Candida bloodstream infections in Serbia: First multicentre report of a national prospective observational survey in intensive care units.
Valentina Arsi'c Arsenijevi'cSuzana A OtasevicDragana JanićPredrag MinićJovan MatijaševićDeana MedićIvanka SavićSnežana DelićSuzana Nestorović LabanZorica VasiljevićMirjana HadnadjevPublished in: Mycoses (2017)
Candida bloodstream infections (BSI) are a significant cause of mortality in intensive care units (ICU), hereof the prospective 12-months (2014-2015) hospital- and laboratory-based survey was performed at the Serbian National Reference Medical Mycology Laboratory (NRMML). Candida identification was done by a matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and a susceptibility test, according to the Clinical and Laboratory Standards Institute methodology. Among nine centres (265 beds; 10 820 patient admissions), four neonatal/paediatric (NICU/PICUs) and five adult centres (ICUs) participated, representing 89 beds and 3446 patient admissions, 166 beds and 7347 patient admissions respectively. The NRMML received 43 isolates, 17 from NICU/PICUs and 26 from adult ICUs. C. albicans dominated highly in NICU/PICUs (~71%), whereas C. albicans and C. parapsilosis were equally distributed within adults (46%, each), both accounting for ~90% of received isolates. The resistance to itraconazole and flucytosine were 25% and 2.4% respectively. In addition, the 2 C. albicans were azole cross-resistant (4.6%). The overall incidence of CandidaBSI was ~3.97 cases/1000 patient admissions (4.93 in NICU/PICU and 3.53 in adult ICU). The 30-day mortality was ~37%, most associated with C. tropicalis and C. glabrataBSI. Data from this national survey may contribute to improving the Balkan and Mediterranean region epidemiology of CandidaBSI within ICUs.
Keyphrases
- candida albicans
- intensive care unit
- preterm infants
- case report
- risk factors
- cross sectional
- healthcare
- biofilm formation
- mechanical ventilation
- emergency department
- quality improvement
- type diabetes
- machine learning
- escherichia coli
- staphylococcus aureus
- mass spectrometry
- double blind
- neural network
- genetic diversity