From Species Identification to Empirical Therapy: A Machine Learning and Rule-Based Decision Support Framework for Antifungal Resistance Prediction in ICU Candida Infections
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Le résumé fourni par la source
Objectives: When a Candida species is identified in an ICU patient, susceptibility results are typically available in 24–72 h. In this study, we built a machine learning model using four variables available at identification to estimate resistance probability in real time. Methods: We analysed 747 fungal isolates from 725 ICU patients (January 2021–March 2026). We trained and compared a Random Forest and a Logistic Regression model, evaluating both with temporal cross-validation, permutation feature importance, three-category (S/I/R) prediction, and calibration analysis. Results: Multidrug resistance doubled from 24.5% (2021) to 51.1% (2025), and Candida auris grew eight-fold in three years. Random Forest reached AUC 0.885 on the held-out test set and 0.848 on prospective 2024–2025 data (Brier score 0.093). Species identity and drug choice together explained 87% of predictive signal. Local C. albicans fluconazole resistance (~16%) far exceeded the ECMM European figure of 0%, and C. krusei was four times more prevalent than the continental average. Conclusions: A four-variable model may provide calibrated resistance estimates during the critical gap before susceptibility results return, though performance reflects predominantly deterministic species–drug patterns rather than complex learned biology. Overall performance was comparable to a rule-based lookup table, confirming that the majority of predictive signal derives from established species–drug susceptibility patterns. Meaningful added value is limited to temporal trend tracking and improved prediction where resistance is acquired rather than intrinsic (C. albicans, C. tropicalis hard-subset AUC 0.929 vs. rule-based 0.899). The model complements a local antifungal testing; it does not replace one.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- From Species Identification to Empirical Therapy: A Machine Learning and Rule-Based Decision Support Framework for Antifungal Resistance Prediction in ICU Candida Infections
- Date Crossref
- 15/06/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Carol Davila University of Medicine and Pharmacy Department of Hygiene and Nutrition pays non établi dans la noticeUniversité ou école supérieure
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Institutul de Pneumoftiziologie "Marius Nasta" pays non établi dans la noticeÉtablissement de santé
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University of Bucharest pays non établi dans la noticeUniversité ou école supérieure
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Clinical Laboratory of Medical Microbiology pays non établi dans la noticeStructure de recherche
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Faculty of Nursing Department of Microbiology pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Medicine Cardiothoracic Department pays non établi dans la noticeUniversité ou école supérieure
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Marius Nasta Institute of Pneumology Pneumology Department pays non établi dans la noticeStructure de recherche
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Faculty of Biology Department of Botany and Microbiology pays non établi dans la noticeUniversité ou école supérieure
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National Reference Laboratory of Tuberculosis pays non établi dans la noticeStructure de recherche
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National Institute of Public Health pays non établi dans la noticeStructure de recherche
Department of Hygiene and Nutrition — Carol Davila University of Medicine and Pharmacy, Institutul de Pneumoftiziologie "Marius Nasta" et University of Bucharest, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.