The role of respiratory superinfections in patients with coronavirus disease 2019 (COVID-19) pneumonia remains unclear. We investigated the prevalence of earlyand late-onset superinfections in invasively ventilated patients with COVID-19 pneumonia admitted to our department of intensive care medicine between March 2020 and November 2020. Of the 102 cases, 74 (72.5%) received invasive ventilation and were tested for viral, bacterial, and fungal pathogens on Days 0-7, 8-14, and 15-21 after the initiation of mechanical ventilation. Approximately 45% developed one or more respiratory superinfections. There was a clear correlation between the duration of invasive ventilation and the prevalence of coinfecting pathogens. Male patients with obesity and those suffering from chronic obstructive pulmonary disease and/or diabetes mellitus had a significantly higher probability to develop a respiratory superinfection. The prevalence of viral coinfections was high, with a predominance of the herpes simplex virus (HSV), followed by cytomegalovirus. No respiratory viruses or intracellular bacteria were detected in our cohort.We observed a high coincidence between Aspergillus fumigatus and HSV infection.Gram-negative bacteria were the most frequent pathogen group. Klebsiella aerogenes was detected early after intubation, while Klebsiella pneumoniae and Pseudomonas aeruginosa were related to a prolonged respiratory weaning.

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0–14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.

The aim of the present study was to evaluate the potential role of cerebrospinal fluid soluble urokinase receptor (suPAR) level, infection and age as risk factors for fatal outcome in patients suspected of having meningitis and/or bacteraemia on admission to hospital. A total of 545 cerebrospinal fluid samples from patients with clinically suspected meningitis were sent to the Hellenic National Meningitis Reference Laboratory. Ten of 545 (1.83%) patients died. Analysis by receiver operating characteristics (ROC) curve revealed that both suPAR and age were significant for prediction of fatal outcome. Patients with levels of suPAR above the cut-off values and age ≥ 51 years, or patients in which either Neisseria meningitis or Streptococcus pneumoniae were detected were categorized as high risk patients. The combination of the above three predictors (suPAR, age and infectious agent) in a logistic regression model with outcome of infection as the dependent variable yielded an overall odds ratio (OR = 85.7, 95% CI 10.6-690.2) with both sensitivity and specificity being equal to the value of 0.9. In conclusion, suPAR, age and type of infection have an additive effect in predicting mortality among patients suspected of meningitis.