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Objectives Obesity is a modifiable risk factor for coronavirus disease 2019 (COVID-19)–related mortality. We estimated excess mortality in obesity, both ‘direct’, through infection, and ‘indirect’, through changes in health care, and also due to potential increasing obesity during lockdown. Study design The study design of this study is a retrospective cohort study and causal inference methods. Methods In population-based electronic health records for 1,958,638 individuals in England, we estimated 1-year mortality risk (‘direct’ and ‘indirect’ effects) for obese individuals, incorporating (i) pre-COVID-19 risk by age, sex and comorbidities, (ii) population infection rate and (iii) relative impact on mortality (relative risk [RR]: 1.2, 1.5, 2.0 and 3.0). Using causal inference models, we estimated impact of change in body mass index (BMI) and physical activity during 3-month lockdown on 1-year incidence for high-risk conditions (cardiovascular diseases, diabetes, chronic obstructive pulmonary disease and chronic kidney disease), accounting for confounders. Results For severely obese individuals (3.5% at baseline), at 10% population infection rate, we estimated direct impact of 240 and 479 excess deaths in England at RR 1.5 and 2.0, respectively, and indirect effect of 383–767 excess deaths, assuming 40% and 80% will be affected at RR = 1.2. Owing to BMI change during the lockdown, we estimated that 97,755 (5.4%: normal weight to overweight, 5.0%: overweight to obese and 1.3%: obese to severely obese) to 434,104 individuals (15%: normal weight to overweight, 15%: overweight to obese and 6%: obese to severely obese) would be at higher risk for COVID-19 over one year. Conclusions Prevention of obesity and promotion of physical activity are at least as important as physical isolation of severely obese individuals during the pandemic., Highlights • In severely obese individuals, we estimated a direct impact of 240–479 excess deaths in England and indirect effect of 383–767 excess deaths over 1 year, assuming 10% infection rate. • Between 97 755 and 434 104 individuals may develop high-risk conditions for coronavirus disease 2019 (COVID-19) mortality during a 3-month lockdown due to change in body mass index and physical activity. • These analyses support COVID-19 and non-COVID-19 impact assessment in policy planning. • The implications of distancing and isolation measures on incidence and mortality from chronic diseases, particularly relating to obesity, need to be considered in clinical practice, public health and research.

ObjectivesTo compare treatment and outcomes for patients admitted to hospital with a primary diagnosis of ST elevation or non-ST elevation myocardial infarction (STEMI or NSTEMI) in six high income countries with very different healthcare delivery systems.DesignRetrospective cross sectional cohort study.SettingPatient level administrative data from the United States, Canada (Ontario and Manitoba), England, the Netherlands, Israel, and Taiwan.ParticipantsAdults aged 66 years and older admitted to hospital with STEMI or NSTEMI between 1 January 2011 and 31 December 2017.Outcomes measuresThe three categories of outcomes were coronary revascularisation (percutaneous coronary intervention or coronary artery bypass graft surgery), mortality, and efficiency (hospital length of stay and 30 day readmission). Rates were standardised to the age and sex distribution of the US acute myocardial infarction population in 2017. Outcomes were assessed separately for STEMI and NSTEMI. Performance was evaluated longitudinally (over time) and cross sectionally (between countries).ResultsThe total number of hospital admissions ranged from 19 043 in Israel to 1 064 099 in the US. Large differences were found between countries for all outcomes. For example, the proportion of patients admitted to hospital with STEMI who received percutaneous coronary intervention in hospital during 2017 ranged from 36.9% (England) to 78.6% (Canada; 71.8% in the US); use of percutaneous coronary intervention for STEMI increased in all countries between 2011 and 2017, with particularly large rises in Israel (48.4-65.9%) and Taiwan (49.4-70.2%). The proportion of patients with NSTEMI who underwent coronary artery bypass graft surgery within 90 days of admission during 2017 was lowest in the Netherlands (3.5%) and highest in the US (11.7%). Death within one year of admission for STEMI in 2017 ranged from 18.9% (Netherlands) to 27.8% (US) and 32.3% (Taiwan). Mean hospital length of stay in 2017 for STEMI was lowest in the Netherlands and the US (5.0 and 5.1 days) and highest in Taiwan (8.5 days); 30 day readmission for STEMI was lowest in Taiwan (11.7%) and the US (12.2%) and highest in England (23.1%).ConclusionsIn an analysis of myocardial infarction in six high income countries, all countries had areas of high performance, but no country excelled in all three domains. Our findings suggest that countries could learn from each other by using international comparisons of patient level nationally representative data.

