Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts.

Chronic inflammation is a hallmark of age-related disease states. The effectiveness of inflammatory proteins including C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N = 17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic-net-regression-based predictor outperformed competing methods and explained 18% of phenotypic variance in the Lothian Birth Cohort of 1936 (LBC1936) cohort, doubling that of existing DNAm predictors. DNAm predictors performed comparably in four additional test cohorts (Avon Longitudinal Study of Parents and Children, Health for Life in Singapore, Southall and Brent Revisited, and LBC1921), including for individuals of diverse genetic ancestry and different age groups. The best-performing predictor surpassed assay-measured CRP and a genetic score in its associations with 26 health outcomes. Our findings forge new avenues for assessing chronic low-grade inflammation in diverse populations.

New quality outcome indicators for bone metastases: expert consensus analysis of patients, their families and specialist healthcare professionals.

OBJECTIVES: As workload increases, surgical care for patients with bone metastases is increasingly decentralised, with a shift in management away from primary bone tumour units to local centres. We must ensure that patients have similar outcomes regardless of where they receive their treatment. The aim was to develop and validate a set of quality outcome indicators (QOIs) to evaluate treatment success for patients undergoing surgery for bone metastases. METHODS: Outcome recommendations were adapted from the literature and field tested in a retrospective patient cohort to determine feasibility. The provisional outcome indicators were assessed during a modified RAND/Delphi consensus process by a group of patients, relatives and healthcare professionals with validated targets added. RESULTS: 1534 articles were reviewed. 38 quality objectives were extracted and assessed for feasibility using clinical records for 117 patients. 28 provisional outcome indicators proceeded to expert consensus and were reviewed by a group of 22 panellists including 10 patients and 4 relatives/carers. After two rounds, 15 QOIs were generated, with validated targets based on expert consensus. These included specific statements such as 'surgery improves pain and reduces the need for morphine, target: at follow-up, pain is documented in 80% of individuals and 50% of these have reduced need for morphine'. CONCLUSIONS: The published evidence and guidelines were adapted into a set of outcome indicators validated by patients, their family/carers and healthcare professionals. These can be used to compare care between centres and identify units of excellence in maximising good outcome after surgery for bone metastases.

Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity.

Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond "White", "Black", "Asian", "Mixed" and "Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

C-Reactive Protein, Interleukin-6, and Vascular Recurrence According to Stroke Subtype: An Individual Participant Data Meta-Analysis

BACKGROUND AND OBJECTIVES: Anti-inflammatory therapies reduce major adverse cardiovascular events (MACE) in coronary artery disease but remain unproven after stroke. Establishing the subtype-specific association between inflammatory markers and recurrence risk is essential for optimal selection of patients in randomized trials (RCTs) of anti-inflammatory therapies for secondary stroke prevention. METHODS: Using individual participant data (IPD) identified from a systematic review, we analyzed the association between high-sensitivity C-reactive protein, interleukin-6 (IL-6), and vascular recurrence after ischemic stroke or transient ischemic attack. The prespecified coprimary end points were (1) any recurrent MACE (first major coronary event, recurrent stroke, or vascular death) and (2) any recurrent stroke (ischemic, hemorrhagic, or unspecified) after sample measurement. Analyses were performed stratified by stroke mechanism, per quarter and per biomarker unit increase after loge transformation. We then did study-level meta-analysis with comparable published studies not providing IPD. Preferred Reporting Items for Systematic Review and Meta-Analyses IPD guidelines were followed. RESULTS: IPD was obtained from 10 studies (8,420 patients). After adjustment for vascular risk factors and statins/antithrombotic therapy, IL-6 was associated with recurrent MACE in stroke caused by large artery atherosclerosis (LAA) (risk ratio [RR] 2.30, 95% CI 1.21-4.36, p = 0.01), stroke of undetermined cause (UND) (RR 1.78, 1.19-2.66, p = 0.005), and small vessel occlusion (SVO) (RR 1.71, 0.99-2.96, p = 0.053) (quarter 4 [Q4] vs quarter 1 [Q1]). No association was observed for stroke due to cardioembolism or other determined cause. Similar results were seen for recurrent stroke and when analyzed per loge unit increase for MACE (LAA, RR 1.26 [1.06-1.50], p = 0.009; SVO, RR 1.22 [1.01-1.47], p = 0.04; UND, RR 1.18 [1.04-1.34], p = 0.01). High-sensitivity CRP was associated with recurrent MACE in UND stroke only (Q4 vs Q1 RR 1.45 [1.04-2.03], p = 0.03). Findings were consistent on study-level meta-analysis of the IPD results with 2 other comparable studies (20,136 patients). DISCUSSION: Our data provide new evidence for the selection of patients in future RCTs of anti-inflammatory therapy in stroke due to large artery atherosclerosis, small vessel occlusion, and undetermined etiology according to inflammatory marker profile.

