Estudio sobre la prevalencia de expresión del pd-l1 en las Células Neoplásicas en nuestros pacientes sometidos a EBUS-TBNA / Study on the prevalence of pd-l1 expression in neoplastic cells in our patients undergoing EBUS-TBNA

El Cáncer de pulmón es la primera causa mundial de muertepor Cáncer. La inmunoterapia con anticuerpos monoclonales antiPD-L1 ha supuesto un avance en su tratamiento. En nuestro estudiose ha revisado la prevalencia de este receptor en las muestrasobtenidas mediante aspiración con aguja transbronquial guiada porecobroncoscopia (EBUS-TBNA). (AU)

Lung cancer is the world's leading cause of death from cancer.Immunotherapy with anti-PD-L1 monoclonal antibodies hasrepresented an advance in its treatment. In our study, we reviewedthe prevalence of this receptor in samples obtained by ultrasoundguided transbronchial needle aspiration (EBUS-TBNA). (AU)

Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19 / Mathematical model optimized for prediction and health care planning for COVID-19

Objetivo: La pandemia de la COVID-19 ha supuesto una amenaza de colapso de los servicios hospitalarios y de unidades de cuidado intensivo (UCI), así como una reducción de la dinámica asistencial de pacientes afectados por otras patologías. El objetivo fue desarrollar un modelo matemático diseñado para optimizar las predicciones relacionadas con las necesidades de hospitalización e ingresos en UCI por la COVID-19. Diseño: Estudio prospectivo. Ámbito: Provincia de Granada (España). Pacientes: Pacientes de COVID-19 hospitalizados, ingresados en UCI, recuperados y fallecidos desde el 15 de marzo hasta el 22 de septiembre del 2020. Intervenciones: Desarrollo de un modelo matemático tipo susceptible, expuesto, infectado y recuperado (SEIR) capaz de predecir la evolución de la pandemia, considerando las medidas de salud pública establecidas. Variables de interés: Número de pacientes infectados por SARS-CoV-2, hospitalizados e ingresados en UCI por la COVID-19.Resultados: A partir de los datos registrados, hemos podido desarrollar un modelo matemático que refleja el flujo de la población entre los diferentes grupos de interés en relación con la COVID-19. Esta herramienta permite analizar diferentes escenarios basados en medidas de restricción socio-sanitarias y pronosticar el número de infectados, hospitalizados e ingresados en UCI. Conclusiones: El modelo matemático es capaz de proporcionar predicciones sobre la evolución de la COVID-19 con suficiente antelación como para poder conjugar los picos de prevalencia y de necesidades de asistencia hospitalaria y de UCI, con la aparición de ventanas temporales que posibiliten la atención de enfermos no-COVID (AU)

Objective: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. Design: Prospective study. Setting: Province of Granada (Spain). Population: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. Study variables: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. Results: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. Conclusions:The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified (AU)

Mathematical model optimized for prediction and health care planning for COVID-19.

OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.

Mathematical model optimized for prediction and health care planning for COVID-19. / Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19.

OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.