Clinical predictive models of invasive Candida infection: A systematic literature review

Clinical predictive models (CPM) serve to identify and categorize patients into risk categories to assist in treatment and intervention recommendations. Predictive accuracy and practicality of models varies depending on methods used for their development, and should be evaluated. The aim of this study was to summarize currently available CPM for invasive candidiasis, analyze their performance, and assess their suitability for use in clinical decision making. We identified studies that described the construction of a CPM for invasive candidiasis from PubMed/MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library databases, and Clinicaltrials.gov. Data extracted included: author, data source, study design, recruitment period, characteristics of study population, outcome types, predictor types, number of study participants and outcome events, modelling method, and list of predictors used in the final model. Calibration and discrimination in the derivative datasets were used to assess the performance of each model. Ten articles were identified in our search and included for full text review. Five models were developed using data from ICUs, and five models included all hospitalized patients. The findings of this review highlight the limitations of currently available models to predict invasive candidiasis, including lack of generalizability, difficulty in everyday clinical use, and overly optimistic performance. There are significant concerns regarding predictive performance and usability in every day practice of existing CPM to predict invasive candidiasis.