Multiple Cardiac Biomarkers to Improve Prediction of Cardiovascular Events: Findings from the Generation Scotland Scottish Family Health Study.

BACKGROUND: Many studies have investigated whether single cardiac biomarkers improve cardiovascular risk prediction for primary prevention but whether a combined approach could further improve risk prediction is unclear. We aimed to test a sex-specific, combined cardiac biomarker approach for cardiovascular risk prediction. METHODS: In the Generation Scotland Scottish Family Health Study, N-terminal pro-B-type natriuretic peptide (NT-proBNP), growth differentiation factor-15 (GDF-15), cardiac troponin I (cTnI), cardiac troponin T (cTnT), and C-reactive protein (CRP) were measured in stored serum using automated immunoassays. Sex-specific Cox models that included SCORE2 risk factors evaluated addition of single and combined biomarkers for prediction of major adverse cardiovascular events (MACE). Combined biomarker models were compared to a baseline model that included SCORE2 risk factors. RESULTS: The study population comprised 18 383 individuals (58.9% women, median age of 48 years [25th-75th percentile, 35-58 years]). During the median follow up of 11.6 (25th-75th percentile, 10.8-13.0) years, MACE occurred in 942 (5.1%) individuals. The greatest increase in discrimination with addition of individual biomarkers to the base model was for women GDF-15 and for men NT-proBNP (change in c-index: + 0.010 for women and +0.005 for men). For women, combined biomarker models that included GDF-15 and NT-proBNP (+0.012) or GDF-15 and cTnI (+0.013), but not CRP or cTnT, further improved discrimination. For men, combined biomarker models that included NT-proBNP and GDF-15 (+0.007), NT-proBNP and cTnI (+0.006), or NT-proBNP and CRP (+0.008), but not cTnT, further improved discrimination. CONCLUSIONS: A combined biomarker approach, particularly the use of GDF-15, NT-proBNP and cTnI, further refined cardiovascular risk estimates.

Cardiovascular events and venous thromboembolism after primary malignant or non-malignant brain tumour diagnosis: a population matched cohort study in Wales (United Kingdom).

BACKGROUND: Elevated standardised mortality ratio of cardiovascular diseases (CVD) in patients with brain tumours may result from differences in the CVD incidences and cardiovascular risk factors. We compared the risk of CVD among patients with a primary malignant or non-malignant brain tumour to a matched general population cohort, accounting for other co-morbidities. METHODS: Using data from the Secured Anonymised Information Linkage (SAIL) Databank in Wales (United Kingdom), we identified all adults aged ≥ 18 years in the primary care database with first diagnosis of malignant or non-malignant brain tumour identified in the cancer registry in 2000-2014 and a matched cohort (case-to-control ratio 1:5) by age, sex and primary care provider from the general population without any cancer diagnosis. Outcomes included fatal and non-fatal major vascular events (stroke, ischaemic heart disease, aortic and peripheral vascular diseases) and venous thromboembolism (VTE). We used multivariable Cox models adjusted for clinical risk factors to compare risks, stratified by tumour behaviour (malignant or non-malignant) and follow-up period. RESULTS: There were 2869 and 3931 people diagnosed with malignant or non-malignant brain tumours, respectively, between 2000 and 2014 in Wales. They were matched to 33,785 controls. Within the first year of tumour diagnosis, malignant tumour was associated with a higher risk of VTE (hazard ratio [HR] 21.58, 95% confidence interval 16.12-28.88) and stroke (HR 3.32, 2.44-4.53). After the first year, the risks of VTE (HR 2.20, 1.52-3.18) and stroke (HR 1.45, 1.00-2.10) remained higher than controls. Patients with non-malignant tumours had higher risks of VTE (HR 3.72, 2.73-5.06), stroke (HR 4.06, 3.35-4.93) and aortic and peripheral arterial disease (HR 2.09, 1.26-3.48) within the first year of diagnosis compared with their controls. CONCLUSIONS: The elevated CVD and VTE risks suggested risk reduction may be a strategy to improve life quality and survival in people with a brain tumour.

