Clinical Features of Covid-19 in Barcelona City

In Spain, the first positive case of SARS-Cov-2 was diagnosed on 31 January 2020. As of 7 May 2020, there have been 221,447 cases, the most in any European Union country. This study aimed to describe the clinical, biological and radiological manifestations, the evolution, treatments and mortality rate of patients with COVID-19 infection in the population of Barcelona city.https://deepblue.lib.umich.edu/bitstream/2027.42/155329/1/FINAL_Manuscript COVID19 Ann Fam Med.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155329/2/FINAL_Figure (1) Ann-Fam-Med.pdfDescription of FINAL_Manuscript COVID19 Ann Fam Med.pdf : Main ArticleDescription of FINAL_Figure (1) Ann-Fam-Med.pdf : Figure

Seguimiento de una cohorte de atención domiciliaria

ObjetivoBuscar oportunidades de mejora mediante la evaluación de la atención domiciliaria que ofrecen los equipos de atención primaria en nuestro entorno a las personas > 65 años de edad con enfermedades crónicas. Identificar cuáles son las variables del paciente y del servicio que recibe que se asocian con el deterioro funcional y cognitivo, ingreso en una residencia geriátrica, visita a urgencias, ingreso hospitalario o muerte.DiseñoEstudio analítico de seguimiento de una cohorte durante 3 años.EmplazamientoEquipos de atención primaria de Cataluña.ParticipantesEn total, 1.300 pacientes > 65 años con enfermedades crónicas incluidos en el Programa de Atención Domiciliaria.Mediciones principalesSe recoge anualmente su estado de salud (Charlson, Barthel, Pfeiffer, Braden y Gijón), datos sobre el cuidador (Zarit), atenciones recibidas (social y sanitaria), salud subjetiva (SF-12), visitas a urgencias, ingresos temporales y el resultado final: muerte, ingreso en residencies geriátricas u hospital. Los análisis principales se basarán en regresiones logísticas y una análisis de supervivencia.DiscusiónEl estudio permitirá identificar las características del paciente que tengan valor pronóstico, así como conocer las prácticas de atención social y sanitaria que se asocian con una mejor supervivencia y un menor consumo de recursos sociosanitarios.ObjectivesTo evaluate home care by primary care teams for people over 65 years old with chronic conditions, in order to identify improvement opportunities.To identify patient and care variables associated with cognitive and functional impairment, nursing home admission, attendance at casualty units, hospital admission and death.DesignAnalytic study of the follow-up of a cohort for 3 years.SettingPrimary health care teams in Catalonia, Spain.PatientsOne thousand three hundred patients over 65 with chronic pathologies and cared for by home care programmes in Catalonia.Main measurementsThe following will be recorded annually: health status (Charlson, Barthel, Pfeiffer, Braden, and Gijón), data on the carer (Zarit), care received (social and health), self-perception of health (SF-12), Casualty attendance, short-term admissions and the final results, i.e. death or definitive admission to a nursing home or hospital. The statistical analyses will be based on logistic regression and a survival analysis.ConclusionsThe study should reveal patient characteristics with prognostic value, as well as identify the social and health factors related to better survival and lower consumption of health and social resources

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts