EL AUMENTO DE LA INDEPENDENCIA DE LOS PACIENTES EN EL HOSPITAL

Introduction: Increasing the level of our patients’ independence is a constant premise in the execution of our care and also has served as the basis for some of the theories that have given form to our conceptual framework. The target of our work is to determine how the patients' level of independence changes after a period of hospital stay. Methodology: Descriptive and analytic study. The reference population is the patients entered in the Hospital of Montilla, Córdoba included in the assistance processes of the Medical or Orthopedic Departments. Results: We made a hypothesis contrast to check if there were statistically significant differences among the Barthel scale at hospital check in and the Barthel scale at hospital check out. We used the parametric test of the t of Student for paired groups. The level of independence increases as much in the patients included in the assistance processes of Medical Department (5.93 IC 95 %: 3.64-8.22; pIntroducción: Aumentar el nivel de independencia de nuestros pacientes, es una premisa constante en la ejecución de nuestros cuidados y además ha servido de fundamento para algunas de las teorías que han dado forma a nuestro marco conceptual. El objetivo de nuestro trabajo es determinar como varía el nivel de independencia de los pacientes tras un período de ingreso hospitalario. Metodología: Estudio de tipo descriptivo y analítico. La población de referencia son los pacientes ingresados en el Hospital de Montilla (Córdoba) incluídos en los procesos asistenciales de tipo Médico o Traumatológico. Resultados: realizamos contraste de hipótesis para comprobar si hay diferencias estadísticamente significativas entre el índice de Barthel al ingreso y el índice de Barthel al alta. Empleamos la prueba paramétrica de la t de Student para grupos apareados. El nivel de independencia aumenta tanto en los pacientes incluídos en los procesos asistenciales de tipo Médico (5,93 IC 95%: 3,64-8,22;

Biopsychosocial factors related to the length of hospital stay in older people

This study aimed to know what variables influence increased length of hospital stay. A descriptive, cross-sectional study was conducted through an integrated geriatric assessment of 81 people over 65 years of age, admitted to a tertiary acute care hospital. Data were collected through the Pfeiffer Scale, Barthel Index, Goldberg Questionnaire, Family APGAR and Gijón Scale. The length of hospital stay increased in people over 80 years, people living alone or in a retirement home, patients with great physical dependence and those with a risk or problem of social exclusion. The most influential variable for longer hospitalization was cognitive impairment (pEste estudio tuvo como objetivo conocer qué variables influyen en el aumento de la duración de la estancia hospitalaria. Se trata de un estudio descriptivo transversal en el que se realizó una Valoración Geriátrica Integral a 81 personas mayores de 65 años de edad que ingresaron en un hospital de agudos de tercer nivel. Para ello, los datos fueron recogidos por medio de la Escala de Pfeiffer, el Índice de Barthel, el Cuestionario de Goldberg, el APGAR familiar y la Escala de Gijón. Se observó un aumento de la duración de la estancia hospitalaria entre los mayores de 80 años, las personas que vivían solas o en una residencia geriátrica, los pacientes que presentaban gran dependencia física y también, entre quienes tenían un riesgo o problema de exclusión social. La variable que más influyó en la mayor duración de la hospitalización fue el deterioro cognitivo (pEste estudo teve como objetivo conhecer quais as variáveis que influenciam o aumento do tempo de internação hospitalar. Trata-se de estudo descritivo e transversal, conduzido mediante ampla avaliação geriátrica de 81 pessoas com mais de 65 anos, internadas em hospital terciário de cuidados agudos. Os dados foram coletados através da Escala Pfeiffer, Índice de Barthel, Questionário de Goldberg, Apgar da Família e Escala de Gijón. Observou-se aumento no tempo de internação entre pessoas com mais de 80 anos, pessoas que vivem sozinhas ou em lar de idosos, pacientes que tinham grande dependência física, e entre aqueles com algum risco ou problema de exclusão social. A variável mais influente, para a maior duração da hospitalização, foi a deterioração cognitiva (p<0,05), em comparaç��o à maior colaboração do paciente sem essa condição ou ao seu desejo de superar a fase aguda da patologia que levou à internação hospitalar

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)