Antipsychotic drug prescribing and mortality in people with dementia before and during the COVID-19 pandemic: a retrospective cohort study in Wales, UK.

BACKGROUND: Concerns have been raised that antipsychotic drug prescribing, which has been associated with increased mortality in people with dementia, might have increased during the COVID-19 pandemic due to social restrictions imposed to limit the spread of SARS-CoV-2. We used multisource, routinely collected health-care data from Wales, UK to investigate prescribing and mortality variations in people with dementia before and during the COVID-19 pandemic. METHODS: In this retrospective cohort study, we used individual-level, anonymised, population-scale linked health data to identify adults aged 60 years and older with a diagnosis of dementia in Wales, UK. We used the CVD-COVID-UK initiative to access Welsh routinely collected electronic health record data from the Secure Anonymised Information Linkage (SAIL) Databank. Patients who were alive and registered with a SAIL general practice on Jan 1, 2016, and who received a dementia diagnosis before the age of 60 years and before or during the study period were included. We explored antipsychotic drug prescribing rate changes over 67 months, between Jan 1, 2016, and Aug 1, 2021, overall and stratified by age and dementia subtype. We used time-series analyses to examine all-cause and myocardial infarction and stroke mortality over the study period and identified the leading causes of death in people with dementia between Jan 1, 2020, and Aug 1, 2021. FINDINGS: Of 3 106 690 participants in SAIL between Jan 1, 2016 and Aug 1, 2021, 57 396 people (35 148 [61·2%] women and 22 248 [38·8%] men) met inclusion criteria for this study and contributed 101 428 person-years of follow-up. Of the 57 396 people with dementia, 11 929 (20·8%) were prescribed an antipsychotic drug at any point during follow-up. Accounting for seasonality, antipsychotic drug prescribing increased during the second half of 2019 and throughout 2020. However, the absolute difference in prescribing rates was small, ranging from 1253 prescriptions per 10 000 person-months in March, 2019, to 1305 per 10 000 person-months in September, 2020. All-cause mortality and stroke mortality increased throughout 2020, while myocardial infarction mortality declined. From Jan 1, 2020, to Aug 1, 2021, 1286 (17·1%) of 7508 participants who died had COVID-19 recorded as the underlying cause of death. INTERPRETATION: During the COVID-19 pandemic, antipsychotic drug prescribing in people with dementia in the UK increased slightly; however, it is unlikely that this was solely related to the pandemic and this increase was unlikely to be a major factor in the substantial increase in mortality during 2020. The long-term increase in antipsychotic drug prescribing in younger people and in those with Alzheimer's disease warrants further investigation using resources with access to more granular clinical data. Although deprescribing antipsychotic medications remains an essential aspect of dementia care, the results of this study suggest that changes in prescribing and deprescribing practices as a result of the COVID-19 pandemic are not required. FUNDING: British Heart Foundation (via the British Heart Foundation Data Science Centre led by Health Data Research UK), and the Scottish Neurological Research Fund.

Cardiovascular imaging research priorities.

OBJECTIVES: Two interlinked surveys were organised by the British Heart Foundation Data Science Centre, which aimed to establish national priorities for cardiovascular imaging research. METHODS: First a single time point public survey explored their views of cardiovascular imaging research. Subsequently, a three-phase modified Delphi prioritisation exercise was performed by researchers and healthcare professionals. Research questions were submitted by a diverse range of stakeholders to the question 'What are the most important research questions that cardiovascular imaging should be used to address?'. Of these, 100 research questions were prioritised based on their positive impact for patients. The 32 highest rated questions were further prioritised based on three domains: positive impact for patients, potential to reduce inequalities in healthcare and ability to be implemented into UK healthcare practice in a timely manner. RESULTS: The public survey was completed by 354 individuals, with the highest rated areas relating to improving treatment, quality of life and diagnosis. In the second survey, 506 research questions were submitted by diverse stakeholders. Prioritisation was performed by 90 researchers or healthcare professionals in the first round and 64 in the second round. The highest rated questions were 'How do we ensure patients have equal access to cardiovascular imaging when it is needed?' and 'How can we use cardiovascular imaging to avoid invasive procedures'. There was general agreement between healthcare professionals and researchers regarding priorities for the positive impact for patients and least agreement for their ability to be implemented into UK healthcare practice in a timely manner. There was broad overlap between the prioritised research questions and the results of the public survey. CONCLUSIONS: We have identified priorities for cardiovascular imaging research, incorporating the views of diverse stakeholders. These priorities will be useful for researchers, funders and other organisations planning future research.

