Long-term cardiovascular safety of COVID-19 vaccination according to brand, dose and combinations: Cohort study of 46 million adults in England (preprint)

Using longitudinal health records from 45.7 million adults in England followed for a year, our study compared the incidence of thrombotic and cardiovascular complications after first, second and booster doses of brands and combinations of COVID-19 vaccines used during the first two years of the UK vaccination program with the incidence before or without the corresponding vaccination. The incidence of common arterial thrombotic events (mainly acute myocardial infarction and ischaemic stroke) was generally lower after each vaccine dose, brand and combination. Similarly, the incidence of common venous thrombotic events, (mainly pulmonary embolism and lower limb deep venous thrombosis) was lower after vaccination. There was a higher incidence of previously reported rare harms after vaccination: vaccine-induced thrombotic thrombocytopenia after first ChAdOx1 vaccination, and myocarditis and pericarditis after first, second and transiently after booster mRNA vaccination (BNT-162b2 and mRNA- 1273) These findings support the wide uptake of future COVID-19 vaccination programs.

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

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 enables 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.

Risk of myocarditis and pericarditis following COVID-19 vaccination in England and Wales (preprint)

We describe our analyses of data from over 52 million people in England and Wales, representing near-complete coverage of the relevant population, to assess the risk of myocarditis and pericarditis following COVID-19 vaccination. A self-controlled case series (SCCS) design has previously reported increased risk of myocarditis after first doses of ChAdOx1, BNT162b2, and mRNA-1273 vaccinations and after second doses of the mRNA COVID-19 vaccinations in England. Here, we use a cohort design to estimate hazard ratios for hospitalised or fatal myocarditis/pericarditis and excess events after first and second doses of BNT162b2 and ChAdOx1 vaccinations. SCCS and cohort designs are subject to different assumptions and biases and therefore provide the opportunity for triangulation of evidence. In contrast to the findings from the SCCS approach previously reported for England, we found evidence of lower incidence of hospitalised or fatal myocarditis/pericarditis after first dose ChAdOx1 and BNT162b2 vaccination.

Understanding COVID-19 trajectories from a nationwide linked electronic health record cohort of 56 million people: phenotypes, severity, waves & vaccination (preprint)

Background: Updatable understanding of the onset and progression of individuals COVID-19 trajectories underpins pandemic mitigation efforts. In order to identify and characterize individual trajectories, we defined and validated ten COVID-19 phenotypes from linked electronic health records (EHR) on a nationwide scale using an extensible framework. Methods: Cohort study of 56.6 million people in England alive on 23/01/2020, followed until 31/05/2021, using eight linked national datasets spanning COVID-19 testing, vaccination, primary & secondary care and death registrations data. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity using a combination of international clinical terminologies (e.g. SNOMED-CT, ICD-10) and bespoke data fields; positive test, primary care diagnosis, hospitalisation, critical care (four phenotypes), and death (three phenotypes). Using these phenotypes, we constructed patient trajectories illustrating the transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings: We identified 3,469,528 infected individuals (6.1%) with 8,825,738 recorded COVID-19 phenotypes. Of these, 364,260 (11%) were hospitalised and 140,908 (4%) died. Of those hospitalised, 38,072 (10%) were admitted to intensive care (ICU), 54,026 (15%) received non-invasive ventilation and 21,404 (6%) invasive ventilation. Amongst hospitalised patients, first wave mortality (30%) was higher than the second (23%) in non-ICU settings, but remained unchanged for ICU patients. The highest mortality was for patients receiving critical care outside of ICU in wave 1 (51%). 13,083 (9%) COVID-19 related deaths occurred without diagnoses on the death certificate, but within 30 days of a positive test while 10,403 (7%) of cases were identified from mortality data alone with no prior phenotypes recorded. We observed longer patient trajectories in the second pandemic wave compared to the first. Interpretation: Our analyses illustrate the wide spectrum of severity that COVID-19 displays and significant differences in incidence, survival and pathways across pandemic waves. We provide an adaptable framework to answer questions of clinical and policy relevance; new variant impact, booster dose efficacy and a way of maximising existing data to understand individuals progression through disease states.

