3 results on '"Antonino Cannistrà"'
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2. Cluster analysis identifies patients at risk of catheter-associated urinary tract infections in intensive care units: findings from the SPIN-UTI Network
- Author
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Francesca Moretti, Giovanni Battista Orsi, Antonella Agodi, Marina Milazzo, Riccardo Pagliarulo, Cristina Arrigoni, Raffaele Squeri, Anna Maria Longhitano, Ida Mura, MC La Rosa, Emanuela Bissolo, Salvatore Coniglio, Paola Piotti, Salesia Fenaroli, Elena Righi, Cesare Vittori, Giovanni Gallo, Massimo Girardis, Alberto Rigo, Marcello Pasculli, Franco Marinangeli, Leila Fabiani, Aida Bianco, Ennio Sicoli, Marinella Astuto, Maria Pavia, Ignazio Dei, Paolo Marco Riela, Sergio Pintaudi, Giuseppe Manta, Giacomo Castiglione, Marcello Mario D’Errico, Massimo Minerva, Stefano Tardivo, Patrizia Laurenti, Patrizia Bellocchi, Marco Brusaferro, Romano Tetamo, Abele Donati, Albino Borracino, Pierangelo Sarchi, Luca Arnoldo, R Magnano San Lio, Giorgio Scrofani, Antonino Cannistrà, Maria Carmela Riggio, Antonino Di Benedetto, Salvatore Tribastoni, Maria Concetta Monea, Maria Teresa Montagna, Martina Barchitta, A R Mattaliano, Patrizia Farruggia, Irene Pandiani, Paolo Stefanini, Franco Ingala, Silvio Brusaferro, Andrea Maugeri, C La Mastra, Rosario Massimo Di Bartolo, Alberto Carli, Giuliana Favara, Barchitta, M., Maugeri, A., Favara, G., Riela, P. M., La Mastra, C., La Rosa, M. C., Magnano San Lio, R., Gallo, G., Mura, I., Agodi, A., Salesia, F., Ennio, S., Montagna, M. T., Squeri, R., Di Bartolo, R. M., Salvatore, T., Mattaliano, A. R., Bellocchi, P., Castiglione, G., Astuto, M., Longhitano, A. M., Monea, M. C., Scrofani, G., Di Benedetto, A., Carmela, R. M., Manta, G., Tetamo, R., Dei, I., Pandiani, I., Antonino, C., Piotti, P., Girardis, M., Righi, E., Pierangelo, S., Arnoldo, L., Brusaferro, S., Coniglio, S., Albino, B., Pintaudi, S., Minerva, M., Milazzo, M., Bissolo, E., Rigo, A., Fabiani, L., Marinangeli, F., Stefanini, P., D'Errico, M. M., Donati, A., Tardivo, S., Moretti, F., Carli, A., Pagliarulo, R., Bianco, A., Pavia, M., Pasculli, M., Vittori, C., Orsi, G. B., Arrigoni, C., Laurenti, P., Ingala, F., and Farruggia, P.
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Microbiology (medical) ,medicine.medical_specialty ,Catheters ,Urinary system ,medicine.medical_treatment ,Catheter-associated urinary tract infection ,030501 epidemiology ,Urinary catheterization ,law.invention ,03 medical and health sciences ,Cluster analysis ,Interquartile range ,law ,Internal medicine ,Intensive care ,Sepsis ,Intensive care unit ,Risk factor ,Cluster Analysis ,Humans ,Intensive Care Units ,Italy ,Catheter-Related Infections ,Cross Infection ,Urinary Tract Infections ,medicine ,Cluster analysi ,Settore MED/42 - IGIENE GENERALE E APPLICATA ,0303 health sciences ,030306 microbiology ,business.industry ,Incidence (epidemiology) ,General Medicine ,Catheter ,Infectious Diseases ,0305 other medical science ,business - Abstract
Background: Although preventive strategies have been proposed against catheter-associated urinary tract infections (CAUTIs) in intensive care units (ICUs), more efforts are needed to control the incidence rate. Aim: To distinguish patients according to their characteristics at ICU admission, and to identify clusters of patients at higher risk for CAUTIs. Methods: A two-step cluster analysis was conducted on 9656 patients from the Italian Nosocomial Infections Surveillance in Intensive Care Units project. Findings: Three clusters of patients were identified. Type of admission, patient origin and administration of antibiotics had the greatest weight on the clustering model. Cluster 1 comprised more patients with a medical type of ICU admission who came from the community. Cluster 2 comprised patients who were more likely to come from other wards/hospitals, and to report administration of antibiotics 48 h before or after ICU admission. Cluster 3 was similar to Cluster 2 but was characterized by a lower percentage of patients with administration of antibiotics 48 h before or after ICU admission. Patients in Clusters 1 and 2 had a longer duration of urinary catheterization [median 7 days, interquartile range (IQR) 12 days for Cluster 1; median 7 days, IQR 11 days for Cluster 2] than patients in Cluster 3 (median 6 days, IQR 8 days; P
