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An increasing number of people are now living with cardiovascular disease (CVD), with concomitant CVD-related hospitalizations, operations, and prescriptions. To ultimately deliver optimal cardiovascular care, access to population-based biobanks with data on multiomics, phenotypes, and lifestyle risk factors are crucial. UK Biobank is a cohort study that incorporated data between 2006 and 2010 from over half a million individuals (40 to 69 years of age) at recruitment from across the United Kingdom. As one of the most accessible, largest, and in-depth cohort studies in the world, UK Biobank continues to enhance the resource with the addition of data from various omics platforms (eg, genomics, metabolomics, proteomics), multimodal imaging, self-reported risk factors and health outcomes, and linkage to electronic health records. The vision of UK Biobank is to allow as many researchers as possible to apply their expertise and imagination to undertake research to prevent, diagnose, and treat a wide range of chronic conditions, including CVD., Competing Interests: Funding Support and Author Disclosures The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021. Published by Elsevier Inc.)

Background Large routinely collected data such as electronic health records (EHRs) are increasingly used in research, but the statistical methods and processes used to check such data for temporal data quality issues have not moved beyond manual, ad hoc production and visual inspection of graphs. With the prospect of EHR data being used for disease surveillance via automated pipelines and public-facing dashboards, automation of data quality checks will become increasingly valuable. Findings We generated 5,526 time series from 8 different EHR datasets and engaged >2,000 citizen-science volunteers to label the locations of all suspicious-looking change points in the resulting graphs. Consensus labels were produced using density-based clustering with noise, with validation conducted using 956 images containing labels produced by an experienced data scientist. Parameter tuning was done against 670 images and performance calculated against 286 images, resulting in a final sensitivity of 80.4% (95% CI, 77.1%–83.3%), specificity of 99.8% (99.7%–99.8%), positive predictive value of 84.5% (81.4%–87.2%), and negative predictive value of 99.7% (99.6%–99.7%). In total, 12,745 change points were found within 3,687 of the time series. Conclusions This large collection of labelled EHR time series can be used to validate automated methods for change point detection in real-world settings, encouraging the development of methods that can successfully be applied in practice. It is particularly valuable since change point detection methods are typically validated using synthetic data, so their performance in real-world settings cannot be assumed to be comparable. While the dataset focusses on EHRs and data quality, it should also be applicable in other fields. [ABSTRACT FROM AUTHOR]

UK Biobank is an intensively characterized prospective study of 500 000 men and women, aged 40 to 69 years when recruited, between 2006 and 2010, from the general population of the United Kingdom. Established as an open-access resource for researchers worldwide to perform health research that is in the public interest, UK Biobank has collected (and continues to collect) a vast amount of data on genetic, physiological, lifestyle, and environmental factors, with prolonged follow-up of heath conditions through linkage to administrative electronic health records. The study has already demonstrated its unique value in enabling research into the determinants of common endocrine and metabolic diseases. The importance of UK Biobank, heralded as a fagship project for UK health research, will only increase over time as the number of incident disease events accrue, and the study is enhanced with additional data from blood assays (such as whole-genome sequencing, metabolomics, and proteomics), wearable technologies (including physical activity and cardiac monitors), and body imaging (magnetic resonance imaging and dual-energy X-ray absorptiometry). This unique research resource is likely to transform our understanding of the causes, diagnosis, and treatment of many endocrine and metabolic disorders. [ABSTRACT FROM AUTHOR]

Data on patients discharged following COVID-19 hospitalization is scarce. We conducted an electronic health records study of community-acquired COVID-19 patients discharged between 15 March and 14 July 2020 from hospitals in Oxfordshire, UK. Of 403 discharged patients, 114 (28%) were readmitted or died within 60 days (incidence rate 18/100 person-months). Rates of readmission or death were twice as high among those ≥ 65 years as those < 65 years [standardized rate ratio: 2.21 (95% CI: 1.45–3.56)] and among women than men [2.25 (1.05–4.18)]. These findings suggest important sex differences in 60-day outcomes following COVID-19 hospitalization that have not previously been well described. [ABSTRACT FROM AUTHOR]

Here, we describe examples of how the United Kingdom's single-payer system, the National Health Service (NHS), has enabled and improved medical research, and how these lessons might translate to the United States if a single-payer system were to be adopted. NHS Digital, the data informatics and information technology branch of the NHS, collates and curates healthcare information nationally. In addition to lowering the costs associated with trial recruitment and follow-up, use of a centralized health information system similar to that of the NHS may address 2 other major limitations in trials: slow recruitment and short follow-up times. [Extracted from the article]