Search

Background: Heart failure is common, complex, and often associated with coexisting chronic medical conditions and a high mortality. We aimed to assess the epidemiology of people admitted to hospital with heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF), including the period covering the COVID-19 pandemic, which was previously not well characterised., Methods: In this retrospective, cohort study, we used whole-population electronic health records with 57 million individuals in England to identify patients hospitalised with heart failure as the primary diagnosis in any consultant episode of an in-patient admission to a National Health Service (NHS) hospital. We excluded individuals with less than 1 year of medical history records in primary or secondary care; admissions to NHS hospitals for which less than 10% of heart failure cases were linkable to the National Heart Failure Audit (NHFA); individuals younger than 18 years at the time of the heart failure hospitalisation; and patients who died in hospital during the index heart failure admission. For patients with new onset heart failure, we assessed incidence rates of 30-day and 1-year all-cause and cause-specific (cardiovascular, non-cardiovascular, and heart failure-related) emergency rehospitalisation and mortality after discharge, and dispensed guideline-recommended medical therapy (GRMT). Follow-up occurred from the index admission to the earliest occurrence of the event of interest, death, or end of data coverage. We estimated adjusted hazard ratios (HRs) to compare HFrEF with HFpEF. We computed population-attributable fractions to quantify the percentage of outcomes attributable to coexisting chronic medical conditions., Findings: Among 233 320 patients identified who survived the index heart failure admission across 335 NHS hospitals between Jan 1, 2019, and Dec 31, 2022, 101 320 (43·4%) had HFrEF, 71 910 (30·8%) had HFpEF, and 60 090 (25·8%) had an unknown classification. In patients with new onset heart failure, there were reductions in all-cause 30-day (-5·2% [95% CI -7·7 to -2·6] in 2019-22) and 1-year rehospitalisation rates (-3·9% [-6·6 to -1·2]). Declining 30-day rehospitalisation rates affected patients with HFpEF (-4·8% [-9·2 to -0·2]) and HFrEF (-6·2% [-10·5 to -1·6]), although 1-year rates were not statistically significant for patients with HFpEF (-2·2% [-6·6 to 2·3] vs -5·7% [-10·6 to -0·5] for HFrEF). There were no temporal trends in incidence rates of 30-day or 1-year mortality after discharge. The rates of all-cause (HR 1·20 [1·18-1·22]) and cause-specific rehospitalisation were uniformly higher in those with HFpEF than those with HFrEF. Patients with HFpEF also had higher rates of 1-year all-cause mortality after discharge (HR 1·07 [1·05-1·09]), driven by excess risk of non-cardiovascular death (HR 1·25 [1·21-1·29]). Rates of rehospitalisation and mortality were highest in patients with coexisting chronic kidney disease, chronic obstructive pulmonary disease, dementia, and liver disease. Chronic kidney disease contributed to 6·5% (5·6-7·4) of rehospitalisations within 1 year for HFrEF and 5·0% (4·1-5·9) of rehospitalisations for HFpEF, double that of any other coexisting condition. There was swift implementation of newer GRMT, but markedly lower dispensing of these medications in patients with coexisting chronic kidney disease., Interpretation: Rates of rehospitalisation in patients with heart failure in England have decreased during 2019-22. Further population health improvements could be reached through enhanced implementation of GRMT, particularly in patients with coexisting chronic kidney disease, who, despite being at high risk, remain undertreated., Funding: Wellcome Trust, Health Data Research UK, British Heart Foundation Data Science Centre., Competing Interests: Declaration of interests RAF received studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome Trust PhD Programme in Health Data Science (grant 218529/Z/19/Z). PR received a grant from the Dr Johannes and Hertha Tuba Foundation. BLN reports fees for travel support, advisory boards, scientific presentations, and steering committee roles from AstraZeneca, Alexion, Bayer, Boehringer Ingelheim, Cambridge Healthcare Research, Cornerstone Medical Education, Janssen, Limbic, Medscape, Novo Nordisk, and Travere Therapeutics with all honoraria paid to the George Institute for Global Health. CT received a studentship from the University College London UK Research and Innovation Centre for doctoral training in AI-enabled healthcare (EP/S021612/1), Medical Research Council Clinical Top-Up, and a studentship from the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at University College London Hospital NHS Trust. NC received a personal fellowship from the Research Foundation Flanders and a research grant from the European Society of Cardiology. ER is funded by Forte Swedish Research Council for Health, Working Life And Welfare (2022–00882) individual postdoctoral fellowship, and Vetenskapsrådet