- Published
- 2021
3. A machine learning approach to predict healthcare-associated infections at intensive care unit admission: findings from the SPIN-UTI project
- Author
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Barchitta, Martina, Maugeri, Andrea, Favara, Giuliana, Riela, Paolo Marco, Gallo, Giovanni, Mura, Ida, Agodi, Antonella, Paola, Murgia, Maria Dolores Masia, Silvio, Brusaferro, Daniele, Celotto, Luca, Arnoldo, Emanuela, Bissolo, Alberto, Rigo, Stefano, Tardivo, Francesca, Moretti, Alberto, Carli, Diana, Pascu, Lorella, Tessari, Mara Olga Bernasconi, Marco, Brusaferro, Federico, Pappalardo, Francesco, Auxilia, Salesia, Fenaroli, Cesira, Pasquarella, Ennio, Sicoli, Maria Teresa Montagna, Giovanni, Egitto, Raffaele, Squeri, Salvatore, Tribastoni, Alessandro, Pulvirenti, Sebastiano, Catalano, Pietro, Battaglia, Patrizia, Bellocchi, Giacomo, Castiglione, Anna Rita Mattaliano, Marinella Astuto Marinella, LA CAMERA, Giuseppa, Anna Maria Longhitano, Giorgio, Scrofani, Maria Concetta Monea, Marina, Milazzo, Antonino, Giarratano, Giuseppe, Calamusa, Maria Valeria Torregrossa, Antonino Di Benedetto, Giuseppa Maria Gisella Rizzo, Giuseppe, Manta, Romano, Tetamo, Rosa, Mancuso, Laura Maria Mella, Ignazio, Dei, Irene, Pandiani, Antonino, Cannistrà, Paola, Piotti, Massimo, Girardis, Elena, Righi, Alberto, Barbieri, Patricia, Crollari, Albino, Borracino, Salvatore, Coniglio, Rosaria, Palermo, Sergio, Pintaudi, Daniela Di Stefano, Antonina, Romeo, Giovanna, Sticca, Massimo, Minerva, Leila, Fabiani, Alessandra, Gentile, Paolo, Stefanini, Marcello Mario D'Errico, Abele, Donati, Santa De Remigis, Federica, Venturoni, Manuela, Antoci, Riccardo, Pagliarulo, Aida, Bianco, Maria, Pavia, Marcello, Pasculli, Cesare, Vittori, Giovanni Battista Orsi, Cristina, Arrigoni, Maria Patrizia Olori, Massimo, Antonelli, Patrizia, Laurenti, Franco, Ingala, Carmela, Conte, Salvatore, Russo, Laura, Condorelli, Patrizia, Farruggia, Cristina Maria Luisa, and Italia, Galassi
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Microbiology (medical) ,medicine.medical_specialty ,Psychological intervention ,030501 epidemiology ,Machine learning ,computer.software_genre ,intensive care unit ,law.invention ,Machine Learning ,03 medical and health sciences ,risk prediction ,law ,Intensive care ,medicine ,Infection control ,Humans ,Hospital Mortality ,Simplified Acute Physiology Score ,healthcare-associated infections ,machine learning ,0303 health sciences ,Cross Infection ,Receiver operating characteristic ,030306 microbiology ,business.industry ,Public health ,General Medicine ,Intensive care unit ,Intensive Care Units ,Infectious Diseases ,ROC Curve ,SAPS II ,Artificial intelligence ,0305 other medical science ,business ,computer ,Delivery of Health Care - Abstract
Identifying patients at higher risk of healthcare-associated infections (HAIs) in intensive care units (ICUs) represents a major challenge for public health. Machine learning could improve patient risk stratification and lead to targeted infection prevention and control interventions.To evaluate the performance of the Simplified Acute Physiology Score (SAPS) II for HAI risk prediction in ICUs, using both traditional statistical and machine learning approaches.Data for 7827 patients from the 'Italian Nosocomial Infections Surveillance in Intensive Care Units' project were used in this study. The Support Vector Machines (SVM) algorithm was applied to classify patients according to sex, patient origin, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II at admission, presence of invasive devices, trauma, impaired immunity, and antibiotic therapy in 48 h preceding ICU admission.The performance of SAPS II for predicting HAI risk provides a receiver operating characteristic curve with an area under the curve of 0.612 (P0.001) and accuracy of 56%. Considering SAPS II along with other characteristics at ICU admission, the SVM classifier was found to have accuracy of 88% and an AUC of 0.90 (P0.001) for the test set. The predictive ability was lower when considering the same SVM model but with the SAPS II variable removed (accuracy 78%, AUC 0.66).This study suggested that the SVM model is a useful tool for early prediction of patients at higher risk of HAIs at ICU admission.
- Published
- 2020
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