Refining epigenetic prediction of chronological and biological age.

BACKGROUND: Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. METHODS: First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study). RESULTS: Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. CONCLUSIONS: The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

Charting a Course for Smartphones and Wearables to Transform Population Health Research.

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people's lives for the better.

Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.

BACKGROUND: The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. METHODS: Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. RESULTS: Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. CONCLUSIONS: We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.

The impact of the COVID-19 pandemic on cardiovascular disease prevention and management.

How the Coronavirus Disease 2019 (COVID-19) pandemic has affected prevention and management of cardiovascular disease (CVD) is not fully understood. In this study, we used medication data as a proxy for CVD management using routinely collected, de-identified, individual-level data comprising 1.32 billion records of community-dispensed CVD medications from England, Scotland and Wales between April 2018 and July 2021. Here we describe monthly counts of prevalent and incident medications dispensed, as well as percentage changes compared to the previous year, for several CVD-related indications, focusing on hypertension, hypercholesterolemia and diabetes. We observed a decline in the dispensing of antihypertensive medications between March 2020 and July 2021, with 491,306 fewer individuals initiating treatment than expected. This decline was predicted to result in 13,662 additional CVD events, including 2,281 cases of myocardial infarction and 3,474 cases of stroke, should individuals remain untreated over their lifecourse. Incident use of lipid-lowering medications decreased by 16,744 patients per month during the first half of 2021 as compared to 2019. By contrast, incident use of medications to treat type 2 diabetes mellitus, other than insulin, increased by approximately 623 patients per month for the same time period. In light of these results, methods to identify and treat individuals who have missed treatment for CVD risk factors and remain undiagnosed are urgently required to avoid large numbers of excess future CVD events, an indirect impact of the COVID-19 pandemic.

Effects of the COVID-19 pandemic on secondary care for cardiovascular disease in the UK: an electronic health record analysis across three countries.

BACKGROUND: Although morbidity and mortality from COVID-19 have been widely reported, the indirect effects of the pandemic beyond 2020 on other major diseases and health service activity have not been well described. METHODS AND RESULTS: Analyses used national administrative electronic hospital records in England, Scotland, and Wales for 2016-21. Admissions and procedures during the pandemic (2020-21) related to six major cardiovascular conditions [acute coronary syndrome (ACS), heart failure (HF), stroke/transient ischaemic attack (TIA), peripheral arterial disease (PAD), aortic aneurysm (AA), and venous thromboembolism(VTE)] were compared with the annual average in the pre-pandemic period (2016-19). Differences were assessed by time period and urgency of care.In 2020, there were 31 064 (-6%) fewer hospital admissions [14 506 (-4%) fewer emergencies, 16 560 (-23%) fewer elective admissions] compared with 2016-19 for the six major cardiovascular diseases (CVDs) combined. The proportional reduction in admissions was similar in all three countries. Overall, hospital admissions returned to pre-pandemic levels in 2021. Elective admissions remained substantially below expected levels for almost all conditions in all three countries [-10 996 (-15%) fewer admissions]. However, these reductions were offset by higher than expected total emergency admissions [+25 878 (+6%) higher admissions], notably for HF and stroke in England, and for VTE in all three countries. Analyses for procedures showed similar temporal variations to admissions. CONCLUSION: The present study highlights increasing emergency cardiovascular admissions during the pandemic, in the context of a substantial and sustained reduction in elective admissions and procedures. This is likely to increase further the demands on cardiovascular services over the coming years.

Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study.

OBJECTIVES: To use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a scenario-based model incorporating baseline mortality risk, infection rate (IR) and relative risk (RR) of death for prediction of excess deaths. DESIGN: An EHR-based, retrospective cohort study. SETTING: Linked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE). PARTICIPANTS: In the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged ≥30 years, respectively. MAIN OUTCOME MEASURES: One-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021. RESULTS: From 1 March 2020 to 1 March 2021, there were 127,020 observed excess deaths. Observed RR was 4.34% (95% CI, 4.31-4.38) and IR was 6.27% (95% CI, 6.26-6.28). In the validation cohort, predicted one-year excess deaths were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. CONCLUSIONS: We show that a simple, parsimonious model incorporating baseline mortality risk, one-year IR and RR of the pandemic can be used for scenario-based prediction of excess deaths in the early stages of a pandemic. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to date. Although infection dynamics are important in the prediction of mortality, future models should take greater account of underlying conditions.