BackgroundReliable identification of heart failure (HF) subtypes might allow targeted management. Machine learning (ML) has been used to explore HF subtypes, but neither across large, independent, population-based datasets, nor across the full spectrum of causes and presentations, nor with clinical and non-clinical validation by different ML methods. Using our published framework, we identified and validated HF subtypes to address these gaps.MethodsWe analysed individuals ≥30 years with incident HF from two population-based electronic health records resources (1998-2018; Clinical Practice Research Datalink, CPRD: n=188,799 HF cases; The Health Improvement Network, THIN: n=124,263 HF cases). Pre-and post-HF factors (n=645) included demography, history, examination, blood laboratory values and medications. We identified subtypes using four unsupervised ML methods (K-means, hierarchical, K-Medoids and mixture model clustering) with 87 (from 645) factors in each dataset. We evaluated subtypes for: (i) external validity (across independent datasets); (ii) prognostic validity (predictive accuracy for 1-year mortality); and (iii) uniquely, genetic validity (in UK Biobank; n=9573 cases): association with polygenic risk score (PRS) for 11 HF related traits, and direct association with 12 reported HF single nucleotide polymorphisms (SNPs).FindingsAfter identifying five clusters, we labelled HF subtypes: 1.Early-onset, 2.Late-onset, 3.AF-related, 4.Metabolic, and 5.Cardiometabolic. External validity: Subtypes were similar across datasets (c-statistic: 0.94, 0.80, 0.79, 0.83, 0.92 for the THIN model in CPRD and 0.79, 0.92, 0.90, 0.89, 0.92 for the CPRD model in THIN for subtypes 1-5, respectively). Prognostic validity: One-year all-cause mortality, risk of non-fatal cardiovascular diseases and all-cause hospitalisation (before and after HF diagnosis) differed across subtypes in CPRD and THIN data. Genetic validity: The AF-related subtype showed associations with PRS for related traits. Late-onset and Cardiometabolic subtypes were most comparable and strongly associated with PRS for Hypertension, Myocardial Infarction and Obesity (p-value < 9.09 × 10−4). We developed a prototype for clinical use, which could enable evaluation of effectiveness and cost-effectiveness.InterpretationAcross four methods and three datasets, and including genetic data, in the largest HF study to-date, ML algorithms identified five subtypes in individuals with incident HF. These subtypes may inform aetiologic research, clinical risk prediction and the design of HF trials.FundingEuropean Union Innovative Medicines Initiative.Research in contextEvidence before this studyIn a systematic review until December 2019, we showed that studies of machine learning in subtyping and risk prediction in cardiovascular diseases are limited by small population size, relatively few factors and poor generalisability of findings due to lack of external validation. We further searched PubMed, medRxiv, bioRxiv, arXiv, for relevant peer-reviewed articles and preprints, focusing on machine learning studies in heart failure. Studies remain focused on single diseases, limited risk factors, often single method of machine learning, rarely use subtyping and risk prediction together, and have not been externally validated across datasets. For heart failure, all subtype discovery studies have identified subtypes based on clustering, but so far with no application to clinical practice.Added value of this studyAcross two independent, population-based datasets, we used four machine learning methods for subtyping and risk prediction with 89 aetiologic factors as well as 556 further factors for heart failure. We identified and validated five subtypes in incident heart failure, which differentially predicted outcomes. In addition, we externally validated clinical cluster differences by exploring corresponding genetic differences in a large-scale genetic cohort. Our methods and results highlight potential value of electronic health records and machine learning in understanding disease subtypes. Moreover, our approach to external, prognostic, and genetic validity provides a framework for validation of machine learning approaches for disease subtype discovery.Implications of all the available evidenceOur analyses support coordinated use of large-scale, linked electronic health records to identify and validate disease subtypes with relevance for clinical risk prediction, patient selection for trials, and future genetic research.

Aims Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both ‘direct’, through infection, and ‘indirect’, through changes in healthcare. Methods and results We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0). Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60–100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2–3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths. Conclusion Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic., Graphical Abstract Graphical Abstract

Patients and public have sought mortality risk information throughout the pandemic, but their needs may not be served by current risk prediction tools. Our mixed methods study involved: (1) systematic review of published risk tools for prognosis, (2) provision and patient testing of new mortality risk estimates for people with high-risk conditions and (3) iterative patient and public involvement and engagement with qualitative analysis. Only one of 53 (2%) previously published risk tools involved patients or the public, while 11/53 (21%) had publicly accessible portals, but all for use by clinicians and researchers. Among people with a wide range of underlying conditions, there has been sustained interest and engagement in accessible and tailored, pre- and postpandemic mortality information. Informed by patient feedback, we provide such information in ‘five clicks’ (https://covid19-phenomics.org/OurRiskCoV.html), as context for decision making and discussions with health professionals and family members. Further development requires curation and regular updating of NHS data and wider patient and public engagement.

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.

Funder: Department of Health, Funder: Medical Research Council, BackgroundTargeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).MethodsIn this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions.FindingsWe included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.InterpretationA radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.FundingThe British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.

ObjectiveElectronic health records (EHRs) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems, and collected for purposes other than medical research. We describe an approach for developing, validating, and sharing reproducible phenotypes from national structured EHR in the United Kingdom with applications for translational research.Materials and MethodsWe implemented a rule-based phenotyping framework, with up to 6 approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population-based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements (for example, blood pressure; medication information; coded diagnoses, symptoms, procedures, and referrals), recorded using 5 controlled clinical terminologies: (1) read (primary care, subset of SNOMED-CT [Systematized Nomenclature of Medicine Clinical Terms]), (2) International Classification of Diseases–Ninth Revision and Tenth Revision (secondary care diagnoses and cause of mortality), (3) Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, Fourth Revision (hospital surgical procedures), and (4) DM+D prescription codes.ResultsUsing the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers, and lifestyle risk factors and provide up to 6 validation approaches. The EHR phenotypes are curated in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national and international research groups in 60 peer-reviewed publications.ConclusionsWe describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step toward international use of UK EHR data for health research.

BackgroundAdult obesity prevention policies, which are largely untargeted, have met with limited success globally. Population groups with the highest risk of weight gain, if they could be reliably identified using readily available information, might benefit from targeted policy. The relative importance of age, sex, ethnicity, geographical region and social deprivation for weight gain is unknown.MethodsWe calculated longitudinal changes in BMI over one, five and ten years and investigated transition between BMI categories using 11,187,383 clinically recorded, repeated measures of BMI from population-based electronic health records of 2,328,477 adults in England (1998-2016). The influence of risk factors was tested using logistic regression.FindingsThe youngest adult age group (18-24 years) was more strongly associated with risk of weight gain than older age, male sex, socioeconomic deprivation, ethnicity or geographic region. Among the youngest adults, the top quartile gained 15.9kg in men and 12kg in women at 10 years. The odds of transitioning to a higher BMI category over 10 years were 4-6 times higher in the youngest (18-24 years) compared to oldest (65-74 years) individuals; odds ratio (95% confidence interval) 4.22 (3.85-4.62)) from normal weight to overweight or obesity, 4.60 (4.06-5.22) for overweight to obesity, and 5.87 (5.23-6.59) from obesity to severe obesity in multiple adjusted analyses. Among the youngest adults, socially deprived men were at greater risk of transitioning from normal weight to overweight (72%) and from overweight to obesity (68%) over 10 years. We provide an open access online risk calculator (https://pasea.shinyapps.io/bmi_shiny_app/) and present high resolution obesity risk charts over a 1-, 5- and 10-year follow-up.InterpretationA radical shift in policy is required to focus on those at highest risk of weight gain-young adults-for individual and population level prevention of obesity and its long-term consequences for health and health care.FundingBHF, HDR-UK, MRC

ObjectivesTo estimate the impact of the COVID-19 pandemic on cancer care services and overall (direct and indirect) excess deaths in people with cancer.MethodsWe employed near real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services. We also used these data, together with national death registrations until June 2020 to model deaths, in excess of background (pre-COVID-19) mortality, in people with cancer. Background mortality risks for 24 cancers with and without COVID-19-relevant comorbidities were obtained from population-based primary care cohort (Clinical Practice Research Datalink) on 3 862 012 adults in England.ResultsDeclines in urgent referrals (median=−70.4%) and chemotherapy attendances (median=−41.5%) to a nadir (lowest point) in the pandemic were observed. By 31 May, these declines have only partially recovered; urgent referrals (median=−44.5%) and chemotherapy attendances (median=−31.2%). There were short-term excess death registrations for cancer (without COVID-19), with peak relative risk (RR) of 1.17 at week ending on 3 April. The peak RR for all-cause deaths was 2.1 from week ending on 17 April. Based on these findings and recent literature, we modelled 40% and 80% of cancer patients being affected by the pandemic in the long-term. At 40% affected, we estimated 1-year total (direct and indirect) excess deaths in people with cancer as between 7165 and 17 910, using RRs of 1.2 and 1.5, respectively, where 78% of excess deaths occured in patients with ≥1 comorbidity.ConclusionsDramatic reductions were detected in the demand for, and supply of, cancer services which have not fully recovered with lockdown easing. These may contribute, over a 1-year time horizon, to substantial excess mortality among people with cancer and multimorbidity. It is urgent to understand how the recovery of general practitioner, oncology and other hospital services might best mitigate these long-term excess mortality risks.

Background ESC and AHA guidelines identify 92 putative aetiological factors for heart failure, but primary prevention strategies for heart failure (HF) have had limited success. There are no previous studies systematically evaluating the potential for prevention among such a wide range of clinically manifest aetiologic factors. Objective To estimate for 92 (putative) aetiologic factors their prevalence, co-occurrence, preventability and impact on HF prognosis in incident HF. Methods We identified 170885 individuals aged ≥30 years with incident HF from 1997–2017 using linked primary and secondary care electronic health records (EHR) in the UK (CALIBER). For each of the 92 factors we developed EHR phenotypes using ICD-10 diagnosis, Read, procedure and medication codes (total = 5961 codes). We conducted a literature review of trials of primary HF prevention across 92 factors. Results 35.6% of individuals with HF had ≥4 aetiologic factors recorded in the preceding 5 years. Of all new HF cases 71.5% had ≥1 of the 7 aetiologic factors with trial evidence of effective preventive therapy for HF; 12.9% had ≥1 aetiologic factor with effective preventive therapy for CVD and 15.6% had either no aetiological factor, or an aetiological factor with no evidence of preventive potential. 88/91 factors were represented. The commonest factors in the 5 years prior to HF diagnosis were hypertension (48.7%), smoking (46.6%), reduced physical activity (39.6%), stable angina (35.6%), obesity (29.4%), atrial arrhythmias (17.3%), unstable angina (16.9%), cancer (16.6%), myocardial infarction (15.9%), diabetes (15.2%), alcohol (14.9%) and severe anaemia (14.3%). Mortality at 1 year varied across all 91 factors (lowest: pregnancy-related hormonal disorder – 4.2%; highest: phaeochromocytoma – 73.7%). Conclusion We provide a systematic map of primary preventive opportunities for heart failure, demonstrate the high burden of co-occurring aetiologic factors and highlight need for trials tackling multiple aetiological factors. Aetiologic factors of heart failure Funding Acknowledgement Type of funding source: Public grant(s) – EU funding. Main funding source(s): Innovative Medicines Initiative-2 Joint Undertaking

