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Background: antimicrobial resistance is recognized as one of the greatest threats to human health worldwide. Infections caused by multidrug-resistant bacteria are associated with higher incidences of mortality, morbidity, prolonged hospital stay and increase of costs. Surveillance of alert organism/conditions and bacterial resistance to antimicrobials is a systematic and dynamic system of data collection that analyses and monitors trends of bacterial resistance. This study was conducted to detect antimicrobial susceptibility patterns in order to inform treatment choices and generate hospital-wide baseline data.Methods: the Local Healtcare Oganization of Rovigo has started a program of surveillance on antimicrobial resistance in hospital and community. In this work some results of the surveillance of microorganism isolated and related antimicrobial resistance are reported, collected in the period 2009-2010 in Rovigo and Trecenta Hospitals, Territorial Nursing Homes (TNH) and community from patients’ blood, urine and respiratory samples.Results: data show a significant difference in the level of antibiotic resistance between the two Hospitals. High rates of extended-spectrum β-lactamase (ESBL)-producing organisms are detected and carbapenems are the only reliable agents for the treatment of many infections in the Hospital of Trecenta and TNH.Conclusions: because ESBL producing bacteria are emerging pathogens in the community, the rational use of available antibiotics or the appropriate antimicrobial prescribing are imperative. Local surveillance is a powerful tool to detect and monitor hospital and community infections and provides information useful as a guide to medical practice, including therapeutics and disease-control activities.

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

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.