Swedish Research Council (grant 2023–01982). SD received research funding from GlaxoSmithKline, AstraZeneca, Bayer, and BenevolentAI. CS is a director of the British Heart Foundation Data Science Centre and a chief scientist and deputy director at Health Data Research UK; has codeveloped National Health Service (NHS) England Secure Data Environment; and leads the CVD-COVID UK/COVID-IMPACT Consortium. SEP has a leadership role for the European Association of Cardiovascular Imaging, received consulting fees from Circle Cardiovascular Imaging, and holds an advisory role for the PROTEUS trial (NCT05028179). GMC served on the board of directors of Satellite Healthcare; served as chair or cochair of trial steering committees for Akebia, AstraZeneca, CSL Behring, Sanifit, and Vertex; served as an advisor for Applaud, CloudCath, Durect, Eliaz Therapeutics, Miromatrix, Outset, Physiowave, Renibus, and Unicycive; and served on data safety monitoring boards for Bayer, Mineralys, and ReCor. KK acted as a consultant or speaker and receiving grants for investigator-initiated studies from AstraZeneca, Abbott, Amgen, Bayer, Daiichi-Sankyo, Embecta, Nestle Health Science, Novartis, Novo Nordisk, Roche, Servier, Sanofi-Aventis, Lilly, MSD, Boehringer Ingelheim, Oramed Pharmaceuticals, and Applied Therapeutics; and was a chair of the scientific advisory group for Emergencies Ethnicity Subgroup. JS has direct or indirect stock ownership in Anagram Kommunikation, Sence Research, Symptoms Europe, and MinForskning; and professional services to Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Gilead, GSK, Göteborg University, Itrim, Ipsen, Janssen, Karolinska Institutet, LIF, Linköping University, Novo Nordisk, Parexel, Pfizer, Region Stockholm, Region Uppsala, Sanofi, STRAMA, Takeda, TLV, Uppsala University, Vifor Pharma, and WeMind. CA received grants from the National Health Medical Research Council (Medical Research Futures Fund) and NSW Health; and honoraria from AstraZeneca, Novo Nordisk, and Amgen. JGFC reports receipt of personal honoraria for lectures and advisory boards from Pharmacosmos, Vifor, AstraZeneca, Amgen, Bayer, Novartis, and Servier; has received research grants through The University of Glasgow from Pharmacosmos and Vifor; and received funding from the British Heart Foundation Center of Research Excellence (RE/18/6134217). JD reports grants, personal fees, and non-financial support from MSD and Novartis; grants from Pfizer and AstraZeneca; and is part of the International Cardiovascular and Metabolic Advisory Board for Novartis, Steering Committee of UK Biobank, MRC International Advisory Group, MRC High Throughput Science Omics Panel, Scientific Advisory Committee for Sanofi, International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis, AstraZeneca Genomics Advisory Board, Scientific Advisory Board of Nightingale Health, Access Board of Our Future Health, and Scientific Advisory Committee of Leducq Foundation. JJVM received payments through Glasgow University for work on clinical trials, consulting, and other activities from Amgen, AstraZeneca, Bayer, Cardurion, Cytokinetics, GSK, KBP Biosciences, and Novartis; personal consultancy fees from Alnylam Pharma, Bayer, BMS, George Clinical, Ionis Pharma, Novartis, Regeneron Pharma, and River 2 Renal; personal lecture fees from Abbott, Alkem Metabolics, AstraZeneca, Blue Ocean Scientific Solutions, Boehringer Ingelheim, Canadian Medical and Surgical Knowledge, Emcure Pharma, Eris Lifesciences, European Academy of CME, Hikma Pharmaceuticals, Imagica Health, Intas Pharma, JB Pharma, Lupin Pharma, Medscape (Heart.org), ProAdWise Communications, Radcliffe Cardiology, Sun Pharma, The Corpus, Translation Research Group, and Translational Medicine Academy; and is a director of Global Clinical Trial Partners. MV received research grant support, served on advisory boards, or had speaker engagements with American Regent, Amgen, AstraZeneca, Bayer, Baxter, Boehringer Ingelheim, Chiesi, Cytokinetics, Lexicon Pharmaceuticals, Merck, Novartis, Novo Nordisk, Pharmacosmos, Relypsa, Roche Diagnostics, Sanofi, and Tricog Health; and participates on clinical trial committees for AstraZeneca, Galmed, Novartis, Bayer, Occlutech, and Impulse Dynamics. AMW received funding from the British Heart Foundation Data Science Centre (HDRUK2023.0239) and NIHR (NIHR303137). All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: To identify highest-risk subgroups for COVID-19 and Long COVID(LC), particularly in contexts of influenza and cardiovascular disease(CVD)., Methods: Using national, linked electronic health records for England (NHS England Secure Data Environment via CVD-COVID-UK/COVID-IMPACT Consortium), we studied individuals (of all ages) with COVID-19 and LC (2020-2023). We compared all-cause hospitalization and mortality by prior CVD, high CV risk, vaccination status (COVID-19/influenza), and CVD drugs, investigating impact of vaccination and CVD prevention using population preventable fractions., Results: Hospitalization and mortality were 15.3% and 2.0% among 17,373,850 individuals with COVID-19 (LC rate 1.3%), and 16.8% and 1.4% among 301,115 with LC. Adjusted risk of mortality and hospitalization were reduced with COVID-19 vaccination ≥ 2 doses(COVID-19:HR 0.36 and 0.69; LC:0.44 and 0.90). With influenza vaccination, mortality was reduced, but not hospitalization (COVID-19:0.86 and 1.01, and LC:0.72 and 1.05). Mortality and hospitalization were reduced by CVD prevention in those with CVD, e.g., anticoagulants- COVID:19:0.69 and 0.92; LC:0.59 and 0.88; lipid lowering- COVID-19:0.69 and 0.86; LC:0.68 and 0.90. COVID-19 vaccination averted 245044 of 321383 and 7586 of 8738 preventable deaths after COVID-19 and LC, respectively., Interpretation: Prior CVD and high CV risk are associated with increased hospitalization and mortality in COVID-19 and LC. Targeted COVID-19 vaccination and CVD prevention are priority interventions., Funding: NIHR. HDR UK., Competing Interests: Declarations of competing interest KK is chair of the ethnicity subgroup of the UK Scientific Advisory Group for Emergencies (SAGE) and is a member of SAGE. KK (Chair) and AB are members of the LC Research Group that reports to the Chief Medical Officer for England. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: For patients with acute heart failure (HF), specialist HF care during admission improves diagnosis and treatments., Objectives: The authors aimed to investigate the association of HF specialist care with in-hospital and longer term prognosis., Methods: The authors used data from the National Heart Failure Audit from January 1, 2018, to December 31, 2022, linked to electronic records for hospitalization and deaths. All-cause mortality was the primary outcome measure and in-hospital mortality the secondary outcome measure., Results: Data for 227,170 patients admitted to hospital with HF (median age: 81 years; IQR: 72-88 years), were analyzed. Approximately 80% of acute HF admissions received support from HF specialists. Thirty-nine percent of patients (n = 70,720) were seen by a multidisciplinary team (HF physicians and HF specialist nurses [HFSNd]), 22% (n = 40,330) were seen by HFSNs alone, and the remaining 39% (n = 71,700) were seen exclusively by specialist HF physicians. At discharge, more patients who received HF specialist care were prescribed medical therapy for HF and had specialized follow-up. Conversely, diuretic agents were prescribed to fewer patients. HF specialist care was independently associated with a higher rate of prescribing HF therapies at discharge and a lower likelihood of receiving diuretic therapy (OR: 0.90 [95% CI: 0.86-0.95]; P < 0.001). HF specialist care was associated with better long-term survival (HR: 0.89 [95% CI: 0.87-0.90]; P < 0.001) and lower in-hospital mortality (OR: 0.92 [95% CI: 0.0.88-0.97]; P < 0.001)., Conclusions: Receiving HF specialist care during admission for HF is associated with a higher rate of implementation of medical therapy, fewer discharges on diuretic therapy, and lower in-hospital and long-term mortality across the left ventricular ejection fraction spectrum, especially for patients with heart failure with reduced ejection fraction., Competing Interests: Funding Support and Author Disclosures The British Heart Foundation Data Science Centre (grant number SP/19/3/34678), awarded to Health Data Research United Kingdom (HDR UK) funded co-development (with NHS England) of the secure data environment, provision of linked data sets, data access, user software licenses, computational usage, and data management, wrangling, and curation support, with additional contributions from the HDR UK Data and Connectivity component of the United Kingdom Government Chief Scientific Adviser’s National Core Studies program to coordinate national COVID-19 priority research. Consortium partner organizations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians. Dr Bromage is supported by a Medical Research Council (MRC) Clinician Scientist Fellowship (MR/X001881/1). Dr Cannata is supported by the British Heart Foundation (FS/CRTF/21/24175). The funder played no role in the study design, collection, analysis, interpretation of data, writing of the report, or in the decision to submit the paper for publication. