Predictive factors for multidrug-resistant gram-negative bacteria among hospitalised patients with complicated urinary tract infections

Background: Patients with complicated urinary tract infections (cUTIs) frequently receive broad-spectrum antibiotics. We aimed to determine the prevalence and predictive factors of multidrug-resistant gram-negative bacteria in patients with cUTI. Methods: This is a multicenter, retrospective cohort study in south and eastern Europe, Turkey and Israel including consecutive patients with cUTIs hospitalised between January 2013 and December 2014. Multidrug-resistance was defined as non-susceptibility to at least one agent in three or more antimicrobial categories. A mixed-effects logistic regression model was used to determine predictive factors of multidrug-resistant gram-negative bacteria cUTI. Results: From 948 patients and 1074 microbiological isolates, Escherichia coli was the most frequent microorganism (559/1074), showing a 14.5% multidrug-resistance rate. Klebsiella pneumoniae was second (168/1074) and exhibited the highest multidrug-resistance rate (54.2%), followed by Pseudomonas aeruginosa (97/1074) with a 38.1% multidrug-resistance rate. Predictors of multidrug-resistant gram-negative bacteria were male gender (odds ratio [OR], 1.66; 95% confidence interval [CI], 1.20-2.29), acquisition of cUTI in a medical care facility (OR, 2.59; 95%CI, 1.80-3.71), presence of indwelling urinary catheter (OR, 1.44; 95%CI, 0.99-2.10), having had urinary tract infection within the previous year (OR, 1.89; 95%CI, 1.28-2.79) and antibiotic treatment within the previous 30 days (OR, 1.68; 95%CI, 1.13-2.50). Conclusions: The current high rate of multidrug-resistant gram-negative bacteria infections among hospitalised patients with cUTIs in the studied area is alarming. Our predictive model could be useful to avoid inappropriate antibiotic treatment and implement antibiotic stewardship policies that enhance the use of carbapenem-sparing regimens in patients at low risk of multidrug-resistance

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4th Marine Imaging Workshop, 3-6 October 2022, Brest, FranceOur understanding of marine ecosystem functioning and processes relies on adequate spatio-temporal multiparametric monitoring procedures. Over the next 3 years, the Project PLOME (Platforms for Long-lasting Observation of Marine Ecosystems) will implement a spatially adaptive and autonomous network of easy-to-use benthic landers with dockable Autonomous Underwater Vehicles (AUVs)ñ This network will be used to intelligently video-monitor and map marine ecosystems and their environment from coastal to deep-sea areas. All platforms will be connected via acoustic or optical communication and will operate over periods of weeks to months with real-time supervision. Stations will provide continuous and intensive temporal observations, while dockable AUVs (with battery recharge and data downloading capability) will provide intensive measurements at various spatial scales, using intelligent and adaptive trajectories to explore surrounding areas. Biological, geochemical and oceanographic data will be generated by an array of sensors including acoustic receivers and cameras. Images will be processed in real-time for species classification and tracking, using advanced data analysis and Deep Learning techniques. Metadata will be communicated between landers and AUVs and transmitted opportunistically whenever an Unmanned Surface Vehicle (USV) connects the platform via aerial communications (i.e. GSM and satellite communications, depending on form distance to shore). The unattended operation will also be possible with an innovation of pop-up buoys that will allow data transfer to the surface from landers and UAVs to be relayed once the pop-up buoys reach the surface. Complex ecological indicators for ecosystem management will be computed from the collected data, by applying advanced computer vision techniques to classify, count and size individuals in video images and to generate multimodal maps of the seabed. A pipeline for automated data treatment will be tailored for multiparametric analyses to derive cause-effect relationships between biological variables and the physical habitatsPeer reviewe

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Resumen del autor en catalánAnte la inminente llegada de la nueva moneda única europea y la necesidad de incluirla en el currículum, un grupo de seis maestros de l'Alt Urgell-Cerdanya decidieron elaborar un material didáctico para poder trabajar el tema en el aula. Es por eso que durante el curso 1999-2000 se creó en el CRP de l'Alt Urgell, en el marco del Plan de Formación de Zona, un seminario llamado 'Tratamiento del euro'.CataluñaES

Vocabulari de botànica: català, castellà, francès, anglès

Vocabulari quadrilingüe català-castellà-francès-anglès que conté 1571 termes de botànica

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10 carteles diseñados para la exposición -- Contenido: 1. Font i Quer, organitzador de la botànica catalana al segle XX ; 2. L’Institut Botànic i el Jardí Botànic de Barcelona ; 3. Font i Quer i la divulgació científica ; 4. Font i Quer i la docència ; 5. Font i Quer, l’etnobotànica i la farmacognòsia ; 6. Font i Quer i altres branques de la ciència 7. Font i Quer, la lingüística i la terminologia ; 8. Font i Quer i l’estudi de les criptògames ; 9. Les campanyes d’exploració de Font i Quer ; 10. Font i Quer, un nou paradigma de la botànica catalanaExposición organizada dentro de los actos de conmemoración del 50º aniversario de la muerte del botánico catalán Pius Font i Quer. Se celebró en el vestíbulo del Edificio Histórico de la Universidad de Barcelona del 15 de octubre al 5 de noviembre de 2014.Peer reviewe

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This report describes a didactic project on Computer Graphics Laboratory courses. The aim of the project is to provide common software tools for the design and implementation of practical works to the students and the teachers of Computer Graphics courses. These software tools may be used in introductory courses as well as more specialized ones and in Master degree projects. The use of this software unifies and eases the process of knowledge acquisition. In addition, it makes it possible to re-use the developments realized in the different courses.Preprin

Predictive factors for multidrug-resistant gram-negative bacteria among hospitalised patients with complicated urinary tract infections

