Predicting Candidemia Risk in ICU Patients

Statistical analyses were performed using the Stata software (v15.1; College Station, TX, USA). Factors associated with candidemia and mortality were analyzed by using univariate and multivariate conditional logistic regression models. A backward stepwise logistic regression was used to select variables entered in the multivariate models, using a cutoff p value of 0.10. New scores to predict the risk of candidemia were developed for patients in and outside the ICU. Scores were obtained by rounding the β-coefficients. Receiving operating characteristic (ROC) curves were drawn using rocreg implemented in Stata®, after adjustment for matching covariates [32 (link)]. Test efficiencies were calculated using the dtroc softwares (Stata®). The best cutoff point was established according to standard methods (Youdden’s approach to determine the cutoff with the best compromise between sensitivity and specificity; the method of Zweig and Campbell, maximizing efficiency) [33 (link), 34 (link)] by using cutpt (Stata®).

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Poissy J., Damonti L., Bignon A., Khanna N., Von Kietzell M., Boggian K., Neofytos D., Vuotto F., Coiteux V., Artru F., Zimmerli S., Pagani J.L., Calandra T., Sendid B., Poulain D., van Delden C., Lamoth F., Marchetti O, & Bochud P.Y. (2020). Risk factors for candidemia: a prospective matched case-control study. Critical Care, 24, 109.

Publication 2020

Stata

Candidemia Patients

Corresponding Organization :

Other organizations : Lille Inflammation Research International Center, University Hospital of Lausanne, University of Lausanne, University Hospital of Bern, University of Bern, Centre Hospitalier Universitaire de Lille, University of Basel, Kantonsspital St. Gallen, Hôpital Beau-Séjour, Université de Lille, Hôpital de Morges

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Variable analysis

independent variables

Factors associated with candidemia and mortality

dependent variables

Candidemia
Mortality

control variables

Matching covariates

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