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims: The COVID-19 pandemic disrupted the delivery of care for patients with heart failure (HF), leading to fewer HF hospitalizations and increased mortality. However, nationwide data on quality of care and long-term outcomes across the pandemic are scarce., Methods and Results: We used data from the National Heart Failure Audit (NHFA) linked to national records for hospitalization and deaths. We compared pre-COVID (2018-2019), COVID (2020), and late/post-COVID (2021-2022) periods. Data for 227 250 patients admitted to hospital with HF were analysed and grouped according to the admission year and the presence of HF with (HFrEF) or without reduced ejection fraction (non-HFrEF). The median age at admission was 81 years (interquartile range 72-88), 55% were men (n = 125 975), 87% were of white ethnicity (n = 102 805), and 51% had HFrEF (n = 116 990). In-hospital management and specialized cardiology care were maintained throughout the pandemic with an increasing percentage of patients discharged on disease-modifying medications over time (p < 0.001). Long-term outcomes improved over time (hazard ratio [HR] 0.92, 95% confidence interval [CI] 0.90-0.95, p < 0.001), mainly driven by a reduction in cardiovascular death. Receiving specialized cardiology care was associated with better long-term outcomes both for those who had HFrEF (HR 0.79, 95% CI 0.77-0.82, p < 0.001) and for those who had non-HFrEF (HR 0.87, 95% CI 0.85-0.90, p < 0.001)., Conclusions: Despite the disruption of healthcare systems, the clinical characteristics of patients admitted with HF were similar and the overall standard of care was maintained throughout the pandemic. Long-term survival of patients hospitalized with HF continued to improve after COVID-19, especially for HFrEF., (© 2024 The Author(s). European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Introduction: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data., Research Design and Methods: In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden., Results: Development : We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity : Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity : 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications : Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset., Conclusions: In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction., Competing Interests: Competing interests: JBM, HG and TM are employed by AstraZeneca UK, a biopharmaceutical company, and declare stock and stock options. AB has received research funding from AstraZeneca. KK has acted as a consultant, speaker or received grants for investigator-initiated studies for AstraZeneca, Bayer, Novartis, Novo Nordisk, Sanofi-Aventis, Lilly and Merck Sharp & Dohme, Boehringer Ingelheim, Oramed Pharmaceuticals, Roche and Applied Therapeutics., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Although chronic kidney disease (CKD) is associated with high multimorbidity, polypharmacy, morbidity and mortality, existing classification systems (mild to severe, usually based on estimated glomerular filtration rate, proteinuria or urine albumin-creatinine ratio) and risk prediction models largely ignore the complexity of CKD, its risk factors and its outcomes. Improved subtype definition could improve prediction of outcomes and inform effective interventions., Methods: We analysed individuals ≥18 years with incident and prevalent CKD (n = 350,067 and 195,422 respectively) from a population-based electronic health record resource (2006-2020; Clinical Practice Research Datalink, CPRD). We included factors (n = 264 with 2670 derived variables), e.g. demography, history, examination, blood laboratory values and medications. Using a published framework, we identified subtypes through seven unsupervised machine learning (ML) methods (K-means, Diana, HC, Fanny, PAM, Clara, Model-based) with 66 (of 2670) variables in each dataset. We evaluated subtypes for: (i) internal validity (within dataset, across methods); (ii) prognostic validity (predictive accuracy for 5-year all-cause mortality and admissions); and (iii) medications (new and existing by British National Formulary chapter)., Findings: After identifying five clusters across seven approaches, we labelled CKD subtypes: 1. Early-onset, 2. Late-onset, 3. Cancer, 4. Metabolic, and 5. Cardiometabolic. Internal validity: We trained a high performing model (using XGBoost) that could predict disease subtypes with 95% accuracy for incident and prevalent CKD (Sensitivity: 0.81-0.98, F1 score:0.84-0.97). Prognostic validity: 5-year all-cause mortality, hospital admissions, and incidence of new chronic diseases differed across CKD subtypes. The 5-year risk of mortality and admissions in the overall incident CKD population were highest in cardiometabolic subtype: 43.3% (42.3-42.8%) and 29.5% (29.1-30.0%), respectively, and lowest in the early-onset subtype: 5.7% (5.5-5.9%) and 18.7% (18.4-19.1%)., Medications: Across CKD subtypes, the distribution of prescription medication classes at baseline varied, with highest medication burden in cardiometabolic and metabolic subtypes, and higher burden in prevalent than incident CKD., Interpretation: In the largest CKD study using ML, to-date, we identified five distinct subtypes in individuals with incident and prevalent CKD. These subtypes have relevance to study of aetiology, therapeutics and risk prediction., Funding: AstraZeneca UK Ltd, Health Data Research UK., Competing Interests: Declaration of interests JBM, HG and TM are employed by AstraZeneca UK Ltd, a biopharmaceutical company and declare stock and stock options. Other authors report no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s).)

