[Research progress on predictive modeling of invasive candidiasis in critically ill patients]

Invasive candidiasis is one of the common invasive fungal infections in the intensive care unit (ICU), characterized by high incidence, high mortality, and diagnostic difficulty. The clinical presentation of invasive candidiasis lacks specificity, and conventional diagnostic methods are time-consuming with low positivity rates, often leading to delayed treatment and increased death risk. Therefore, early and precise identification of high-risk patients, along with the development of sensitive and specific risk assessment tools to guide individualized antifungal strategies, remains a critical clinical challenge. In recent years, traditional prediction models based on clinical risk factors, Candida colonization status, and microbiological markers have been progressively refined and have played an important role in risk stratification and empirical antifungal therapy decisions. However, their positive predictive value and generalizability remain limited. With advances in precision medicine, biomarkers such as (1,3)-β-D-glucan (BDG), inflammatory and nutritional indicators, immune cell subsets, host response assays, and molecular diagnostic techniques are increasingly being incorporated into risk assessment frameworks, driving the evolution of predictive models from single clinical indicators to multidimensional integration. Meanwhile, artificial intelligence approaches, including machine learning, deep learning, and federated learning, can effectively mine complex information from electronic health records and have demonstrated considerable promise in improving predictive accuracy, enabling dynamic risk assessment, and supporting clinical decision-making. Nevertheless, most current models lack large-scale, multicenter prospective validation, and their interpretability, generalizability, and clinical utility require further clarification. This review summarizes the current state of research on invasive candidiasis prediction models in critically ill patients, covering traditional clinical predictive models, biomarker integration strategies, and artificial intelligence-based models, and discusses future research directions to facilitate early identification and precision diagnosis and treatment of invasive candidiasis in critically ill settings.