ArticleFrontiers in microbiology2023
A scoring system based on novel biomarkers and clinical risk factors to predict invasive candidiasis in immunocompetent critically ill patients.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- Development and Validation of a Machine Learning-Based Nomogram for Predicting Severe Pneumocystis jirovecii Pneumonia in Kidney Transplant Recipients.Annals of transplantation · 2026Article
- A multimarker panel for diagnosis and prognosis prediction of fungal infections.Clinical and experimental medicine · 2026Article
- Review on the management of intra-abdominal candidiasis.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2025Review
- Early-Onset Candidemia in Adult Intensive Care Units.Diagnostics (Basel, Switzerland) · 2025Review
- Establishment and Validation of a Machine-Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra-Abdominal Candidiasis in Septic Patients.Clinical and translational science · 2025Article
- Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Delayed diagnosis further increases the mortality of invasive candidiasis (IC) in intensive care unit (ICU) patients. This study aimed to develop and validate a score based on novel serological biomarkers and clinical risk factors for predicting IC in immunocompetent ICU patients. Methods: We retrospectively collected clinical data and novel serological markers on admission to ICU. Multivariate logistic regression was used to identify the risk factors associated with IC, which were adopted to establish a scoring system. Results: Patients with IC had a higher C-reactive protein-to-albumin ratio (CAR) and neutrophil-to-lymphocyte ratio (NLR) and lower prognostic nutritional index than those without IC. The NLR, CAR, sepsis, total parenteral nutrition, 1,3-β-D-glucan (BDG)-positivity, and Sequential Organ Failure Assessment score were identified as independent risk factors for IC by multivariate logistic regression analysis and entered into the final scoring system. The area under receiver operating characteristic curve of the score were 0.883 and 0.892, respectively, in the development and validation cohort, higher than Candida score (0.883 vs.0.730, Conclusion: We established a parsimonious score based on NLR, CAR, BDG-positivity, and clinical risk factors, which can accurately identify IC in ICU patients to give treatment on time and reduce mortality.
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