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., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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.

Chronic kidney disease (CKD) is associated with increased risk of baseline mortality and severe COVID-19, but analyses across CKD stages, and comorbidities are lacking. In prevalent and incident CKD, we investigated comorbidities, baseline risk, COVID-19 incidence, and predicted versus observed one-year excess death. In a national dataset (NHS Digital Trusted Research Environment [NHSD TRE]) for England encompassing 56 million individuals), we conducted a retrospective cohort study (March 2020 to March 2021) for prevalence of comorbidities by incident and prevalent CKD, SARS-CoV-2 infection and mortality. Baseline mortality risk, incidence and outcome of infection by comorbidities, controlling for age, sex and vaccination were assessed. Observed versus predicted one-year mortality at varying population infection rates and pandemic-related relative risks using our published model in pre-pandemic CKD cohorts (NHSD TRE and Clinical Practice Research Datalink [CPRD]) were compared. Among individuals with CKD (prevalent:1,934,585, incident:144,969), comorbidities were common (73.5% and 71.2% with one or more condition[s] in respective data sets, and 13.2% and 11.2% with three or more conditions, in prevalent and incident CKD), and associated with SARS-CoV-2 infection, particularly dialysis/transplantation (odds ratio 2.08, 95% confidence interval 2.04-2.13) and heart failure (1.73, 1.71-1.76), but not cancer (1.01, 1.01-1.04). One-year all-cause mortality varied by age, sex, multi-morbidity and CKD stage. Compared with 34,265 observed excess deaths, in the NHSD-TRE and CPRD databases respectively, we predicted 28,746 and 24,546 deaths (infection rates 10% and relative risks 3.0), and 23,754 and 20,283 deaths (observed infection rates 6.7% and relative risks 3.7). Thus, in this largest, national-level study, individuals with CKD have a high burden of comorbidities and multi-morbidity, and high risk of pre-pandemic and pandemic mortality. Hence, treatment of comorbidities, non-pharmaceutical measures, and vaccination are priorities for people with CKD and management of long-term conditions is important during and beyond the pandemic., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework., Methods: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status., Findings: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1., Interpretation: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources., Funding: British Heart Foundation Data Science Centre, led by Health Data Research UK., Competing Interests: Declaration of interests AB reports grants from the National Institute for Health Research (NIHR), British Medical Association, AstraZeneca, and UK Research and Innovation, outside the submitted work. BAM is an employee of the Wellcome Trust and reports grants from Health Data Research UK (HDR UK), UK Medical Research Council (MRC), and Diabetes UK. SH works as a data scientist and data curator for NHS Digital, which holds and processes the data. MAM is supported by research funding from AstraZeneca, outside the submitted work. AH is employed by Institute of Health Informatics, University College London. CS reports grants from the Wellcome Trust, MRC, HDR UK, University of Edinburgh, UK Research and Innovation (UKRI), and the BHF, outside the submitted work; participates on the data safety monitoring board for TARDIS; and has leadership or fiduciary roles with Cancer Research UK Early Detection and Diagnosis Research Committee, UKRI Expert Review Panel for Longitudinal Health & Wellbeing National Core Study, NIHR/UKRI Long COVID call Funding Review Panel, Accelerated Access Collaborative/NIHR/NHSX Artificial Intelligence (AI) in Healthcare Awards Funding Panel, Wellcome Trust Biomedical Resources Award Funding Panel, MRC strategic review Advisory Group for Maximising the opportunities from data science for innovative biomedical research, MRC Data Science Strategy Advisory Group, UKRI Digital Health Research and Innovation Strategy Expert Group, MRC Strategic Review of Units and Centres Main Panel & Population Heath Panel, Wellcome Trust Science Funding Review External advisory group, MRC Methodology Research (Better Methods Better Research) Panel, REF 2021 Subpanel - Public Health, Health Services and Primary Care, Longitudinal Health & Wellbeing COVID-19 National Core Study Strategic Advisory Board, UK Government Clinical Research Recovery Resilience and Growth programme Clinical Trials Expert Group, UK Government Scientific Advisory Group & SAGE Task and Finish Advisory Group on mass population testing for COVID-19, Scottish Government Covid-19 Data Taskforce, BHF Data Science Centre Steering Group, Our Future Health Scientific Advisory Board, Imperial College UKRI Centre for Doctoral Training in AI for Healthcare External advisory board, Swansea University UKRI Centre for Doctoral Training in AI, Machine Learning & Advanced Computing External advisory board, HDR UK Science Strategy Board / Science and