Background Obesity is a modifiable risk factor for coronavirus(COVID-19)-related mortality. We estimated excess mortality in obesity, both “direct”, through infection, and “indirect”, through changes in healthcare, and also due to potential increasing obesity during lockdown. Methods In population-based electronic health records for 1 958 638 individuals in England, we estimated 1-year mortality risk(“direct” and “indirect” effects) for obese individuals, incorporating: (i)pre-COVID-19 risk by age, sex and comorbidities, (ii)population infection rate, and (iii)relative impact on mortality(relative risk, RR: 1.2, 1.5, 2.0 and 3.0). Using causal inference models, we estimated impact of change in body-mass index(BMI) and physical activity during 3-month lockdown on 1-year incidence for high-risk conditions(cardiovascular diseases, CVD; diabetes; chronic obstructive pulmonary disease, COPD and chronic kidney disease, CKD), accounting for confounders. Findings For severely obese individuals (3.5% at baseline), at 10% population infection rate, we estimated direct impact of 240 and 479 excess deaths in England at RR 1.5 and 2.0 respectively, and indirect effect of 383 to 767 excess deaths, assuming 40% and 80% will be affected at RR=1.2. Due to BMI change during the lockdown, we estimated that 97 755 (5.4%: normal weight to overweight, 5.0%: overweight to obese and 1.3%: obese to severely obese) to 434 104 individuals (15%: normal weight to overweight, 15%: overweight to obese and 6%: obese to severely obese) individuals would be at higher risk for COVID-19 over one year. Interpretation Prevention of obesity and physical activity are at least as important as physical isolation of severely obese individuals during the pandemic. Research in context Evidence before this study We searched PubMed, medRxiv, bioRxiv, arXiv, and Wellcome Open Research for peer-reviewed articles, preprints, and research reports on obesity, excess mortality and change in body-mass index in the coronavirus disease 2019 (COVID-19), using the search terms “obesity”, “coronavirus”, “COVID-19”, and similar terms, and “mortality”, up to June 15, 2020. We found no prior studies of excess deaths in obese individuals due to COVID-19 pandemic, and no studies of long-term estimates or the relative impact of COVID-19 on mortality. Moreover, there were no studies of change in body-mass index during lockdown periods. Without these data, it is difficult to make specific recommendations in obese people at individual or population level during the pandemic. Added value of this study We estimated excess COVID-19-related mortality in severely obese individuals, targeted in physical distancing and isolation policies in UK government guidance. Assuming 10% infection rate, we estimated a direct impact of 240 to 479 excess deaths in England and indirect effect of 383 to 767 excess deaths. On the other hand, we estimated that between 97 755 and 434 104 individuals may develop high-risk conditions for COVID-19 mortality during a 3-month lockdown due to change in body-mass index and physical activity. Implications of all the available evidence These analyses support COVID-19 and non-COVID-19 impact assessment in policy planning during the pandemic. The implications of distancing and isolation measures on incidence and mortality from chronic diseases, particularly relating to obesity, needs to be considered in clinical practice, public health and research.

Background: Cancer and multiple non-cancer conditions are considered by the Centers for Disease Control and Prevention (CDC) as high risk conditions in the COVID-19 emergency. Professional societies have recommended changes in cancer service provision to minimize COVID-19 risks to cancer patients and health care workers. However, we do not know the extent to which cancer patients, in whom multi-morbidity is common, may be at higher overall risk of mortality as a net result of multiple factors including COVID-19 infection, changes in health services, and socioeconomic factors. Methods: We report multi-center, weekly cancer diagnostic referrals and chemotherapy treatments until April 2020 in England and Northern Ireland. We analyzed population-based health records from 3,862,012 adults in England to estimate 1-year mortality in 24 cancer sites and 15 non-cancer comorbidity clusters (40 conditions) recognized by CDC as high-risk. We estimated overall (direct and indirect) effects of COVID-19 emergency on mortality under different Relative Impact of the Emergency (RIE) and different Proportions of the population Affected by the Emergency (PAE). We applied the same model to the US, using Surveillance, Epidemiology, and End Results (SEER) program data. Results: Weekly data until April 2020 demonstrate significant falls in admissions for chemotherapy (45-66% reduction) and urgent referrals for early cancer diagnosis (70-89% reduction), compared to pre-emergency levels. Under conservative assumptions of the emergency affecting only people with newly diagnosed cancer (incident cases) at COVID-19 PAE of 40%, and an RIE of 1.5, the model estimated 6,270 excess deaths at 1 year in England and 33,890 excess deaths in the US. In England, the proportion of patients with incident cancer with ≥1 comorbidity was 65.2%. The number of comorbidities was strongly associated with cancer mortality risk. Across a range of model assumptions, and across incident and prevalent cancer cases, 78% of excess deaths occur in cancer patients with ≥1 comorbidity. Conclusion: We provide the first estimates of potential excess mortality among people with cancer and multimorbidity due to the COVID-19 emergency and demonstrate dramatic changes in cancer services. To better inform prioritization of cancer care and guide policy change, there is an urgent need for weekly data on cause-specific excess mortality, cancer diagnosis and treatment provision and better intelligence on the use of effective treatments for comorbidities.

