Risk predictive models should factor in both clinical risk factors and Candida colonisation parameters. Integrating these models into therapeutic algorithms first requires external validation in different patient populations and settings.

k Colonization with Candida species is an independent risk factor for invasive candidiasis (IC), but the minimum and most practicable parameters for prediction of IC have not been optimized. We evaluated Candida colonization in a prospective cohort of 6,015 nonneutropenic, critically ill patients. Throat, perineum, and urine were sampled 72 h post-intensive care unit (ICU) admission and twice weekly until discharge or death. Specimens were cultured onto chromogenic agar, and a subset underwent molecular characterization. Sixty-three (86%) patients who developed IC were colonized prior to infection; 61 (97%) tested positive within the first two time points. The median time from colonization to IC was 7 days (range, 0 to 35). Colonization at any site was predictive of IC, with the risk of infection highest for urine colonization (relative risk [RR] ‫؍‬ 2.25) but with the sensitivity highest (98%) for throat and/or perineum colonization. Colonization of >2 sites and heavy colonization of >1 site were significant independent risk factors for IC (RR ‫؍‬ 2.25 and RR ‫؍‬ 3.7, respectively), increasing specificity to 71% to 74% but decreasing sensitivity to 48% to 58%. Molecular testing would have prompted a resistance-driven decision to switch from fluconazole treatment in only 11% of patients infected with C. glabrata, based upon species-level identification alone. Positive predictive values (PPVs) were low (2% to 4%) and negative predictive values (NPVs) high (99% to 100%) regardless of which parameters were applied. In the Australian ICU setting, culture of throat and perineum within the first two time points after ICU admission captures 84% (61/73 patients) of subsequent IC cases. These optimized parameters, in combination with clinical risk factors, should strengthen development of a setting-specific risk-predictive model for IC. Invasive candidiasis (IC), particularly, candidemia, is a significant cause of mortality in critically ill patients, accounting for almost a third of nosocomial infections in intensive care units (ICUs) (1, 2). Early antifungal therapy reduces IC-related mortality and approximately halves the incidence of IC (3-8). Untargeted prophylactic antifungal use, however, is expensive, has the potential to cause adverse drug reactions, and may select for resistant fungal species (9).Clinical risk prediction rules that identify the high-risk patients most likely to benefit from prophylaxis have been developed (10-16), but those studies have used different sets of predictors, including (a) clinical risk factors only and (b) clinical risk factors in combination with colonization indices (CIs). Those rules have been applied in various settings and have included assessment at different times postadmission to the ICU and different types of ICU (surgical ICUs only or mixed medical/surgical ICUs-defined as units that house both medical and surgical populations). These differences may explain why predictive models and algorithms have performed poorly outside their derivative populations (17).Since IC is pr...