A clinical predictive model of candidaemia by Candida auris in previously colonized critically ill patients

Candida auris is an emerging multidrug-resistant fungus that has been associated with nosocomial outbreaks with high rates of mortality and transmission. The aim of this study was to perform a retrospective cohort analysis of risk factors and to build a scoring method for estimating the risk of candidaemia in colonized critically ill patients.

We performed a retrospective observational cohort study of patients aged ≥15 years colonized by C. auris in the 3-year period between March 2016 and March 2019. Epidemiological, clinical, laboratory and microbiological data were collected. We developed a predictive model for candidaemia using elastic net multivariable logistic regression techniques, assessed its discriminative capacity, and internally validated it using bootstrap resampling.

Two-hundred and six patients were enrolled in the cohort for derivation and internal validation. Thirty-seven out of 206 patients developed candidaemia. Total parenteral nutrition was the foremost risk factor (adjusted OR 3.73); previous surgery (adjusted OR 1.03), sepsis (adjusted OR 1.75), previous exposure to antifungal agents (adjusted OR 1.17), arterial catheters (adjusted OR 1.46), central venous catheters (adjusted OR 1.21), presence of advanced chronic kidney disease (adjusted OR 1.35) and multifocal colonization (adjusted OR of unifocal colonization 0.46) were proven to be independent predictors of candidaemia in our cohort. The corresponding area under the curve (AUC) of the elastic net regularized predictive model was 0.89 (95%CI 0.826; 0.951). After performing the internal validation by generating 500 bootstrap replications, the model still showed great accuracy, with a resulting AUC of 0.84.

Our study provides evidence on the independent predisposing factors for candidaemia. It may help predict its estimated risk and may identify a high-risk population that could benefit from early or prophylactic antifungal treatment after external validation in other cohorts.