Summary Background The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. Methods In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK–CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. Findings We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41–4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. Interpretation We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. Funding National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.

BackgroundThe medical, health service, societal and economic impact of the COVID-19 emergency has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom (to date at least) have underlying conditions. Models have not incorporated information on high risk conditions or their longer term background (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence rates and differing mortality impacts.MethodsUsing population based linked primary and secondary care electronic health records in England (HDR UK - CALIBER), we report the prevalence of underlying conditions defined by UK Public Health England COVID-19 guidelines (16 March 2020) in 3,862,012 individuals aged ≥30 years from 1997-2017. We used previously validated phenotypes, openly available (https://caliberresearch.org/portal), for each condition using ICD-10 diagnosis, Read, procedure and medication codes. We estimated the 1-year mortality in each condition, and developed simple models of excess COVID-19-related deaths assuming relative risk (RR) of the impact of the emergency (compared to background mortality) of 1.2, 1.5 and 2.0.Findings20.0% of the population are at risk according to current PHE guidelines, of which; 13.7% were age>70 years and 6.3% aged ≤70 years with ≥1 underlying condition (cardiovascular disease (2.3%), diabetes (2.2%), steroid therapy (1.9%), severe obesity (0.9%), chronic kidney disease (0.6%) and chronic obstructive pulmonary disease, COPD (0.5%). Multimorbidity (co-occurrence of ≥2 conditions in an individual) was common (10.1%). The 1-year mortality in the at-risk population was 4.46%, and age and underlying conditions combine to influence background risk, varying markedly across conditions (5.9% in age>70 years, 8.6% for COPD and 13.1% in those with ≥3 or more conditions). In a suppression scenario (at SARS CoV2 rates of 0.001% of the UK population), there would be minimal excess deaths (3 and 7 excess deaths at relative risk, RR, 1.5 and 2.0 respectively). At SARS CoV2 rates of 10% of the UK population (mitigation) the model estimates the numbers of excess deaths as: 13791, 34479 and 68957 (at RR 1.2, 1.5 and 2.0 respectively). At SARS CoV2 rates of 80% in the UK population (“do-nothing”), the model estimates the number of excess deaths as 110332, 275,830 and 551,659 (at RR 1.2, 1.5 and 2.0) respectively.InterpretationWe provide the public, researchers and policy makers a simple model to estimate the excess mortality over 1 year from COVID-19, based on underlying conditions at different ages. If the relative mortality impact of COVID-19 were to be about 20% (similar magnitude as the established winter vs summer mortality excess), then the excess deaths would be 0 when 1 in 100 000 (suppression), 13791 when 1 in 10 (mitigation) and 110332 when 8 in 10 are infected (“do nothing”) scenario. However, the relative impact of COVID-19 is unknown. If the emergency were to double the mortality risk, then we estimate 7, 68957 and 551,659 excess deaths in the same scenarios. These results may inform the need for more stringent suppression measures as well as efforts to target those at highest risk for a range of preventive interventions.

BackgroundCoronavirus (COVID-19) poses health system challenges in every country. As with any public health emergency, a major component of the global response is timely, effective science. However, particular factors specific to COVID-19 must be overcome to ensure that research efforts are optimised. We aimed to model the impact of COVID-19 on the clinical academic response in the UK, and to provide recommendations for COVID-related research.MethodsWe constructed a simple stochastic model to determine clinical academic capacity in the UK in four policy approaches to COVID-19 with differing population infection rates: “Italy model” (6%), “mitigation” (10%), “relaxed mitigation” (40%) and “do-nothing” (80%) scenarios. The ability to conduct research in the COVID-19 climate is affected by the following key factors: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics).FindingsIn “Italy model”, “mitigation”, “relaxed mitigation” and “do-nothing” scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, InterpretationPandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3-4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches.

ObjectiveElectronic health records are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems and collected for purposes other than medical research. We describe an approach for developing, validating and sharing reproducible phenotypes from national structured Electronic Health Records (EHR) in the UK with applications for translational research.Materials and MethodsWe implemented a rule-based phenotyping framework, with up to six approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements such as blood pressure, medication information and coded diagnoses, symptoms, procedures and referrals, recorded using five controlled clinical terminologies: a) Read (primary care, subset of SNOMED-CT), b) ICD-9, ICD-10 (secondary care diagnoses and cause of mortality), c) OPCS-4 (hospital surgical procedures) and d) Gemscript Drug Codes.ResultsThe open-access CALIBER Portal (https://www.caliberresearch.org/portal) demonstrates phenotyping algorithms for 50 diseases, syndromes, biomarkers and lifestyle risk factors and provides up to six validation layers. These phenotyping algorithms have been used by 40 national/international research groups in 60 peer-reviewed publications.ConclusionHerein, we describe the UK EHR phenomics approach, CALIBER, with initial evidence of validity and use, as an important step towards international use of UK EHR data for health research.