Abstract Background Patients with complicated urinary tract infections (cUTIs) frequently receive broad-spectrum antibiotics. We aimed to determine the prevalence and predictive factors of multidrug-resistant gram-negative bacteria in patients with cUTI. Methods This is a multicenter, retrospective cohort study in south and eastern Europe, Turkey and Israel including consecutive patients with cUTIs hospitalised between January 2013 and December 2014. Multidrug-resistance was defined as non-susceptibility to at least one agent in three or more antimicrobial categories. A mixed-effects logistic regression model was used to determine predictive factors of multidrug-resistant gram-negative bacteria cUTI. Results From 948 patients and 1074 microbiological isolates, Escherichia coli was the most frequent microorganism (559/1074), showing a 14.5% multidrug-resistance rate. Klebsiella pneumoniae was second (168/1074) and exhibited the highest multidrug-resistance rate (54.2%), followed by Pseudomonas aeruginosa (97/1074) with a 38.1% multidrug-resistance rate. Predictors of multidrug-resistant gram-negative bacteria were male gender (odds ratio [OR], 1.66; 95% confidence interval [CI], 1.20–2.29), acquisition of cUTI in a medical care facility (OR, 2.59; 95%CI, 1.80–3.71), presence of indwelling urinary catheter (OR, 1.44; 95%CI, 0.99–2.10), having had urinary tract infection within the previous year (OR, 1.89; 95%CI, 1.28–2.79) and antibiotic treatment within the previous 30 days (OR, 1.68; 95%CI, 1.13–2.50). Conclusions The current high rate of multidrug-resistant gram-negative bacteria infections among hospitalised patients with cUTIs in the studied area is alarming. Our predictive model could be useful to avoid inappropriate antibiotic treatment and implement antibiotic stewardship policies that enhance the use of carbapenem-sparing regimens in patients at low risk of multidrug-resistance

Predictive factors for multidrug-resistant gram-negative bacteria among hospitalised patients with complicated urinary tract infections

Background: Patients with complicated urinary tract infections (cUTIs) frequently receive broad-spectrum antibiotics. We aimed to determine the prevalence and predictive factors of multidrug-resistant gram-negative bacteria in patients with cUTI. Methods: This is a multicenter, retrospective cohort study in south and eastern Europe, Turkey and Israel including consecutive patients with cUTIs hospitalised between January 2013 and December 2014. Multidrug-resistance was defined as non-susceptibility to at least one agent in three or more antimicrobial categories. A mixed-effects logistic regression model was used to determine predictive factors of multidrug-resistant gram-negative bacteria cUTI. Results: From 948 patients and 1074 microbiological isolates, Escherichia coli was the most frequent microorganism (559/1074), showing a 14.5% multidrug-resistance rate. Klebsiella pneumoniae was second (168/1074) and exhibited the highest multidrug-resistance rate (54.2%), followed by Pseudomonas aeruginosa (97/1074) with a 38.1% multidrug-resistance rate. Predictors of multidrug-resistant gram-negative bacteria were male gender (odds ratio [OR], 1.66; 95% confidence interval [CI], 1.20-2.29), acquisition of cUTI in a medical care facility (OR, 2.59; 95%CI, 1.80-3.71), presence of indwelling urinary catheter (OR, 1.44; 95%CI, 0.99-2.10), having had urinary tract infection within the previous year (OR, 1.89; 95%CI, 1.28-2.79) and antibiotic treatment within the previous 30 days (OR, 1.68; 95%CI, 1.13-2.50). Conclusions: The current high rate of multidrug-resistant gram-negative bacteria infections among hospitalised patients with cUTIs in the studied area is alarming. Our predictive model could be useful to avoid inappropriate antibiotic treatment and implement antibiotic stewardship policies that enhance the use of carbapenem-sparing regimens in patients at low risk of multidrug-resistance

Predictive factors for multidrug-resistant gram-negative bacteria among hospitalised patients with complicated urinary tract infections

Background: Patients with complicated urinary tract infections (cUTIs) frequently receive broad-spectrum antibiotics. We aimed to determine the prevalence and predictive factors of multidrug-resistant gram-negative bacteria in patients with cUTI. Methods: This is a multicenter, retrospective cohort study in south and eastern Europe, Turkey and Israel including consecutive patients with cUTIs hospitalised between January 2013 and December 2014. Multidrug-resistance was defined as non-susceptibility to at least one agent in three or more antimicrobial categories. A mixed-effects logistic regression model was used to determine predictive factors of multidrug-resistant gram-negative bacteria cUTI. Results: From 948 patients and 1074 microbiological isolates, Escherichia coli was the most frequent microorganism (559/1074), showing a 14.5% multidrug-resistance rate. Klebsiella pneumoniae was second (168/1074) and exhibited the highest multidrug-resistance rate (54.2%), followed by Pseudomonas aeruginosa (97/1074) with a 38.1% multidrug-resistance rate. Predictors of multidrug-resistant gram-negative bacteria were male gender (odds ratio [OR], 1.66; 95% confidence interval [CI], 1.20-2.29), acquisition of cUTI in a medical care facility (OR, 2.59; 95%CI, 1.80-3.71), presence of indwelling urinary catheter (OR, 1.44; 95%CI, 0.99-2.10), having had urinary tract infection within the previous year (OR, 1.89; 95%CI, 1.28-2.79) and antibiotic treatment within the previous 30 days (OR, 1.68; 95%CI, 1.13-2.50). Conclusions: The current high rate of multidrug-resistant gram-negative bacteria infections among hospitalised patients with cUTIs in the studied area is alarming. Our predictive model could be useful to avoid inappropriate antibiotic treatment and implement antibiotic stewardship policies that enhance the use of carbapenem-sparing regimens in patients at low risk of multidrug-resistance