Development of Candidaemia Score (CanDi-Score) in Decision of Early Empirical Antifungal Treatment for Patients in Intensive Care Unit: The International Prospective Observational ID-IRI Study.
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Kayaaslan Bircan, Araz Halime, Zengin Hatice Yağmur, Bulut Rukiyye, Oktay Zeynep, Tasbakan Meltem, Istek Sumeyra, Kartal Elif Doyuk, Bayrak Ozlem, Kaya Ayse, Eser Fatma, Atasoy Pınar Yuruk, Kıratlı Hatice Esranur, Doruk Berfin Cirkin, Uguz Mustafa, Sarı Sema, Sivrikaya Bahar Busra, Akkoyunlu Yasemin, Altunal Lutfiye Nilsun, Albayrak Ayse, Alay Handan, Iskender Gulsen, Amani Elkholy, Onder Taylan, Ozcengiz Dilek, Turktan Mediha, Mirko Mihalj, Darazam Ilad Alavi, Shadravan Mohammad Mehdi, Sahzabi Reza Yahyavi, Yardımcı Ahmet Cem, Korkmazer Ruveyda, Senol Gunes, Kuruoglu Tuba, Temocin Fatih, Alıcı Ozlem, Sonmezoglu Meral, Esmaoglu Aliye, Ture Zeynep, Cortegiani Andrea, Akyıldız Ozay, Guner Hatice Rahmet, Erdem Hakan
Аннотация
OBJECTIVES: This international, multicentre, prospective, observational study aimed to identify risk factors for candidaemia in ICU patients and to develop an easy-to-use predictive tool, the CanDi-Score, for early initiation of empirical antifungal treatment.
PATIENTS/METHODS: All adults over 18 years hospitalised for more than 48 h in the ICUs were included in the study and followed for 30 days. Data on demographics, comorbidities, clinical severity scores including APACHE II and SOFA scores, Charlson comorbidity index (CCI), established risk factors for candidaemia and their time-dependent effects such as total parenteral nutrition (TPN), mechanical ventilation (MV), and central venous catheter (CVC) were collected. Univariate and multivariate logistic regression analyses were used to identify factors associated with candidaemia development, and fast backwards variable selection was used. To account for between-centre heterogeneity, sensitivity analyses were performed using mixed-effects logistic regression models including centre-specific random intercepts. A nomogram model was constructed using independent risk factors identified in multivariable analysis, and its predictive performance was evaluated with Area Under the Curve (AUC), sensitivity, and specificity.
RESULTS: A total of 2,704 ICU patients, including 204 with candidaemia, were enrolled. Patients with candidaemia had higher APACHE II (19 vs. 13) and CCI scores (6 vs. 5; both p < 0.001), and more frequently had chronic renal failure, haemodialysis, malignancy, recent gastrointestinal surgery, and neutropenia (all p ≤ 0.05). Patients were randomly split into development (80%) and validation (20%) cohorts. Multivariate analysis identified higher CCI, concurrent infection, neutropenia, ICU stay > 14 days, MV > 3 days, CVC duration and TPN > 14 days as independent risk factors. The risk associated with CVC increased substantially with prolonged use (OR: 0-7 days: 3.69; 8-21 days: 8.58; > 21 days: 17.21). The model demonstrated good performance in both the development and validation cohorts, with sensitivity, specificity, and AUC values of 79.6%, 76.5%, and 0.86 (95% CI: 0.83-0.88) in the development cohort, respectively, and comparable predictive performance in the validation cohort. The score model was demonstrated by a nomogram.
CONCLUSIONS: Given the high mortality of candidaemia in ICU patients, the CanDi-Score may serve as a valuable tool for early prediction and timely empirical antifungal treatment. Key predictors include CCI, presence of concurrent infections, the duration of CVC use, and TPN administration. CanDi-Score provides clinicians with a practical approach to identify high-risk patients and guides decision-making for the early initiation of antifungal treatment.