Impact of tumour characteristics and cancer treatment on cerebrovascular mortality after glioma diagnosis: Evidence from a population-based cancer registry.

Objective: We aimed to examine brain tumour grade, a marker of biological aggressiveness, tumour size and cancer treatment are associated with cerebrovascular mortality among patients with malignant glioma, the most common and aggressive type of brain tumour. Methods: We conducted a retrospective, observational cohort study using the US National Cancer Institute's state and regional population-based cancer registries. We identified adult patients with glioma in 2000 to 2018 (N=72,916). The primary outcome was death from cerebrovascular disease. Cox regression modelling was used to estimate the associations with cerebrovascular mortality of tumour grade, tumour size and treatment (surgery, radiotherapy, chemotherapy), calculating hazard ratios (HR) adjusted for these factors as well as for age, sex, race, marital status and calendar year. Results: Higher grade (Grade IV vs Grade II: HR=2.47, 95% CI=1.69-3.61, p<0.001) and larger brain tumours (size 3 to <6 cm: HR=1.40, 95% CI=1.03 -1.89, p<0.05; size ≥ 6 cm: HR=1.47, 95% CI=1.02-2.13, p<0.05 compared to size < 3cm) were associated with increased cerebrovascular mortality. Cancer treatment was associated with decreased risk (surgery: HR= 0.60, p<0.001; chemotherapy: HR=0.42, p<0.001; radiation: HR= 0.69, p<0.05). However, among patents surviving five years or more from cancer diagnosis radiotherapy was associated with higher risk of cerebrovascular mortality (HR 2.73, 95% CI 1.49-4.99, p<0.01). Conclusion: More aggressive tumour characteristics are associated with increased cerebrovascular mortality. Radiotherapy increased risk of cerebrovascular mortality five-year after cancer diagnosis. Further research is needed to better understand the long-term cardiovascular consequences of radiation therapy, and whether the consequent risk can be mitigated.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Feasibility and ethics of using data from the Scottish newborn blood spot archive for research.

Background: Newborn heel prick blood spots are routinely used to screen for inborn errors of metabolism and life-limiting inherited disorders. The potential value of secondary data from newborn blood spot archives merits ethical consideration and assessment of feasibility for public benefit. Early life exposures and behaviours set health trajectories in childhood and later life. The newborn blood spot is potentially well placed to create an unbiased and cost-effective population-level retrospective birth cohort study. Scotland has retained newborn blood spots for all children born since 1965, around 3 million in total. However, a moratorium on research access is currently in place, pending public consultation. Methods: We conducted a Citizens' Jury as a first step to explore whether research use of newborn blood spots was in the public interest. We also assessed the feasibility and value of extracting research data from dried blood spots for predictive medicine. Results: Jurors delivered an agreed verdict that conditional research access to the newborn blood spots was in the public interest. The Chief Medical Officer for Scotland authorised restricted lifting of the current research moratorium to allow a feasibility study. Newborn blood spots from consented Generation Scotland volunteers were retrieved and their potential for both epidemiological and biological research demonstrated. Conclusions: Through the Citizens' Jury, we have begun to identify under what conditions, if any, should researchers in Scotland be granted access to the archive. Through the feasibility study, we have demonstrated the potential value of research access for health data science and predictive medicine.

Association of COVID-19 With Major Arterial and Venous Thrombotic Diseases: A Population-Wide Cohort Study of 48 Million Adults in England and Wales.

