Use of non-steroidal anti-inflammatory drugs and analgesics in a cohort of hospitalized elderly patients: Results from the REPOSI study.

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Prognostic relevance of glomerular filtration rate estimation obtained through different equations in hospitalized elderly patients.

The estimated glomerular filtration rate (eGFR) is a predictor of important outcomes and its reduction has been associated with the risk of all-cause mortality in both general population and elderly patients. However while reduced renal function is common in older people, the best method for estimating GFR remains unclear, especially in an acute care setting. Most studies analyzing the accuracy of eGFR in the elderly were carried out in different heterogeneous settings. In this study, we compare the prognostic value of different formulas estimating GFR in predicting the risk of in-hospital morbidity and mortality within 3 months from discharge in elderly hospitalized patients. Data were extracted from "Registro Politerapia Società Italiana di Medicina Interna (REPOSI)". Patients with available creatinine values at hospital admission were selected and eGFR was calculated according to the different formulas: Cockcroft-Gault, Modification of Diet in Renal Disease equation, Chronic Kidney Disease Epidemiology Collaboration, Berlin Initiative Study and Full Age Spectrum. 4621 patients were included in the analysis. Among these, 4.2% and 14.2% died during hospitalization and within 3 months from discharge, respectively. eGFR > 60 ml/min/1.73 m2 at admission was associated with a very low risk of mortality during the hospital stay and within 90 days from discharge, while an eGFR < 60 ml/min/1.73 m2 was associated with unfavorable outcomes, although with a poor level of accuracy (AUC 0.60-0.66). No difference in predictive power between different equations was found. Physicians should be aware of the prognostic role of eGFR in a comprehensive assessment of elderly in-patients

Appropriateness of antiplatelet therapy for primary and secondary cardio- and cerebrovascular prevention in acutely hospitalized older people

Aims: Antiplatelet therapy is recommended for the secondary prevention of cardio- and cerebrovascular disease, but for primary prevention it is advised only in patients at very high risk. With this background, this study aims to assess the appropriateness of antiplatelet therapy in acutely hospitalized older people according to their risk profile. Methods: Data were obtained from the REPOSI register held in Italian and Spanish internal medicine and geriatric wards in 2012 and 2014. Hospitalized patients aged 6565 assessable at discharge were selected. Appropriateness of the antiplatelet therapy was evaluated according to their primary or secondary cardiovascular prevention profiles. Results: Of 2535 enrolled patients, 2199 were assessable at discharge. Overall 959 (43.6%, 95% CI 41.5\u201345.7) were prescribed an antiplatelet drug, aspirin being the most frequently chosen. Among patients prescribed for primary prevention, just over half were inappropriately prescribed (52.1%), being mainly overprescribed (155/209 patients, 74.2%). On the other hand, there was also a high rate of inappropriate underprescription in the context of secondary prevention (222/726 patients, 30.6%, 95% CI 27.3\u201334.0%). Conclusions: This study carried out in acutely hospitalized older people shows a high degree of inappropriate prescription among patients prescribed with antiplatelets for primary prevention, mainly due to overprescription. Further, a large proportion of patients who had had overt cardio- or cerebrovascular disease were underprescribed, in spite of the established benefits of antiplatelet drugs in the context of secondary prevention

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)