Risk Factors and Predictive Score for Bacteremic Biliary Tract Infections Due to Enterococcus faecalis and Enterococcus faecium: a Multicenter Cohort Study from the PROBAC Project

Biliary-tract bloodstream infections (BT-BSI) caused by Enterococcus faecalis and E. faecium are associated with inappropriate empirical treatment and worse outcomes compared to other etiologies. The objective of this study was to investigate the risk factors for enterococcal BT-BSI. Patients with BT-BSI from the PROBAC cohort, including consecutive patients with BSI in 26 Spanish hospitals between October 2016 and March 2017, were selected; episodes caused by E. faecalis or E. faecium and other causes were compared. Independent predictors for enterococci were identified by logistic regression, and a predictive score was developed. Eight hundred fifty episodes of BT-BSI were included; 73 (8.5%) were due to target Enterococcus spp. (48 [66%] were E. faecium and 25 [34%] E. faecalis). By multivariate analysis, the variables independently associated with Enterococcus spp. were (OR; 95% confidence interval): cholangiocarcinoma (4.48;1.32 to 15.25), hospital acquisition (3.58;2.11 to 6.07), use of carbapenems in the previous month (3.35;1.45 to 7.78), biliary prosthesis (2.19;1.24 to 3.90), and moderate or severe chronic kidney disease (1.55;1.07 to 2.26). The AUC of the model was 0.74 [95% CI0.67 to 0.80]. A score was developed, with 7, 6, 5, 4, and 2 points for these variables, respectively, with a negative predictive value of 95% for a score ? 6. A model, including cholangiocarcinoma, biliary prosthesis, hospital acquisition, previous carbapenems, and chronic kidney disease showed moderate prediction ability for enterococcal BT-BSI. Although the score will need to be validated, this information may be useful for deciding empirical therapy in biliary tract infections when bacteremia is suspected. IMPORTANCE Biliary tract infections are frequent, and a significant cause of morbidity and mortality. Bacteremia is common in these infections, particularly in the elderly and patients with cancer. Inappropriate empirical treatment has been associated with increased risk of mortality in bacteremic cholangitis, and the probability of receiving inactive empirical treatment is higher in episodes caused by enterococci. This is because many of the antimicrobial agents recommended in guidelines for biliary tract infections lack activity against these organisms. To the best of our knowledge, this is the first study analyzing the predictive factors for enterococcal BT-BSI and deriving a predictive score

Risk Factors and Predictive Score for Bacteremic Biliary Tract Infections Due to Enterococcus faecalis and Enterococcus faecium: a Multicenter Cohort Study from the PROBAC Project

Biliary-tract bloodstream infections (BT-BSI) caused by Enterococcus faecalis and E. faecium are associated with inappropriate empirical treatment and worse outcomes compared to other etiologies. The objective of this study was to investigate the risk factors for enterococcal BT-BSI. Patients with BT-BSI from the PROBAC cohort, including consecutive patients with BSI in 26 Spanish hospitals between October 2016 and March 2017, were selected; episodes caused by E. faecalis or E. faecium and other causes were compared. Independent predictors for enterococci were identified by logistic regression, and a predictive score was developed. Eight hundred fifty episodes of BT-BSI were included; 73 (8.5%) were due to target Enterococcus spp. (48 [66%] were E. faecium and 25 [34%] E. faecalis). By multivariate analysis, the variables independently associated with Enterococcus spp. were (OR; 95% confidence interval): cholangiocarcinoma (4.48;1.32 to 15.25), hospital acquisition (3.58;2.11 to 6.07), use of carbapenems in the previous month (3.35;1.45 to 7.78), biliary prosthesis (2.19;1.24 to 3.90), and moderate or severe chronic kidney disease (1.55;1.07 to 2.26). The AUC of the model was 0.74 [95% CI0.67 to 0.80]. A score was developed, with 7, 6, 5, 4, and 2 points for these variables, respectively, with a negative predictive value of 95% for a score # 6. A model, including cholangiocarcinoma, biliary prosthesis, hospital acquisition, previous carbapenems, and chronic kidney disease showed moderate prediction ability for enterococcal BT-BSI. Although the score will need to be validated, this information may be useful for deciding empirical therapy in biliary tract infections when bacteremia is suspected. IMPORTANCE Biliary tract infections are frequent, and a significant cause of morbidity and mortality. Bacteremia is common in these infections, particularly in the elderly and patients with cancer. Inappropriate empirical treatment has been associated with increased risk of mortality in bacteremic cholangitis, and the probability of receiving inactive empirical treatment is higher in episodes caused by enterococci. This is because many of the antimicrobial agents recommended in guidelines for biliary tract infections lack activity against these organisms. To the best of our knowledge, this is the first study analyzing the predictive factors for enterococcal BT-BSI and deriving a predictive score.8 página

Diarrea en pacientes con infección por el virus de la inmunodeficiencia humana

Tesis doctoral inédita leída el 20 de Febrero de 1998 en la Facultad de Medicina, Departmaento de Medicina, Universidad Autónoma de Madri

Resumen de la tarea de detección de emociones en español EmoEvalEs en IberLEF 2021