Purpose: The Stickler syndromes are the most common causes of inherited and childhood retinal detachment; however, no consensus exists regarding the effectiveness of prophylactic intervention. We evaluate the long-term safety and efficacy of the Cambridge prophylactic cryotherapy protocol, a standardized retinal prophylactic treatment developed to prevent retinal detachment arising from giant retinal tears in type 1 Stickler syndrome. Design: Retrospective comparative case series. Participants: Four hundred eighty seven patients with type 1 Stickler syndrome. Methods: Time to retinal detachment was compared between patients who received bilateral prophylaxis and untreated controls, with and without individual patient matching. Patients receiving unilateral prophylaxis (after fellow eye retinal detachment) were similarly compared with an appropriate control subgroup. Individual patient matching ensured equal age and follow-up between groups and that an appropriate control (who had not suffered a retinal detachment before the age at which their individually matched treatment patient underwent prophylactic treatment) was selected. Matching was blinded to outcome events. Individual patient matching protocols purposely weighted bias against the effectiveness of treatment. All treatment side effects are reported. Main Outcome Measures: Time to retinal detachment and side effects occurring after prophylactic treatment. Results: The bilateral control group (n ¼ 194) had a 7.4-fold increased risk of retinal detachment compared to the bilateral prophylaxis group (n ¼ 229) (hazard ratio [HR], 7.40; 95% confi dence interval [CI], 4.53e12.08; P

SummaryBackgroundSmall studies suggest peanut oral immunotherapy (OIT) might be effective in the treatment of peanut allergy. We aimed to establish the efficacy of OIT for the desensitisation of children with allergy to peanuts.MethodsWe did a randomised controlled crossover trial to compare the efficacy of active OIT (using characterised peanut flour; protein doses of 2–800 mg/day) with control (peanut avoidance, the present standard of care) at the NIHR/Wellcome Trust Cambridge Clinical Research Facility (Cambridge, UK). Randomisation (1:1) was by use of an audited online system; group allocation was not masked. Eligible participants were aged 7–16 years with an immediate hypersensitivity reaction after peanut ingestion, positive skin prick test to peanuts, and positive by double-blind placebo-controlled food challenge (DBPCFC). We excluded participants if they had a major chronic illness, if the care provider or a present household member had suspected or diagnosed allergy to peanuts, or if there was an unwillingness or inability to comply with study procedures. Our primary outcome was desensitisation, defined as negative peanut challenge (1400 mg protein in DBPCFC) at 6 months (first phase). Control participants underwent OIT during the second phase, with subsequent DBPCFC. Immunological parameters and disease-specific quality-of-life scores were measured. Analysis was by intention to treat. Fisher's exact test was used to compare the proportion of those with desensitisation to peanut after 6 months between the active and control group at the end of the first phase. This trial is registered with Current Controlled Trials, number ISRCTN62416244.FindingsThe primary outcome, desensitisation, was recorded for 62% (24 of 39 participants; 95% CI 45–78) in the active group and none of the control group after the first phase (0 of 46; 95% CI 0–9; p

The aim of this study is to develop models to aid the decision to prolong dual antiplatelet therapy (DAPT) that requires balancing an individual patient's potential benefits and harms.Using population-based electronic health records (EHRs) (CALIBER, England, 2000-10), of patients evaluated 1 year after acute myocardial infarction (MI), we developed (n = 12 694 patients) and validated (n = 5613) prognostic models for cardiovascular (cardiovascular death, MI or stroke) events and three different bleeding endpoints. We applied trial effect estimates to determine potential benefits and harms of DAPT and the net clinical benefit of individuals. Prognostic models for cardiovascular events (c-index: 0.75 (95% CI: 0.74, 0.77)) and bleeding (c index 0.72 (95% CI: 0.67, 0.77)) were well calibrated: 3-year risk of cardiovascular events was 16.5% overall (5.2% in the lowest- and 46.7% in the highest-risk individuals), while for major bleeding, it was 1.7% (0.3% in the lowest- and 5.4% in the highest-risk patients). For every 10 000 patients treated per year, we estimated 249 (95% CI: 228, 269) cardiovascular events prevented and 134 (95% CI: 87, 181) major bleeding events caused in the highest-risk patients, and 28 (95% CI: 19, 37) cardiovascular events prevented and 9 (95% CI: 0, 20) major bleeding events caused in the lowest-risk patients. There was a net clinical benefit of prolonged DAPT in 63-99% patients depending on how benefits and harms were weighted.Prognostic models for cardiovascular events and bleeding using population-based EHRs may help to personalise decisions for prolonged DAPT 1-year following acute MI.