Association of COVID-19 vaccines ChAdOx1 and BNT162b2 with major venous, arterial, and thrombocytopenic events: whole population cohort study in 46 million adults in England (preprint)

BackgroundThromboses in unusual locations after the COVID-19 vaccine ChAdOx1-S have been reported. Better understanding of population-level thrombotic risks after COVID-19 vaccination is needed. MethodsWe analysed linked electronic health records from adults living in England, from 8th December 2020 to 18th March 2021. We estimated incidence rates and hazard ratios (HRs) for major arterial, venous and thrombocytopenic outcomes 1-28 and >28 days after first vaccination dose for ChAdOx1-S and BNT162b2 vaccines. Analyses were performed separately for ages <70 and [≥]70 years, and adjusted for age, sex, comorbidities, and social and demographic factors. ResultsOf 46,162,942 adults, 21,193,814 (46%) had their first vaccination during follow-up. Adjusted HRs 1-28 days after ChAdOx1-S, compared with unvaccinated rates, at ages <70 and [≥]70 respectively, were 0.97 (95% CI: 0.90-1.05) and 0.58 (0.53-0.63) for venous thromboses, and 0.90 (0.86-0.95) and 0.76 (0.73-0.79) for arterial thromboses. Corresponding HRs for BNT162b2 were 0.81 (0.74-0.88) and 0.57 (0.53-0.62) for venous thromboses, and 0.94 (0.90-0.99) and 0.72 (0.70-0.75) for arterial thromboses. HRs for thrombotic events were higher at younger ages for venous thromboses after ChAdOx1-S, and for arterial thromboses after both vaccines. Rates of intracranial venous thrombosis (ICVT) and thrombocytopenia in adults aged <70 years were higher 1-28 days after ChAdOx1-S (adjusted HRs 2.27, 95% CI:1.33- 3.88 and 1.71, 1.35-2.16 respectively), but not after BNT162b2 (0.59, 0.24-1.45 and 1.00, 0.75-1.34) compared with unvaccinated. The corresponding absolute excess risks of ICVT 1-28 days after ChAdOx1-S were 0.9-3 per million, varying by age and sex. ConclusionsIncreases in ICVT and thrombocytopenia after ChAdOx1-S vaccination in adults aged <70 years were small compared with its effect in reducing COVID-19 morbidity and mortality, although more precise estimates for adults <40 years are needed. For people aged [≥]70 years, rates of arterial or venous thrombotic, events were generally lower after either vaccine.

Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation (preprint)

Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to improve the efficiency and accuracy of medical coding. Recent studies on deep learning for automated medical coding achieved promising performances. However, the explainability of these models is usually poor, preventing them to be used confidently in supporting clinical practice. Another limitation is that these models mostly assume independence among labels, ignoring the complex correlation among medical codes which can potentially be exploited to improve the performance. We propose a Hierarchical Label-wise Attention Network (HLAN), which aimed to interpret the model by quantifying importance (as attention weights) of words and sentences related to each of the labels. Secondly, we propose to enhance the major deep learning models with a label embedding (LE) initialisation approach, which learns a dense, continuous vector representation and then injects the representation into the final layers and the label-wise attention layers in the models. We evaluated the methods using three settings on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS COVID-19 shielding codes. Experiments were conducted to compare HLAN and LE initialisation to the state-of-the-art neural network based methods. HLAN achieved the best Micro-level AUC and $F_1$ on the top-50 code prediction and comparable results on the NHS COVID-19 shielding code prediction to other models. By highlighting the most salient words and sentences for each label, HLAN showed more meaningful and comprehensive model interpretation compared to its downgraded baselines and the CNN-based models. LE initialisation consistently boosted most deep learning models for automated medical coding.

The 4C Initiative (Clinical Care for Cardiovascular disease in the COVID-19 pandemic): monitoring the indirect impact of the coronavirus pandemic on services for cardiovascular diseases in the UK (preprint)

Background: The coronavirus (COVID-19) pandemic affects cardiovascular diseases (CVDs) directly through infection and indirectly through health service reorganisation and public health policy. Real-time data are needed to quantify direct and indirect effects. We aimed to monitor hospital activity for presentation, diagnosis and treatment of CVDs during the pandemic to inform on indirect effects. Methods: We analysed aggregate data on presentations, diagnoses and treatments or procedures for selected CVDs (acute coronary syndromes, heart failure, stroke and transient ischaemic attack, venous thromboembolism, peripheral arterial disease and aortic aneurysm) in UK hospitals before and during the COVID-19 epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends. Findings: Nine hospitals across England and Scotland contributed hospital activity data from 28 Oct 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown), and for the same weeks during 2018-2019. Across all hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1-58.6%) and 52.9% (52.2-53.5%) respectively compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown, and fell by 31-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances RR 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020. Interpretation: Substantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.