Clinical Characteristics and Risk Factors for COVID-19-associated Candidemia

Patients with COVID-19-associated candidemia (CAC) in an intensive care unit (ICU) were matched 1:2 with those without candidemia, based on ICU admission date and length of stay in ICU being at least equal to that before candidemia in the corresponding case. The incidence rate of CAC was 2.34 per 1,000 ICU days. Eighty cases could be matched to appropriate controls. In the multivariate conditional logistic regression analysis, age (P 0.001), and sequential organ failure assessment score (P 0.046) were the only risk factors independently associated with CAC. Tocilizumab and corticosteroids therapy were not independently associated with candidemia. Lay Summary In COVID-19 patients who need medical care in an intensive care unit, the risk of developing bloodstream Candida infection is higher in older patients and in those who have a more severe critical illness. Treatment with steroids or tocilizumab does not seem to affect the risk of candida bloodstream infection in these patients.

Risk Factors and Outcomes of Non-albicans Candida Bloodstream Infection in Patients with Candidemia at Siriraj Hospital—Thailand’s Largest National Tertiary Referral Hospital

This study aimed to investigate the risk factors for and the outcomes of patients with candidemia caused by non-albicans Candida. Candidemia patients treated at Siriraj Hospital (Bangkok, Thailand) during January 2016 to December 2017 were enrolled. A total of 156 patients (mean age: 65 years, 56.4% male) were included. The most prevalent underlying conditions were diabetes (32.1%), chronic cardiac disease (28.2%), chronic kidney disease (26.9%), and hematologic malignancies (21.2%). Candida species isolated from patient blood were C. tropicalis (49.4%), C. albicans (28.8%), C. glabrata (16.7%), and C. parapsilosis (5.1%). Fluconazole resistance was significantly increased in C. tropicalis (37.8%). No independent risk factors were associated with patients with non-albicans Candida candidemia compared to those with C. albicans candidemia. There was no significant difference in mortality between patients with non-albicans Candida candidemia and patients with C. albicans candidemia (OR: 1.35, 95% CI: 0.64–2.85). When compared with C. albicans candidemia, multivariate analysis revealed chronic liver disease (OR: 11.39, 95% CI: 1.38–94.02), neutropenia (OR: 4.31, 95% CI: 1.34–13.87), and male gender (OR: 2.34, 95% CI: 1.04–5.29) to be independent risk factors for C. tropicalis candidemia. The observed high resistance of C. tropicalis to fluconazole indicates that fluconazole should not be used for empirical antifungal treatment in these patients.

Wickerhamomyces anomalous: A Rare Cause of Fungemia Causing Febrile Neutropenia in Acute Lymphoblastic Leukemia

Candida bloodstream infection is the major cause of increased morbidity and mortality (20–49%) in hospitalized patients in both paediatric and adult age groups. Due to the increase in the number of immunocompromised patients, other important species such as Trichosporon asahii and Debaryomyces hansenii are emerging. One such organism, Wickerhamomyces anomalous, previously known as Pichia anomala (teleomorph stages of several Candida species), is increasingly being reported as a cause of fungemia in neonatal intensive care units and is now increasingly being reported in a lot of immunosuppressive conditions such as interstitial lung disease, endocarditis, enteritis, corticosteroids, and chemotherapy uptake. Though this yeast is ubiquitous in nature, systemic infections from isolated cases and sporadic outbreaks with high mortality have been reported in ICUs, which emphasize the importance to consider this fungus within the diagnostic possibilities. Here, we report a case of catheter-related bloodstream infection (CRBSI) caused by W. anomalus in a leukemic immunosuppressed patient who was successfully treated by early detection and treatment of this emerging fungus.

168. Creation and Internal Validation of a Clinical Predictive Model for Fluconazole Resistance in Patients with Candida Bloodstream Infection

Background IDSA guidelines on candidemia recommend fluconazole as first-line therapy in patients considered low risk for fluconazole resistant infections. However, there is currently no mechanism to determine risk of resistance, and most community hospitals cannot perform rapid sensitivity testing, leading to prolonged use of echinocandin therapy. This study aims to develop a clinical predictive model to identify patients at low risk for fluconazole resistance where first-line use of fluconazole therapy would be acceptable without requiring resistance testing. Methods We performed a retrospective cohort analysis of all hospitalized adult patients with a positive blood culture for Candida spp. from 2013 to 2018. Fluconazole resistance was determined using Sensititre™ YeastOne™ YO9 AST Plate, with cutoffs defined for each Candida species based on Clinical and Laboratory Standards Institute performance standards for antifungal testing (M60) in all patients. Using backwards stepwise regression, we developed a multivariable logistic regression model to identify factors associated with fluconazole resistance in patients in Candida bloodstream infection, including only variables with clinical plausibility and p < 0.1 in bivariable analysis. Stepwise regression was performed on bootstrapped samples to test individual variable stability and estimate confidence intervals. We used graphs of observed vs expected values to assess model performance across the probability spectrum. Results We identified 539 patients with Candida bloodstream infection from 2013–2018, of which 13.4% (72/539) were fluconazole resistant. Increased risk of fluconazole resistance was associated with age (1.12 [1.01, 1.24]), bacterial septicemia (2.14 [1.20, 3.79]), receipt of previous azole therapy (5.47 [2.92, 10.26]), bone marrow transplant (2.63 [1.31, 5.29]), and myelodysplastic syndrome (3.13 [1.14, 8.60]). The model predicted fluconazole sensitivity well (c-statistic 0.788) and all the variables were stable (Figure 1). Figure 1. Graph comparing observed versus expected probability of fluconazole resistance. Bars included on the top parameter of the graph indicate the number of individuals, illustrating the distribution of the sample. Conclusion The presented model provides a potential tool for identifying the 80% of patients at low enough risk for fluconazole resistance to receive empiric therapy with azoles and reduce use of echinocandins. Disclosures Margaret A. Olsen, PhD, MPH, Merck (Grant/Research Support)Pfizer (Consultant, Grant/Research Support) Dustin Stwalley, MA, AbbVie Inc (Shareholder)Bristol-Myers Squibb (Shareholder) Andrej Spec, MD, MSCI, Astellas (Grant/Research Support)Mayne (Consultant)Scynexis (Consultant)