Objective To estimate the potential magnitude in unselected patients of the benefits and harms of prolonged dual antiplatelet therapy after acute myocardial infarction seen in selected patients with high risk characteristics in trials. Design Observational population based cohort study. Setting PEGASUS-TIMI-54 trial population and CALIBER (ClinicAl research using LInked Bespoke studies and Electronic health Records). Participants 7238 patients who survived a year or more after acute myocardial infarction. Interventions Prolonged dual antiplatelet therapy after acute myocardial infarction. Main outcome measures Recurrent acute myocardial infarction, stroke, or fatal cardiovascular disease. Fatal, severe, or intracranial bleeding. Results 1676/7238 (23.1%) patients met trial inclusion and exclusion criteria (“target” population). Compared with the placebo arm in the trial population, in the target population the median age was 12 years higher, there were more women (48.6% v 24.3%), and there was a substantially higher cumulative three year risk of both the primary (benefit) trial endpoint of recurrent acute myocardial infarction, stroke, or fatal cardiovascular disease (18.8% (95% confidence interval 16.3% to 21.8%) v 9.04%) and the primary (harm) endpoint of fatal, severe, or intracranial bleeding (3.0% (2.0% to 4.4%) v 1.26% (TIMI major bleeding)). Application of intention to treat relative risks from the trial (ticagrelor 60 mg daily arm) to CALIBER’s target population showed an estimated 101 (95% confidence interval 87 to 117) ischaemic events prevented per 10 000 treated per year and an estimated 75 (50 to 110) excess fatal, severe, or intracranial bleeds caused per 10 000 patients treated per year. Generalisation from CALIBER’s target subgroup to all 7238 real world patients who were stable at least one year after acute myocardial infarction showed similar three year risks of ischaemic events (17.2%, 16.0% to 18.5%), with an estimated 92 (86 to 99) events prevented per 10 000 patients treated per year, and similar three year risks of bleeding events (2.3%, 1.8% to 2.9%), with an estimated 58 (45 to 73) events caused per 10 000 patients treated per year. Conclusions This novel use of primary-secondary care linked electronic health records allows characterisation of “healthy trial participant” effects and confirms the potential absolute benefits and harms of dual antiplatelet therapy in representative patients a year or more after acute myocardial infarction.

BackgroundPeanut allergy is a common disease that causes severe and fatal food allergic reactions. Currently, the best treatment is avoidance as repeated reactions can occur. Quality of life (QoL) is reduced by fear of severe reactions and social limitations. Oral immunotherapy (OIT) is a novel treatment that may be an effective treatment for peanut allergy.ObjectivesTo determine the efficacy of peanut OIT in children.DesignA phase 2 randomised, controlled, crossover trial (open label).SettingSingle UK centre study.ParticipantsChildren aged 7–15 years with peanut allergy diagnosed by double-blind, placebo-controlled food challenge (DBPCFC). No children were excluded because of anaphylaxis or asthma.InterventionsDaily immunotherapy (2 mg, 5 mg, 12.5 mg, 25 mg, 50 mg, 100 mg, 200 mg, 400 mg and 800 mg of peanut protein) was administered as peanut flour (containing 50% peanut protein). Doses were increased at 2-weekly intervals to a maintenance dose of 800 mg of protein. The control group underwent peanut avoidance for 6 months during phase 1.Main outcome measureA peanut DBPCFC up to 1400 mg of peanut protein was performed in both groups at 6 months. The highest amount of peanut tolerated was the main outcome measure.RandomisationRandomised by online audited system to active or control group (1 : 1).BlindingThe intervention arm allocation was not blinded.MethodsWe assigned 99 participants aged 7–16 years with peanut allergy of all severities to active OIT or control (peanut avoidance/current standard of care). The primary outcome was desensitisation, defined as negative peanut challenge (1400 mg of protein DBPCFC) at 6 months (phase 1). Control participants underwent OIT during phase 2, followed by DBPCFC. Immunological parameters and disease-specific QoL scores were measured.ResultsThe primary outcome, desensitisation, was observed in 62% (24/39) of the active group and none (0/46) of the control group after phase 1 [95% confidence interval (CI) 45% to 78% vs. 0% to 9%;p p p p ConclusionIn children with peanut allergy of any severity, OIT successfully induced desensitisation in the majority, with a clinically meaningful increase in peanut threshold. QoL improved after intervention and there was a good safety profile. Immunological changes reflected clinical desensitisation. Peanut OIT should not be undertaken in non-specialist settings. Future work will include a phase 3 confirmatory study and studies of long-term tolerance; similar studies of other allergens are also required.Trial registrationCurrent Controlled Trials ISRCTN62416244.FundingThis project was awarded by the Efficacy and Mechanism Evaluation programme and is funded by the Medical Research Council (MRC) and managed by the National Institute for Health Research (NIHR) on behalf of the MRC–NIHR partnership, and jointly sponsored by the University of Cambridge and Addenbrooke’s Hospital [Cambridge University Hospital Foundation Trust (RD authorisation A091686)]. The project will be published in full inEfficacy and Mechanism Evaluation; Vol. 1, No. 4. See the NIHR Journals Library website for further project information.

