As proof of this concept, we studied five patients and used two rat models of mono-infection and coinfection. We report the noninvasive combined monitoring of Aspergillus fumigatus and Pseudomonas aeruginosa infection. The invasive coinfection was detected by monitoring the fungal triacetylfusarinine C and ferricrocin siderophore levels and the bacterial metabolites pyoverdin E, pyochelin, and 2-heptyl-4-quinolone, studied in the urine, endotracheal aspirate, or breath condensate. The coinfection was monitored by mass spectrometry followed by isotopic data filtering. In the rat infection model, detection indicated 100-fold more siderophores in urine compared to sera, indicating the diagnostic potential of urine sampling. The tools utilized in our studies can now be examined in large clinical series, where we could expect the accuracy and speed of diagnosis to be competitive with conventional methods and provide advantages in unraveling the complexities of mixed infections

A Simple Risk Formula for the Prediction of COVID-19 Hospital Mortality

SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality in hospitalized patients. We aimed to assess the risk factors for hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. A total of 991 patients hospitalized between January 2021 and March 2021 with a PCR-confirmed SARS-CoV-2 acute respiratory infection in two university hospitals and five rural hospitals were included in this analysis. After excluding patients with unknown outcomes, 790 patients entered the final analyses. Out of 790 patients included in the analysis, 282/790 (35.7%) patients died in the hospital; 162/790 (20.5) were male and 120/790 (15.2%) were female. There were 141/790 (18%) patients with mild, 461/790 (58.3%) with moderate, and 187/790 (23.7%) with severe courses of the disease based mainly on the oxygenation status. The best-performing multivariate regression model contains only two predictors—age and the patient’s state; both predictors were rendered significant (p < 0.0001). Both age and disease state are very significant predictors of hospital mortality. An increase in age by 10 years raises the risk of hospital mortality by a factor of 2.5, and a unit increase in the oxygenation status raises the risk of hospital mortality by a factor of 20