Online training in radiology during the COVID-19 pandemic.

The COVID-19 pandemic has changed the methods used for teaching radiology in medical schools, residency programs, and continuing medical education. The need to continue training in radiology in a situation requiring physical distancing has led to the massive use of online methods, and this is where internet has provided a solution to mitigate the problem. This paper aims to present a series of useful, freely accessible resources that share the #FOAMRad philosophy for online training in radiology during the COVID-19 pandemic.

Formación online en Radiología en tiempos de COVID / Online training in radiology during the COVID-19 pandemic

La pandemia por COVID-19ha alterado de forma significativa la metodología que tradicionalmente se ha empleado para la enseñanza de la Radiología en pregrado, posgrado y formación continuada. La necesidad de continuar con la formación en Radiología bajo una situación de distanciamiento físico ha provocado el uso masivo de metodología online y aquí es donde Internet se ha constituido en una solución para mitigar el problema. El objetivo de este trabajo es presentar una serie de recursos útiles de acceso gratuito que comparten la filosofía #FOAMRad para la formación online en Radiología en estos tiempos de COVID.(AU)

The COVID-19 pandemic has changed the methods used for teaching radiology in medical schools, residency programs, and continuing medical education. The need to continue training in radiology in a situation requiring physical distancing has led to the massive use of online methods, and this is where internet has provided a solution to mitigate the problem. This paper aims to present a series of useful, freely accessible resources that share the #FOAMRad philosophy for online training in radiology during the COVID-19 pandemic.(AU)

[Online training in radiology during the COVID-19 pandemic]. / Formación online en Radiología en tiempos de COVID.

The COVID-19 pandemic has changed the methods used for teaching radiology in medical schools, residency programs, and continuing medical education. The need to continue training in radiology in a situation requiring physical distancing has led to the massive use of online methods, and this is where internet has provided a solution to mitigate the problem. This paper aims to present a series of useful, freely accessible resources that share the #FOAMRad philosophy for online training in radiology during the COVID-19 pandemic.

Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray.

OBJECTIVES: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters. METHODS: All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model. RESULTS: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics. CONCLUSION: The developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.

Elaboración de modelos predictivos de la gravedad y la mortalidad en pacientes con COVID-19 que acuden al servicio de urgencias, incluida la radiografía torácica / Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray

Objetivos: Desarrollar modelos de predicción de pronóstico para pacientes con COVID-19 que acuden a urgencias, basados en la radiografía de tórax inicial (RXT), parámetros demográficos, clínicos y de laboratorio. Métodos: Se reclutaron todos los pacientes sintomáticos con COVID-19 confirmada, que ingresaron en urgencias de nuestro hospital entre el 24 de febrero y el 24 de abril de 2020. Los parámetros de la RXT, las variables clínicas y de laboratorio y los índices de hallazgos en RXT extraídos por una herramienta diagnóstica de inteligencia artificial en esta primera visita se consideraron potenciales predictores. El desenlace individual más grave definió los tres niveles de gravedad: 0) alta domiciliaria u hospitalización de 3 días o inferior, 1) hospitalización más de 3 días y 2) necesidad de cuidados intensivos o muerte. Se desarrollaron y validaron internamente modelos de predicción multivariable de gravedad y mortalidad hospitalaria. El índice de Youden se utilizó para la selección del umbral óptimo del modelo de clasificación. Resultados: Se registraron 440 pacientes (mediana de 64 años; 55,9% hombres); el 13,6% de los pacientes fueron dados de alta, el 64% estuvo hospitalizado más de 3 días, el 6,6% requirió cuidados intensivos y un 15,7% falleció. El modelo de predicción de gravedad incluyó saturación de oxígeno/fracción de oxígeno inspirado (SatO2/FiO2), edad, proteína C reactiva (PCR), linfocitos, puntuación de la extensión de la afectación pulmonar en la RXT (ExtScoreRXT), lactato deshidrogenasa (LDH), dímero D y plaquetas, con AUC-ROC=0,94 y AUC-PRC=0,88. El modelo de predicción de mortalidad incluyó edad, SatO2/FiO2, PCR, LDH, ExtScoreRXT, linfocitos y dímero D, con AUC-ROC=0,97 y AUC-PRC=0,78. La adición de índices radiológicos obtenidos por inteligencia artificial no mejoró significativamente las métricas predictivas.(AU)

Objectives: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters. Methods: All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay>3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model. Results: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC=0.94 and AUC-PRC=0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC=0.97 and AUC-PRC=0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics. Conclusion: The developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.(AU)

[Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray]. / Elaboración de modelos predictivos de la gravedad y la mortalidad en pacientes con COVID-19 que acuden al servicio de urgencias, incluida la radiografía torácica.