Infrastructure Delivery Group, University of Bristol MRC Integrative Epidemiology Unit Scientific Advisory Board, International evaluation panel for Danish National Biobank, H2020 IMI ROADMAP Steering Committee, and the STAT-PD Steering Committee. NS reports grants from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics; and has received consulting fees from Afimmune, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, and Sanofi, outside of the submitted work. WNW is supported by a Scottish senior clinical fellowship, Chief Scientist Office (SCAF/17/01), and the Stroke Association (SA CV 20\100018), has received consulting fees from Bayer; payment for expert testimony from UK courts; participates on the data safety monitoring or advisory board for PROTECT-U, CATIS, INTERACT-4, MOSES, and Bayer; has leadership of fiduciary roles with BIASP Scientific Committee; and is associate editor of Stroke. SD has received research funding from GlaxoSmithKline, Astra Zeneca, Bayer, and BenevolentAI. All other authors declare no competing interests, (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: To evaluate antithrombotic (AT) use in individuals with atrial fibrillation (AF) and at high risk of stroke (CHA 2 DS 2 -VASc score ≥2) and investigate whether pre-existing AT use may improve COVID-19 outcomes., Methods: Individuals with AF and CHA 2 DS 2 -VASc score ≥2 on 1 January 2020 were identified using electronic health records for 56 million people in England and were followed up until 1 May 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 in individuals with pre-existing AT use versus no AT use, anticoagulants (AC) versus antiplatelets (AP), and direct oral anticoagulants (DOACs) versus warfarin., Results: From 972 971 individuals with AF (age 79 (±9.3), female 46.2%) and CHA 2 DS 2 -VASc score ≥2, 88.0% (n=856 336) had pre-existing AT use, 3.8% (n=37 418) had a COVID-19 hospitalisation and 2.2% (n=21 116) died, followed up to 1 May 2021. 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, 95% CI 0.87 to 0.96), but higher odds of hospitalisation (OR=1.20, 95% CI 1.15 to 1.26). AC versus AP was associated with lower odds of death (OR=0.93, 95% CI 0.87 to 0.98) and higher hospitalisation (OR=1.17, 95% CI 1.11 to 1.24). For DOACs versus warfarin, lower odds were observed for hospitalisation (OR=0.86, 95% CI 0.82 to 0.89) but not for death (OR=1.00, 95% CI 0.95 to 1.05)., Conclusions: Pre-existing AT use may be associated with lower odds of COVID-19 death and, while not evidence of causality, provides further incentive to improve AT coverage for eligible individuals with AF., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Background: Records of medication prescriptions can be used in conjunction with pharmacy dispensing records to investigate the incidence of adherence, which is defined as observing the treatment plans agreed between a patient and their clinician. Using prescribing records alone fails to identify primary non-adherence; medications not being collected from the dispensary. Using dispensing records alone means that cases of conditions that resolve and/or treatments that are discontinued will be unaccounted for. While using a linked prescribing and dispensing dataset to measure medication non-adherence is optimal, this linkage is not routinely conducted. Furthermore, without a unique common event identifier, linkage between these two datasets is not straightforward., Methods: We undertook a secondary analysis of the Salford Lung Study dataset. A novel probabilistic record linkage methodology was developed matching asthma medication pharmacy dispensing records and primary care prescribing records, using semantic (meaning) and syntactic (structure) harmonization, domain knowledge integration, and natural language feature extraction. Cox survival analysis was conducted to assess factors associated with the time to medication dispensing after the prescription was written. Finally, we used a simplified record linkage algorithm in which only identical records were matched, for a naïve benchmarking to compare against the results of our proposed methodology., Results: We matched 83% of pharmacy dispensing records to primary care prescribing records. Missing data were prevalent in the dispensing records which were not matched - approximately 60% for both medication strength and quantity. A naïve benchmarking approach, requiring perfect matching, identified one-quarter as many matching prescribing records as our methodology. Factors associated with delay (or failure) to collect the prescribed medication from a pharmacy included season, quantity of medication prescribed, previous dispensing history and class of medication. Our findings indicate that over 30% of prescriptions issued were not collected from a dispensary (primary non-adherence)., Conclusions: We have developed a probabilistic record linkage methodology matching a large percentage of pharmacy dispensing records with primary care prescribing records for asthma medications. This will allow researchers to link datasets in order to extract information about asthma medication non-adherence.

Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).

Background: Comprehensive tobacco control policies can help to protect children from tobacco smoke exposure and associated adverse respiratory health consequences. We investigated the impact of England's 2015 regulation that prohibits smoking in a private vehicle with children present on changes in environmental tobacco smoke exposure and respiratory health in children., Methods: In this quasi-experimental study, we used repeated cross-sectional, nationally representative data from the Health Survey for England from Jan 1, 2008, to Dec 31, 2017, of children aged up to 15 years. We did interrupted time series logistic or ordinal regression analyses to assess changes in prevalence of self-reported respiratory conditions, prevalence of self-reported childhood tobacco smoke exposure (children aged 8-15 years only), and salivary cotinine levels (children aged 2 years or older) before and after implementation of the smoke-free private vehicle regulation on Oct 1, 2015. Children who were considered active smokers were excluded from the analyses of salivary cotinine levels. Our primary outcome of interest was self-reported current wheezing or asthma, defined as having medicines prescribed for these conditions. Analyses were adjusted for underlying time trends, quarter of year, sex, age, Index of Multiple Deprivation quintile, and urbanisation level., Findings: 21 096 children aged 0-15 years were included in our dataset. Implementation of the smoke-free private vehicle regulation was not associated with a demonstrable change in self-reported current wheezing or asthma (adjusted odds ratio 0·81, 95% CI 0·62-1·05; p=0·108; assessed in 13 369 children), respiratory conditions (1·02, 0·80-1·29; p=0·892; assessed in 17 006 children), or respiratory conditions probably affecting stamina, breathing, or fatigue (0·75, 0·47-1·19; p=0·220; assessed in 12 386 children). Self-reported tobacco smoke exposure and salivary cotinine levels generally decreased over the study period. There was no additional change in self-reported tobacco smoke exposure in cars among children aged 8-15 years following the legislation (0·77, 0·51-1·17; p=0·222; assessed in 5399 children). We observed a relative increase in the odds of children having detectable salivary cotinine levels post legislation (1·36, 1·09-1·71; p=0·0074; assessed in 7858 children) and levels were also higher (1·30, 1·04-1·62; p=0·020; ordinal variable). Despite introduction of the regulation, one in 20 children still reported being regularly exposed to tobacco smoke in cars and one in three still had detectable salivary cotinine levels., Interpretation: We found no demonstrable association between the implementation of England's smoke-free private vehicle regulation and changes in children's self-reported tobacco smoke exposure or respiratory health. There is an urgent need to develop more effective approaches to protect children from tobacco smoke in various places, including in private vehicles., Funding: Netherlands Lung Foundation, Erasmus MC, Farr Institute, Health Data Research UK, Asthma UK Centre for Applied Research, Academy of Medical Sciences, and Newton Fund., (Copyright © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Collecting data on the localization of users is a key issue for the MASK (Mobile Airways Sentinel networK: the Allergy Diary) App. Data anonymization is a method of sanitization for privacy. The European Commission's Article 29 Working Party stated that geolocation information is personal data.To assess geolocation using the MASK method and to compare two anonymization methods in the MASK database to find an optimal privacy method., Methods: Geolocation was studied for all people who used the Allergy Diary App from December 2015 to November 2017 and who reported medical outcomes. Two different anonymization methods have been evaluated: Noise addition (randomization) and k-anonymity (generalization)., Results: Ninety-three thousand one hundred and sixteen days of VAS were collected from 8535 users and 54,500 (58.5%) were geolocalized, corresponding to 5428 users. Noise addition was found to be less accurate than k-anonymity using MASK data to protect the users' life privacy., Discussion: k-anonymity is an acceptable method for the anonymization of MASK data and results can be used for other databases., Competing Interests: No ethical committee was required for the study.Not needed. JB reports personal fees and other from Chiesi, Cipla, Hikma, Menarini, Mundipharma, Mylan, Novartis, Sanofi-Aventis, Takeda, Teva, Uriach, other from