BACKGROUND: Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a prothrombotic state, but long-term effects of COVID-19 on incidence of vascular diseases are unclear. METHODS: We studied vascular diseases after COVID-19 diagnosis in population-wide anonymized linked English and Welsh electronic health records from January 1 to December 7, 2020. We estimated adjusted hazard ratios comparing the incidence of arterial thromboses and venous thromboembolic events (VTEs) after diagnosis of COVID-19 with the incidence in people without a COVID-19 diagnosis. We conducted subgroup analyses by COVID-19 severity, demographic characteristics, and previous history. RESULTS: Among 48 million adults, 125 985 were hospitalized and 1 319 789 were not hospitalized within 28 days of COVID-19 diagnosis. In England, there were 260 279 first arterial thromboses and 59 421 first VTEs during 41.6 million person-years of follow-up. Adjusted hazard ratios for first arterial thrombosis after COVID-19 diagnosis compared with no COVID-19 diagnosis declined from 21.7 (95% CI, 21.0-22.4) in week 1 after COVID-19 diagnosis to 1.34 (95% CI, 1.21-1.48) during weeks 27 to 49. Adjusted hazard ratios for first VTE after COVID-19 diagnosis declined from 33.2 (95% CI, 31.3-35.2) in week 1 to 1.80 (95% CI, 1.50-2.17) during weeks 27 to 49. Adjusted hazard ratios were higher, for longer after diagnosis, after hospitalized versus nonhospitalized COVID-19, among Black or Asian versus White people, and among people without versus with a previous event. The estimated whole-population increases in risk of arterial thromboses and VTEs 49 weeks after COVID-19 diagnosis were 0.5% and 0.25%, respectively, corresponding to 7200 and 3500 additional events, respectively, after 1.4 million COVID-19 diagnoses. CONCLUSIONS: High relative incidence of vascular events soon after COVID-19 diagnosis declines more rapidly for arterial thromboses than VTEs. However, incidence remains elevated up to 49 weeks after COVID-19 diagnosis. These results support policies to prevent severe COVID-19 by means of COVID-19 vaccines, early review after discharge, risk factor control, and use of secondary preventive agents in high-risk patients.

Severe mental illness and quality of care for type 2 diabetes: A retrospective population-based cohort study.

AIMS: To compare quality of care for type 2 diabetes in people with severe mental illness (SMI) versus no mental illness. METHODS: We used routinely collected linked data to create a retrospective cohort study. We included 158,901 people diagnosed with type 2 diabetes in Scotland during 2009-2018 of whom 1701 (1%), 768 (0.5%) and 5211 (3%) had a prior hospital admission record for schizophrenia, bipolar disorder, and major depression, respectively. We compared recording of HbA1c, cholesterol, creatinine, blood pressure, urinary albumin, foot examination, retinopathy screening, body mass index and smoking during the first year after diabetes diagnosis using logistic regression and recording of HbA1c and retinopathy screening over longer follow-up using generalised linear mixed effects model, adjusting for confounding factors. RESULTS: Receipt of care during the first year was generally similar, or better, for people with each SMI than for people without any mental illness. During mean follow up of 4.8 (SD 2.5) years, depression and bipolar disorder were associated with lower odds of receiving retinopathy screening. CONCLUSIONS: Receipt of diabetes care was similar or better among people with SMI versus without SMI. However, mechanisms to support improved retinopathy screening for people with SMI are needed.

Frequency and Phenotype Associations of Rare Variants in 5 Monogenic Cerebral Small Vessel Disease Genes in 200,000 UK Biobank Participants.

Background and Objectives: Based on previous case reports and disease-based cohorts, a minority of patients with cerebral small vessel disease (cSVD) have a monogenic cause, with many also manifesting extracerebral phenotypes. We investigated the frequency, penetrance, and phenotype associations of putative pathogenic variants in cSVD genes in the UK Biobank (UKB), a large population-based study. Methods: We used a systematic review of previous literature and ClinVar to identify putative pathogenic rare variants in CTSA, TREX1, HTRA1, and COL4A1/2. We mapped phenotypes previously attributed to these variants (phenotypes-of-interest) to disease coding systems used in the UKB's linked health data from UK hospital admissions, death records, and primary care. Among 199,313 exome-sequenced UKB participants, we assessed the following: the proportion of participants carrying ≥1 variant(s); phenotype-of-interest penetrance; and the association between variant carrier status and phenotypes-of-interest using a binary (any phenotype present/absent) and phenotype burden (linear score of the number of phenotypes a participant possessed) approach. Results: Among UKB participants, 0.5% had ≥1 variant(s) in studied genes. Using hospital admission and death records, 4%-20% of variant carriers per gene had an associated phenotype. This increased to 7%-55% when including primary care records. Only COL4A1 variant carrier status was significantly associated with having ≥1 phenotype-of-interest and a higher phenotype score (OR = 1.29, p = 0.006). Discussion: While putative pathogenic rare variants in monogenic cSVD genes occur in 1:200 people in the UKB population, only approximately half of variant carriers have a relevant disease phenotype recorded in their linked health data. We could not replicate most previously reported gene-phenotype associations, suggesting lower penetrance rates, overestimated pathogenicity, and/or limited statistical power.