376. Predictive Model for Fluconazole Resistance in Patient With Candida Bloodstream Infection. (26th November 2018)

• Record Type: Journal Article
• Authors: Aljorayid, Abdullah; Kronen, Ryan; Salazar, Ana S; Hsueh, Kevin; Lin, Charlotte; Powderly, William; Spec, Andrej
• Is Part Of: Open forum infectious diseases. Volume 5(2018)Supplement 1
• Page Start: S146
• Page End: S146
• Publication Date: 2018-11-26
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofy210.387
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Candida bloodstream infection (CBSI) is associated with high morbidity and mortality. Guidelines recommend echinocandins as initial therapy, with fluconazole as an acceptable alternative in selected patients, including those at low risk for fluconazole resistance. We aimed to create a predictive model to identify patient at high risk of fluconazole resistance. Methods: We performed a retrospective analysis of hospitalized patients with CBSI at a large tertiary referral hospital between January 2007 and January 2015. Data were collected on demographics, comorbidities, medications, procedures, central lines, vital signs, and laboratory values. Univariate and multivariable logistic regression analyses were used to build the predictive model. Variables with P < 0.25 were considered for the multivariable analysis, and only those that remain significant ( P < 0.05) were retained in the final model. Results: We identified 1, 083 patients with CBSI, of whom 684 patients had azole susceptibility data available. Among cases with available resistance data, C. glabrata was the most common species isolated, occurring in 240 cases (38%), followed by C. parapsilosis, 176 cases (25.7%), and C. albican s, 121 cases (17.6%). One hundred thiry-nine isolates were found to have fluconazole resistance ( C. glabrata 55, C. krusei 36). Eighty-three variables were considered in the multivariable analysis; nine remained significant and were included in our final model. Variables associated with a higher risk of fluconazole resistance were: hematological cancer (OR 1.69 [95% CI 1.03, 2.79]), presence of an indwelling line (2.00 [1.30, 3.10]), prior fluconazole use (2.46 [1.32, 4.56]), prior voriconazole use (10.89 [1.18, 99.84]), prior calcineurin inhibitor use (2.65 [1.24, 5.66]), prior nitroimidazole use (1.63 [1.01, 2.64]), and prior tetracycline use (4.77 [1.96, 11.64]). Isolation of C. parapsilosis (0.20 [0.10, 0.39]), and chronic pulmonary disease (0.43 [0.21, 0.87]) were associated with a lower risk of resistance. The final model had a C-statistic of 0.75. Conclusion: We identified nine risk factors that were significantly associated with fluconazole resistance. By creating a predictive model, patients at higher or lower risk for resistance may be identified earlier which may assist in the choice of initial antifungal treatment. Disclosures: All authors: No reported disclosures.