This paper presents the EmoEvalEs shared task, organized at IberLEF 2021, as part of the 37th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2021). The aim of this task is to promote the Emotion detection and Evaluation for Spanish. It consists of a fine-grained emotion classification of tweets from the EmoEvalEs corpus in one of these seven classes: anger, disgust, fear, joy, sadness, surprise, or others. In this edition, 70 teams registered, 15 submitted results and 11 presented papers describing their systems. Most teams experimented with neural networks, being Transformers the most widely used model. It should be noted that few of them also considered the features of offensiveness and event that were provided in the corpus apart from the tweet texts.Este artículo presenta la tarea EmoEvalEs, organizada en IberLEF 2021, en el marco del de la 37 edición de la Conferencia Internacional de la Sociedad Española para el Procesamiento del Lenguaje Natural. El objetivo de esta tarea es promover la Detección y Evaluación de Emociones en Español. Consiste en la clasificación de grano fino de los tweets del corpus EmoEvent en una de las siguientes siete clases: ira, asco, miedo, alegría, tristeza, sorpresa u otros. En esta edición, se registraron 70 equipos, 15 enviaron resultados y 11 presentaron art culos describiendo sus sistemas. La mayoría de los equipos experimentaron con redes neuronales, siendo Transformers el modelo más utilizado. Cabe destacar que pocos equipos consideraron también las características de ofensividad y evento que se proporcionaron en el corpus aparte de los textos de los tweets.This work has been partially supported by a grant from Fondo Social Europeo, Administration of the Junta de Andalucía (DOC 01073 and P20 00956-PAIDI 2020), Fondo Europeo de Desarrollo Regional (FEDER), LIVING-LANG project (RTI2018-094653-B-C21) and the Ministry of Science, Innovation and Universities (scholarship [FPI-PRE2019-089310]) from the Spanish Government

Colonic leishmaniasis in a patient with HIV: a case report

Background: To describe an unusual clinical presentation of visceral leishmaniasis affecting the colon. Case report: We report the case of an HIV-positive patient with visceral leishmaniasis. We describe the clinical case, the procedures performed, the treatment provided and the patient's evolution. A comparative table of previously reported similar cases is shown. Discussion: Visceral leishmaniasis with intestinal involvement is an uncommon process. Nevertheless, this possibility should be taken into consideration in the differential diagnosis of immunosuppressed patients with symptoms of diarrhea, as a favorable prognosis depends on early diagnosis and appropriate treatment

Monge: Monitor Geográfico de Enfermedades

Monge is a prototype of a geographic monitor of diseases, based on tweets. After the recovering phase of tweets, located in different Spanish cities, these tweets are processed and filtered with techniques and tools of Human Language Technologies. Tweets are filtered with three criteria: location, language (Spanish and Catalan) and bag of words of diseases (generated using synonyms of WordReference and embeddings). The processed information is presented in an interactive way allowing to predict possible epidemic outbreaks of different diseases (e.g. flu, asthma). This demo could be very useful because the Centers for Disease Control and Prevention take between 1-2 weeks from the moment the patient is diagnosed until the data is available, while with this prototype a real-time monitoring of diseases is offered.Monge es un prototipo de un monitor geográfico de enfermedades basado en tweets. Recuperando tweets localizados en distintas ciudades españolas, tanto en español como en catalán, y procesando y analizando la información con técnicas y herramientas de Tecnologías del Lenguaje Humano, permite predecir posibles brotes epidémicos de distintas enfermedades de interés general (gripe, asma, etc.). Los tweets son filtrados utilizando tres criterios: localización, idioma y bolsas de palabras de enfermedades que han sido generadas utilizando sinónimos de WordReference y embeddings. Esta demo podría ser de gran utilidad porque los Centros para el Control y la Prevención de enfermedades tardan entre 1-2 semanas desde que se diagnostica al paciente hasta que los datos están disponibles, mientras que con este prototipo se ofrece una monitorización en tiempo real.This work has been partially supported by a grant from the Ministerio de Educación Cultura y Deporte (MECD – scholarship FPU014/00983), Fondo Europeo de Desarrollo Regional (FEDER) and REDES project (TIN2015-65136-C2-1-R) from the Spanish Government

Monge: Monitor Geográfico de Enfermedades

Monge is a prototype of a geographic monitor of diseases, based on tweets. After the recovering phase of tweets, located in different Spanish cities, these tweets are processed and filtered with techniques and tools of Human Language Technologies. Tweets are filtered with three criteria: location, language (Spanish and Catalan) and bag of words of diseases (generated using synonyms of WordReference and embeddings). The processed information is presented in an interactive way allowing to predict possible epidemic outbreaks of different diseases (e.g. flu, asthma). This demo could be very useful because the Centers for Disease Control and Prevention take between 1-2 weeks from the moment the patient is diagnosed until the data is available, while with this prototype a real-time monitoring of diseases is offered.Monge es un prototipo de un monitor geográfico de enfermedades basado en tweets. Recuperando tweets localizados en distintas ciudades españolas, tanto en español como en catalán, y procesando y analizando la información con técnicas y herramientas de Tecnologías del Lenguaje Humano, permite predecir posibles brotes epidémicos de distintas enfermedades de interés general (gripe, asma, etc.). Los tweets son filtrados utilizando tres criterios: localización, idioma y bolsas de palabras de enfermedades que han sido generadas utilizando sinónimos de WordReference y embeddings. Esta demo podría ser de gran utilidad porque los Centros para el Control y la Prevención de enfermedades tardan entre 1-2 semanas desde que se diagnostica al paciente hasta que los datos están disponibles, mientras que con este prototipo se ofrece una monitorización en tiempo real.This work has been partially supported by a grant from the Ministerio de Educación Cultura y Deporte (MECD – scholarship FPU014/00983), Fondo Europeo de Desarrollo Regional (FEDER) and REDES project (TIN2015-65136-C2-1-R) from the Spanish Government