Objectives: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters. Methods: All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model. Results: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics. Conclusion: The developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.

Google y PubMed para médicos: ¿cómo buscar información sin perderse? / Google and PubMed for physicians: how to find information without getting lost

Buscar en Internet información médica de interés clínico como ayuda a la toma de decisiones, para el autoaprendizaje o para la elaboración de un trabajo de investigación se ha convertido actualmente en una tarea cotidiana en Radiología. Esta tarea se ve potenciada por el entorno tecnológico en el que se desenvuelve el radiólogo actualmente y que facilita sobremanera el acceso a fuentes de información en Internet desde la propia estación de trabajo. El objetivo del presente artículo es revisar aspectos fundamentales de la búsqueda de información en Internet, que faciliten la comprensión de su funcionamiento con el fin de optimizar las búsquedas. Para ello se emplean como modelos los buscadores Google y Google Académico, y la base de datos PubMed (AU)

Searching on Internet looking for clinically relevant medical information, used as a clinical decision aid tool, for self-learning or for research, is currently a common practice in Radiology. This task has been strengthened by the technological environment where radiologists work with direct access to information sources from the Workstation. The aim of this paper is to review the basic features of information searching tools in order to understand their functions and to optimize medical information searching on Internet. Google, Google Scholar and PubMed are reviewed as models for that purpose (AU)

[Google and PubMed for physicians: how to find information without getting lost]. / Google y PubMed para médicos: ¿cómo buscar información sin perderse?

Searching on Internet looking for clinically relevant medical information, used as a clinical decision aid tool, for self-learning or for research, is currently a common practice in Radiology. This task has been strengthened by the technological environment where radiologists work with direct access to information sources from the Workstation. The aim of this paper is to review the basic features of information searching tools in order to understand their functions and to optimize medical information searching on Internet. Google, Google Scholar and PubMed are reviewed as models for that purpose.

Herramientas de formación on-line en radiología / Online training tools in radiology

Los recursos on-line que ofrece Internet surgen como complementos o incluso como alternativas a los recursos empleados tradicionalmente para adquirir nuevos conocimientos. La posibilidad de acceder a estas fuentes desde cualquier lugar geográfico con acceso a la Red, y a cualquier hora del día, sin estar limitado por el número de usuarios que utilizan simultáneamente estos recursos, y la posibilidad de establecer comunicación inmediata con los autores de los contenidos, independientemente de su localización geográfica, suponen algunas de las ventajas que ofrece Internet cuando se desea aprender. El presente trabajo revisa los recursos educativos que están actualmente disponibles en Internet y que pueden ser de utilidad para la formación en Radiología. Consideramos que crear y promover este tipo de herramientas debe estar entre las líneas estratégicas de las asociaciones profesionales de radiólogos (AU)

The online resources offered on the Internet have become complements or even alternatives to traditional resources for acquiring new knowledge. Among other advantages, online training tools are accessible 24hours a day from anywhere provided there is an internet connection. Furthermore, there is no limit to the number of simultaneous users of these resources, and users can communicate easily with the authors of the content regardless of where they are located. This article reviews the educational resources that are available on the Internet that can be useful for training in radiology. We consider that creating and promoting this type of tool should form part of the strategic plans of professional associations of radiologists (AU)

[Online training tools in radiology]. / Herramientas de formación on-line en radiología.

The online resources offered on the Internet have become complements or even alternatives to traditional resources for acquiring new knowledge. Among other advantages, online training tools are accessible 24 hours a day from anywhere provided there is an internet connection. Furthermore, there is no limit to the number of simultaneous users of these resources, and users can communicate easily with the authors of the content regardless of where they are located. This article reviews the educational resources that are available on the Internet that can be useful for training in radiology. We consider that creating and promoting this type of tool should form part of the strategic plans of professional associations of radiologists.

[The imaging diagnosis of carotid body tumors]. / Diagnóstico por la imagen de los tumores del cuerpo carotídeo.

The paper deals with 2 cases of unilateral tumor of the carotid body, in men aged 46 and 51. The diagnosis, prior to surgery, was achieved through ultrasonographic and scanner studies, as well as conventional or selective angiography (digital subtraction). The semeiology displayed by these techniques, and by other possible systems of imagery, are emphasized for patients suspected of carotid glomus.