Candidemia among Iranian Patients with Severe COVID-19 Admitted to ICUs.
Amir ArastehfarTahmineh ShabanHossein ZarrinfarMaryam RoudbaryMona GhazanfariMohammad-Taghi HedayatiAlireza SedaghatMacit IlkitMohammad Javad NajafzadehDavid S PerlinPublished in: Journal of fungi (Basel, Switzerland) (2021)
As a novel risk factor, COVID-19 has led to an increase in the incidence of candidemia and an elevated mortality rate. Despite being of clinical importance, there is a lack of data regarding COVID-19-associated candidemia (CAC) among Iranian patients. Therefore, in this retrospective study, we assessed CAC epidemiology in the intensive care units (ICUs) of two COVID-19 centers in Mashhad, Iran, from early November 2020 to late January 2021. Yeast isolates from patients' blood were identified by 21-plex polymerase chain reaction (PCR) and sequencing, then subjected to antifungal susceptibility testing according to the CLSI M27-A3 protocol. Among 1988 patients with COVID-19 admitted to ICUs, seven had fungemia (7/1988; 0.03%), among whom six had CAC. The mortality of the limited CAC cases was high and greatly exceeded that of patients with COVID-19 but without candidemia (100% (6/6) vs. 22.7% (452/1988)). In total, nine yeast isolates were collected from patients with fungemia: five Candida albicans, three C. glabrata, and one Rhodotorula mucilaginosa. Half of the patients infected with C. albicans (2/4) were refractory to both azoles and echinocandins. The high mortality of patients with CAC, despite antifungal therapy, reflects the severity of the disease in these patients and underscores the importance of rapid diagnosis and timely initiation of antifungal treatment.
Keyphrases
- candida albicans
- end stage renal disease
- coronavirus disease
- newly diagnosed
- sars cov
- chronic kidney disease
- ejection fraction
- peritoneal dialysis
- randomized controlled trial
- intensive care unit
- prognostic factors
- type diabetes
- machine learning
- staphylococcus aureus
- mesenchymal stem cells
- single cell
- electronic health record
- big data
- replacement therapy
- sensitive detection