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ObjectivesTo estimate the impact of the COVID-19 pandemic on cardiovascular disease (CVD) and CVD management using routinely collected medication data as a proxy. DesignDescriptive and interrupted time series analysis using anonymised individual-level population-scale data for 1.32 billion records of dispensed CVD medications across 15.8 million individuals in England, Scotland and Wales. SettingCommunity dispensed CVD medications with 100% coverage from England, Scotland and Wales, plus primary care prescribed CVD medications from England (including 98% English general practices). Participants15.8 million individuals aged 18+ years alive on 1st April 2018 dispensed at least one CVD medicine in a year from England, Scotland and Wales. Main outcome measuresMonthly counts, percent annual change (1st April 2018 to 31st July 2021) and annual rates (1st March 2018 to 28th February 2021) of medicines dispensed by CVD/ CVD risk factor; prevalent and incident use. ResultsYear-on-year change in dispensed CVD medicines by month were observed, with notable uplifts ahead of the first (11.8% higher in March 2020) but not subsequent national lockdowns. Using hypertension as one example of the indirect impact of the pandemic, we observed 491,203 fewer individuals initiated antihypertensive treatment across England, Scotland and Wales during the period March 2020 to end May 2021 than would have been expected compared to 2019. We estimated that this missed antihypertension treatment could result in 13,659 additional CVD events should individuals remain untreated, including 2,281 additional myocardial infarctions (MIs) and 3,474 additional strokes. Incident use of lipid-lowering medicines decreased by an average 14,793 per month in early 2021 compared with the equivalent months prior to the pandemic in 2019. In contrast, the use of incident medicines to treat type-2 diabetes (T2DM) increased by approximately 1,642 patients per month. ConclusionsManagement of key CVD risk factors as proxied by incident use of CVD medicines has not returned to pre-pandemic levels in the UK. Novel methods to identify and treat individuals who have missed treatment are urgently required to avoid large numbers of additional future CVD events, further adding indirect cost of the COVID-19 pandemic.
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Deep learning (DL) and machine learning (ML) models trained on long-term patient trajectories held as medical codes in electronic health records (EHR) have the potential to improve disease prediction. Anticoagulant prescribing decisions in atrial fibrillation (AF) offer a use case where the benchmark stroke risk prediction tool (CHA2DS2-VASc) could be meaningfully improved by including more information from a patients medical history. In this study, we design and build the first DL and ML pipeline that uses the routinely updated, linked EHR data for 56 million people in England accessed via NHS Digital to predict first ischaemic stroke in people with AF, and as a secondary outcome, COVID-19 death. Our pipeline improves first stroke prediction in AF by 17% compared to CHA2DS2-VASc (0.61 (0.57-0.65) vs 0.52 (0.52-0.52) area under the receiver operating characteristics curves, 95% confidence interval) and provides a generalisable, opensource framework that other researchers and developers can build on.
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BackgroundUpdatable 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. MethodsCohort 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. FindingsWe 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. InterpretationOur 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. Research in ContextO_ST_ABSEvidence before the studyC_ST_ABSWe searched PubMed on October 14, 2021, for publications with the terms "COVID-19" or "SARS-CoV-2", "severity", and "electronic health records" or "EHR" without date or language restrictions. Multiple studies explore factors associated with severity of COVID-19 infection, and model predictions of outcome for hospitalised patients. However, most work to date focused on isolated facets of the healthcare system, such as primary or secondary care only, was conducted in subpopulations (e.g. hospitalised patients) of limited sample size, and often utilized dichotomised outcomes (e.g. mortality or hospitalisation) ignoring the full spectrum of disease. We identified no studies which comprehensively detailed severity of infections while describing disease severity across pandemic waves, vaccination status, and patient trajectories. Added value of this studyTo our knowledge, this is the first study providing a comprehensive view of COVID-19 across pandemic waves using national data and focusing on severity, vaccination, and patient trajectories. Drawing on linked electronic health record (EHR) data on a national scale (56.6 million people alive and registered with GP in England), we describe key demographic factors, frequency of comorbidities, impact of the two main waves in England, and effect of full vaccination on COVID-19 severities. Additionally, we identify and describe patient trajectory networks which illustrate the main transition pathways of COVID-19 patients in the healthcare system. Finally, we provide reproducible COVID-19 phenotyping algorithms reflecting clinically relevant stages of disease severity i.e. positive tests, primary care diagnoses, hospitalisation, critical care treatments (e.g. ventilatory support) and mortality. Implications of all the available evidenceThe COVID-19 phenotypes and trajectory analysis framework outlined produce a reproducible, extensible and repurposable means to generate national-scale data to support critical policy decision making. By modelling patient trajectories as a series of interactions with healthcare systems, and linking these to demographic and outcome data, we provide a means to identify and prioritise care pathways associated with adverse outcomes and highlight healthcare system touch points which may act as tangible targets for intervention.
