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

Abstract

Abstract Background Fluconazole is recommended as first-line therapy for candidemia when risk of fluconazole resistance (fluc-R) is low. Lack of methods to estimate resistance risk results in extended use of echinocandins and prolonged hospitalization. This study aimed to develop a clinical predictive model to identify patients at low risk for fluc-R where initial or early step-down fluconazole would be appropriate. Methods Retrospective analysis of hospitalized adult patients with positive blood culture for Candida spp from 2013 to 2019. Multivariable logistic regression model was performed to identify factors associated with fluc-R. Stepwise regression was performed on bootstrapped samples to test individual variable stability and estimate confidence intervals (CIs). We used receiver operating characteristic curves to assess performance across the probability spectrum. Results We identified 539 adults with candidemia and 72 Candida isolates (13.4%) were fluc-R. Increased risk of fluc-R was associated with older age, prior bacterial bloodstream infection (odds ratio [OR], 2.02 [95% CI, 1.13–3.63]), myelodysplastic syndrome (OR, 3.09 [95% CI, 1.13–8.44]), receipt of azole therapy (OR, 5.42 [95% CI, 2.90–10.1]) within 1 year of index blood culture, and history of bone marrow or stem cell transplant (OR, 2.81 [95% CI, 1.41–5.63]). The model had good discrimination (optimism-corrected c-statistic 0.771), and all of the selected variables were stable. The prediction model had a negative predictive value of 95.7% for the selected sensitivity cutoff of 90.3%. Conclusions This model is a potential tool for identifying patients at low risk for fluc-R candidemia to receive first-line or early step-down fluconazole.