Carlo Tascini1, Emanuela Sozio2, Laura Corte3,4, Francesco Sbrana5, Claudio Scarparo6, Andrea Ripoli4, Giacomo Bertolino7, Maria Merelli8, Enrico Tagliaferri9, Antonio Corcione10, Matteo Bassetti8, Gianluigi Cardinali3,4, Francesco Menichetti9. 1. a First Division of Infectious Diseases , Cotugno Hospital, Azienda Ospedaliera dei Colli , Napoli , Italy. 2. b Unit of Emergency Medicine , Nuovo Santa Chiara University Hospital, Azienda Ospedaliera Universitaria Pisana , Pisa , Italy. 3. c Department of Pharmaceutical Sciences-Microbiology , University of Perugia , Perugia , Italy. 4. d CEMIN (Centre of Excellence on Nanostructured Innovative Materials), Department of Chemistry, Biology and Biotechnology , University of Perugia , Perugia , Italy. 5. e Fondazione Toscana Gabriele Monasterio , Pisa , Italy. 6. f Unit of Microbiology , Santa Maria Misericordia University Hospital , Udine , Italy. 7. g Department of Pharmaceutical Sciences-Medicine management , Azienda Ospedaliera Universitaria Pisana , Pisa , Italy. 8. h Division of Infectious Diseases , Santa Maria Misericordia University Hospital , Udine , Italy. 9. i Infectious Diseases Clinic , Nuovo Santa Chiara University Hospital, Azienda Ospedaliera Universitaria Pisana , Pisa , Italy. 10. j Department of Intensive Care , Monaldi Hospital, Azienda Ospedaliera dei Colli , Napoli , Italy.
Abstract
BACKGROUND: Evaluation of the role on patient mortality exerted by biofilm forming (BF) Candida strains, by using predictive clinical data. METHODS: Eighty-nine strains isolated from Candida bloodstream infection, occurring in two Italian University Hospitals, were employed in this study. A random forest (RF) model was built with a procedure of iterative selection of the risk factors potentially able to predict the probability of death. The similarity between patient conditions and Bayesian clustering was calculated in order to evaluate the role of predictors in the stratification of the death risk. RESULTS: Three different groups of patients with different probability of death were obtained with a RF approach: Group 1 (mortality in 33.3% of cases), Group 2 (death in 50% of cases), and Group 3 (mortality in 76.9% of cases). The comparison between these three groups showed that BF correlated well with increased mortality in patients, admitted for medical diagnosis, with high APACHE II score and treated with azoles. Early treatment within 24 h between candidemia diagnosis and the beginning of antifungal therapy was associated with the lowest of BF rate and mortality. CONCLUSIONS: BF by Candida spp. seems to be clinically associated with increased mortality especially in medical patients with higher Apache II score or treated with azoles.
BACKGROUND: Evaluation of the role on patient mortality exerted by biofilm forming (BF) Candida strains, by using predictive clinical data. METHODS: Eighty-nine strains isolated from Candida bloodstream infection, occurring in two Italian University Hospitals, were employed in this study. A random forest (RF) model was built with a procedure of iterative selection of the risk factors potentially able to predict the probability of death. The similarity between patient conditions and Bayesian clustering was calculated in order to evaluate the role of predictors in the stratification of the death risk. RESULTS: Three different groups of patients with different probability of death were obtained with a RF approach: Group 1 (mortality in 33.3% of cases), Group 2 (death in 50% of cases), and Group 3 (mortality in 76.9% of cases). The comparison between these three groups showed that BF correlated well with increased mortality in patients, admitted for medical diagnosis, with high APACHE II score and treated with azoles. Early treatment within 24 h between candidemia diagnosis and the beginning of antifungal therapy was associated with the lowest of BF rate and mortality. CONCLUSIONS:BF by Candida spp. seems to be clinically associated with increased mortality especially in medical patients with higher Apache II score or treated with azoles.
Authors: Dolly K Khona; Sashwati Roy; Subhadip Ghatak; Kaixiang Huang; Gargi Jagdale; Lane A Baker; Chandan K Sen Journal: Bioelectrochemistry Date: 2021-08-04 Impact factor: 5.373
Authors: Siang Fei Yeoh; Tae Jin Lee; Ka Lip Chew; Stephen Lin; Dennis Yeo; Sajita Setia Journal: Infect Drug Resist Date: 2018-05-30 Impact factor: 4.003