Kyomed, outside the submitted work.RA reports grants from North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL2020 Partnership Agreement and through the European Regional Development Fund (ERDF), during the conduct of the study.AC reports grants and personal fees from GlaxoSmithKline, personal fees from Boehringer Ingelheim, MEDA Pharma, CHIESI, AstraZeneca, om Merck, Sharp & Dohme, Novartis EUROFARMA outside the submitted work.H reports personal fees from Munidpharma, Novartis, OrionPharma, outside the submitted work. PK reports personal fees from Adamed, Boehringer Ingelheim, AstraZeneca, Chiesi, FAES, Berlin Chemie, Novartis, Polpharma, Allergopharma, outside the submitted work.V.K has received payment for consultancy from GSK and for lectures from StallergensGreer, Berlin-CHemie outsaide the submitted work.LL reports personal fees from MSD, Pfizer, GSK, Astrazeneca, MEDA, Boehringer Ingelheim, Novartis, Grunenthal, UCB, TEVA, Amstrong, Siegfried, DBV Technologies, grants from Sanofi, Pfizer, Astrazeneca, Novartis, UCB, outside the submitted work.RM reports personal fees from ALK, allergopharma, Allergy Therapeutics, Bayer, Friulchem, FAES, GSK, Hexal, Servier, Klosterfrau, MSD, Johnson&Johnson, Meda, Stada, UCB, Nuvo; grants and personal fees from Bencard, Stallergenes; grants from Leti, Optima, ASIT biotech, BitopAG, Hulka, Ursapharm; grants, personal fees and non-financial support from Lofarma; non-financial support from Atmos, Roxall, Bionorica, Otonomy, Ferrero; personal fees and non-financial support from Novartis; outside the submitted work.NGP reports personal fees from Abbvie Novartis, Faes Farma, BIOMAY, HAL, Nutricia Research, Menarini, Novartis, MEDA, MSD, Omega Pharma, Danone, grants from Menarini, outside the submitted work.0P reports grants and personal fees from ALK-Abelló, Allergopharma, Stallergenes Greer, HAL Allergy Holding B.V./HAL Allergie GmbH, Bencard Allergie GmbH/Allergy Therapeutics, Lofarma, Biotech Tools S.A., Laboratorios LETI/LETI Pharma, Anergis S.A., grants from Biomay, Nuvo, Circassia, Glaxo Smith Kline, personal fees from Novartis Pharma, MEDA Pharma, Mobile Chamber Experts (a GA2LEN Partner), Pohl-Boskamp, Indoor Biotechnologies, grants from, outside the submitted work.RS reports grants from São Paulo Research Foundation, MSD, grants and personal fees from Novartis, grants, personal fees and non-financial support from AstraZeneca, Chiesi, personal fees and non-financial support from Boheringer Ingelheim, outside the submitted work.TBAM reports grants and personal fees from Novartis, Boehringer Ingelheim, Mundipharma, GSK (GlaxoSmithKline), personal fees from Teva Pharma, AstraZeneca, grants from Leti, outside the submitted work. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Health organisations in Turkey gather a vast amount of valuable individual data that can be used for public health purposes. The organisations use rigid methods to remove some useful details from the data while publishing the rest of the data in a highly aggregated form, mostly because of privacy concerns and lack of standardised policies. This action leads to information loss and bias affecting public health research. Hence, organisations need dynamic policies and well-defined procedures rather than a specific algorithm to protect the privacy of individual data. To address this need, we developed a framework for the systematic application of anonymity methods while reducing and objectively reporting the information loss without leaking confidentiality. This framework acts as a roadmap for policymaking by providing high-level pseudo-policies with semitechnical guidelines in addition to some sample scenarios suitable for policymakers, public health programme managers and legislators., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Paper-based privacy policies fail to resolve the new changes posed by electronic healthcare. Protecting patient privacy through electronic systems has become a serious concern and is the subject of several recent studies. The shift towards an electronic privacy policy introduces new ethical challenges that cannot be solved merely by technical measures. Structured Patient Privacy Policy (S3P) is a software tool assuming an automated electronic privacy policy in an electronic healthcare setting. It is designed to simulate different access levels and rights of various professionals involved in healthcare in order to assess the emerging ethical problems. The authors discuss ethical issues concerning electronic patient privacy policies that have become apparent during the development and application of S3P.