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ObjectiveEvaluate antithrombotic (AT) use in individuals with atrial fibrillation (AF) and high stroke risk (CHA2DS2-VASc score>=2) and investigate whether pre-existing AT use may improve COVID-19 outcomes. MethodsIndividuals with AF and a CHA2DS2-VASc score>=2 on January 1st 2020 were identified using pseudonymised, linked electronic health records for 56 million people in England and followed-up until May 1st 2021. Factors associated with pre-existing AT use were analysed using logistic regression. Differences in COVID-19 related hospitalisation and death were analysed using logistic and Cox regression for individuals exposed to pre-existing AT use vs no AT use, anticoagulants (AC) vs antiplatelets (AP) and direct oral anticoagulants (DOACs) vs warfarin. ResultsFrom 972,971 individuals with AF and a CHA2DS2-VASc score>=2, 88.0% (n=856,336) had pre-existing AT use, 3.8% (n=37,418) had a COVID-19 related hospitalisation and 2.2% (n=21,116) died. Factors associated with no AT use included comorbidities that may contraindicate AT use (liver disease and history of falls) and demographics (socioeconomic status and ethnicity). Pre-existing AT use was associated with lower odds of death (OR=0.92 [0.87-0.96 at 95% CI]), but higher odds of hospitalisation OR=1.20 [1.15-1.26 at 95% CI]). The same pattern was observed for AC vs AP (death (OR=0.93 [0.87-0.98]), hospitalisation (OR=1.17 [1.11-1.24])) but not for DOACs vs warfarin (death (OR=1.00 [0.95-1.05]), hospitalisation (OR=0.86 [0.82-0.89]). ConclusionsPre-existing AT use may offer marginal protection against COVID-19 death, with AC offering more protection than AP. Although this association may not be causal, it provides further incentive to improve AT coverage for eligible individuals with AF. KEY QUESTIONSO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIAnticoagulants (AC), a sub-class of antithrombotics (AT), reduce the risk of stroke and are recommended for individuals with atrial fibrillation (AF) and at high risk of stroke (CHA2DS2-VASc score>=2, National Institute for Health and Care Excellence threshold). However, previous evaluations suggest that up to one third of these individuals may not be taking AC. Over estimation of bleeding and fall risk in elderly patients have been identified as potential factors in this under medicating. C_LIO_LIIn response to the COVID-19 pandemic, several observational studies have observed correlations between pre-existing AT use, particularly anticoagulants (AC), and lower risk of severe COVID-19 outcomes such as hospitalisation and death. However, these correlations are inconsistent across studies and have not compared all major sub-types of AT in one study. C_LI What does this study add?O_LIThis study uses datasets covering primary care, secondary care, pharmacy dispensing, death registrations, multiple COVID-19 diagnoses routes and vaccination records for 56 million people in England and is the largest scale evaluation of AT use to date. This provides the statistical power to robustly analyse targeted sub-types of AT and control for a wide range of potential confounders. All code developed for the study is opensource and an updated nationwide evaluation can be rapidly created for future time points. C_LIO_LIIn 972,971 individuals with AF and a CHA2DS2-VASc score>=2, we observed 88.0% (n=856,336) with pre-existing AT use which was associated with marginal protection against COVID-19 death (OR=0.92 [0.87-0.96 at 95% CI]). C_LI How might this impact on clinical practice?O_LIThese findings can help shape global AT medication policy and provide population-scale, observational analysis results alongside gold-standard randomised control trials to help assess whether a potential beneficial effect of pre-existing AT use on COVID-19 death alters risk to benefit assessments in AT prescribing decisions. C_LI
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BackgroundThe 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. MethodsWe 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. FindingsNine 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. InterpretationSubstantial 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. FundingBritish Heart Foundation, Health Data Research UK