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Fungemia remains a major threat in intensive care units (ICUs), with high mortality rates despite advances in diagnostics and treatment. Colonisation by yeasts is an independent risk factor for fungemia; however, its predictive utility requires further research. In this 8-year study, we analysed 38,017 samples from 3206 patients and 171 fungemia episodes as part of a weekly fungal surveillance programme. We evaluated species-specific colonisation patterns, the predictive value of the Colonisation Index (CI) and Corrected Colonisation Index (CCI), and candidemia risks associated with different yeast species and anatomical site colonisation. Our results showed that C. auris , N. glabratus , and C. parapsilosis colonisation increased with longer hospital stays (0.8% to 11.55%, 8.13% to 16.8%, and 1.93% to 5.14%, respectively). The CI and CCI had low discriminatory power (AUROC 67% and 66%). Colonisation by any yeast genera demonstrated high sensitivity (98.32%) and negative predictive value (NPV) (95.90%) but low specificity and positive predictive value (PPV) (23.90% and 6.64%). Tracheal and urine cultures had the highest PPV (15.64% and 12.91%), while inguinal cultures had the highest NPV (98.60%). C. auris (12.32%) and C. parapsilosis (5.5%) were associated with a higher fungemia risk (log-rank < 0.001). These findings support the use of weekly surveillance to better stratify the fungemia risk and optimise antifungal use in ICUs.

The present investigation assessed the Liaison® diagnostic performance in detecting Epstein-Barr (EBV) IgM-VCA in a large patient cohort, considering age and symptomatology. VIDAS® were employed as a benchmark for acute EBV infection. The study also probed other coexisting conditions and potential cross-reactivity for error sources. A total of 1311 samples were analyzed, with notable associations found only among paediatric (kappa=0.75) and young adult (kappa=0.58) populations with compatible symptoms. ROC analysis revealed varying optimal cutoff values based on age and symptom categorizations. Logistic regression models identified age and patients from Oncology or Infectious Disease as significant factors for false positives. Potential interferences emerged with RF, ANCA, cytomegalovirus-IgM and VHS-IgM. Notably, Liaison® couldn´t distinguish EBV patients from Oncology, Haemathology or Internal Medicine. This study provides valuable insights, such as implementing ageand symptom-specific thresholds or reviewing test requests, for optimizing EBV serology in microbiology laboratories, leading to faster and more reliable responses., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest related to this research. There are no financial, personal, or professional relationships that could potentially bias or influence the interpretation of the results presented in this study., (Copyright © 2023 Elsevier Inc. All rights reserved.)