Ischemia/reperfusion injury (IRI) that develops after liver implantation may prejudice long-term graft survival, but it remains poorly understood. Here we correlate the severity of IRIs that were determined by histological grading of time-zero biopsies sampled after graft revascularization with patient and graft outcomes. Time-zero biopsies of 476 liver transplants performed at our center between 2000 and 2010 were graded as follows: nil (10.5%), mild (58.8%), moderate (26.1%), and severe (4.6%). Severe IRI was associated with donor age, donation after circulatory death, prolonged cold ischemia time, and liver steatosis, but it was also associated with increased rates of primary nonfunction (9.1%) and retransplantation within 90 days (22.7%). Longer term outcomes in the severe IRI group were also poor, with 1-year graft and patient survival rates of only 55% and 68%, respectively (cf. 90% and 93% for the remainder). Severe IRI on the time-zero biopsy was, in a multivariate analysis, an independent determinant of 1-year graft survival and was a better predictor of 1-year graft loss than liver steatosis, early graft dysfunction syndrome, and high first-week alanine aminotransferase with a positive predictive value of 45%. Time-zero biopsies predict adverse clinical outcomes after liver transplantation, and severe IRI upon biopsy signals the likely need for early retransplantation.

The Stickler syndromes are the most common causes of inherited and childhood retinal detachment; however, no consensus exists regarding the effectiveness of prophylactic intervention. We evaluate the long-term safety and efficacy of the Cambridge prophylactic cryotherapy protocol, a standardized retinal prophylactic treatment developed to prevent retinal detachment arising from giant retinal tears in type 1 Stickler syndrome.Retrospective comparative case series.Four hundred eighty seven patients with type 1 Stickler syndrome.Time to retinal detachment was compared between patients who received bilateral prophylaxis and untreated controls, with and without individual patient matching. Patients receiving unilateral prophylaxis (after fellow eye retinal detachment) were similarly compared with an appropriate control subgroup. Individual patient matching ensured equal age and follow-up between groups and that an appropriate control (who had not suffered a retinal detachment before the age at which their individually matched treatment patient underwent prophylactic treatment) was selected. Matching was blinded to outcome events. Individual patient matching protocols purposely weighted bias against the effectiveness of treatment. All treatment side effects are reported.Time to retinal detachment and side effects occurring after prophylactic treatment.The bilateral control group (n = 194) had a 7.4-fold increased risk of retinal detachment compared to the bilateral prophylaxis group (n = 229) (hazard ratio [HR], 7.40; 95% confidence interval [CI], 4.53-12.08; P0.001); the matched bilateral control group (n = 165) had a 5.0-fold increased risk compared to the matched bilateral prophylaxis group (n = 165) (HR, 4.97; 95% CI, 2.82-8.78; P0.001). The unilateral control group (n = 104) had a 10.3-fold increased risk of retinal detachment compared to the unilateral prophylaxis group (n = 64) (HR, 10.29; 95% CI, 4.96-21.36; P0.001); the matched unilateral control group (n = 39) had a 8.4-fold increased risk compared to the matched unilateral prophylaxis group (n = 39) (HR, 8.36; 95% CI, 3.24-21.57; P0.001). No significant long-term side effects occurred.In the largest global cohort of type 1 Stickler syndrome patients published, all analyses indicate that the Cambridge prophylactic cryotherapy protocol is safe and markedly reduces the risk of retinal detachment.

The risk of recurrent cardiovascular (CV) events and bleeding remains high in stable myocardial infarction (MI) survivors in the US, England, Sweden and France [Rapsomaniki 2014]. However there are no prognostic models to support risk stratification of this population. The study aim was to develop

Systolic blood pressure variability is an independent risk factor for mortality and cardiovascular events. Standard measures of blood pressure predict outcome poorly in haemodialysis patients. We investigated whether systolic blood pressure variability was associated with mortality in incident haemodialysis patients. We performed a longitudinal observational study of patients commencing haemodialysis between 2005 and 2011 in East Anglia, UK, excluding patients with cardiovascular events within 6 months of starting haemodialysis. The main exposure was variability independent of the mean (VIM) of systolic blood pressure from short-gap, pre-dialysis blood pressure readings between 3 and 6 months after commencing haemodialysis, and the outcome was all-cause mortality. Of 203 patients, 37 (18.2%) patients died during a mean follow-up of 2.0 (SD 1.3) years. The age and sex-adjusted hazard ratio (HR) for mortality was 1.09 (95% confidence interval (CI) 1.02-1.17) for a one-unit increase of VIM. This was not altered by adjustment for diabetes, prior cardiovascular disease and mean systolic blood pressure (HR 1.09, 95% CI 1.02-1.16). Patients with VIM of systolic blood pressure above the median were 2.4 (95% CI 1.17-4.74) times more likely to die during follow-up than those below the median. Results were similar for all measures of blood pressure variability and further adjustment for type of dialysis access, use of antihypertensives and absolute or variability of fluid intake did not alter these findings. Diastolic blood pressure variability showed no association with all cause mortality. Our study shows that variability of systolic blood pressure is a strong and independent predictor of all-cause mortality in incident haemodialysis patients. Further research is needed to understand the mechanism as this may form a therapeutic target or focus for management.