Search

Abstract Background Heart failure is a major global health challenge incurring a high rate of mortality, morbidity and hospitalisation. Effective medicines management at the time of hospital discharge into the community could reduce poor outcomes for people with heart failure. Within the Improving the Safety and Continuity Of Medicines management at Transitions of care (ISCOMAT) programme, the Medicines at Transitions Intervention (MaTI) was co-designed to improve such transitions, with a cluster randomised controlled trial to test effectiveness. The MaTI includes a patient toolkit and transfer of discharge medicines information to community pharmacy. This paper aims to determine the degree to which the intervention was delivered, and identify barriers and facilitators experienced by staff for the successful implementation of the intervention. Methods The study was conducted in six purposively selected intervention sites. A mixed-methods design was employed using hospital staff interviews, structured and unstructured ward observations, and routine trial data about adherence to the MaTI. A parallel mixed analysis was applied. Qualitative data were analysed thematically using the Framework method. Data were synthesised, triangulated and mapped to the Consolidated Framework for Implementation Research (CFIR). Results With limited routines of communication between ward staff and community pharmacy, hospital staff found implementing community pharmacy-related steps of the intervention challenging. Staff time was depleted by attempts to bridge system barriers, sometimes leading to steps not being delivered. Whilst the introduction of the patient toolkit was often completed and valued as important patient education and a helpful way to explain medicines, the medicines discharge log within it was not, as this was seen as a duplication of existing systems. Within the CFIR the most applicable constructs were identified as ‘intervention complexity’ and ‘cosmopolitanism’ based on how well hospitals were networked with community pharmacies, and the availability of hospital resources to facilitate this. Conclusion The MaTI was generally successfully implemented, particularly the introduction of the toolkit. However, implementation involving community pharmacy was more challenging and more effective communication systems are needed to support wider implementation. Trial registration 11/04/2018 ISRCTN66212970. https://www.isrctn.com/ISRCTN66212970 .

Background: Cardiovascular disease (CVD) causes a quarter of all deaths in the UK,1 and the NHS Long Term Plan aims for earlier detection and treatment of cardiovascular, renal and metabolic risk factors.2 We trained, tested and prospectively implemented a machine learning algorithm in primary care electronic health record (EHR) data to identify individuals at higher risk of incident cardio-renal-metabolic diseases and cardiovascular death.3, 4 Methods: We used UK primary care EHR data from 2,081,139 individuals aged ≥30 years (2 January 1998, 30 November 2018), randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities (OPTIMISE). We calculated the cumulative incidence rate for ten cardio-renal-metabolic diseases and death, and excluded individuals for the analysis of each disease who had a preceding diagnosis of that disease. Fine and Gray's models with competing risk of death were fit for each outcome between higher and lower predicted risk.We implemented OPTIMISE in a pilot interventional non-randomised single arm study across four primary care sites. Consenting individuals aged ≥30 years at higher predicted risk received community-based cardio-renal-metabolic phenotyping and assessment for guideline-adherence of current treatment. Results: In the testing dataset (n = 416,228), individuals at higher predicted risk had higher long-term risk of heart failure (HR 12.54, 95% CI 12.08–13.01), aortic stenosis (9.98, 9.16–10.87), AF (HR 8·75, 95% CI 8·44–9·06), stroke/TIA (8.07, 7.80–8.34), chronic kidney disease (CKD) (6.85, 6.70–7.00), peripheral vascular disease (6.62, 6.28–6.98), valvular heart disease (6.49, 6.14–6.85), MI (5.02, 4.82–5.22), diabetes (2.05, 2.00–2.10) and COPD (2.02, 2.00–2.05) (Figure 1). They were also at higher risk of death (10.45, 10.23–10.68), accounting for 74% of cardiovascular deaths (8,582/11,676).Of 82 higher risk patients in the pilot clinical implementation (mean age 71.6 years (SD 7.5), 50% women), 78.0% had hypertension and 37.8% had type 2 diabetes (Table 1). Of higher risk patients with hypertension, 58.5% (31/53) of those aged 140 mmHg, and 54.5% (6/11) of those aged ≥80 years had a SBP >150 mmHg. Of those with type 2 diabetes and co-existent CVD, only 23.1% (3/13) were on SGLT2 inhibitor therapy. Of higher risk patients on statin therapy, 37.0% (20/54) had LDL-cholesterol >1.8 mmol/L, and 23.1% (3/13) of patients with previous CVD had an LDL-cholesterol >2.0 mmol/L.Furthermore, 19.5% (16/82) of the higher risk cohort had undiagnosed moderate or high risk CKD. Those with unrecognised CKD were often not on a statin (41.7%; 5/12), ACEi/ARB therapy with co-existent hypertension (61.5%; 8/13), or SGLT2 inhibitor with co-existent diabetes (83.3%; 5/6). Almost half of the cohort (49%) were found to be obese, and 17% (14/82) were eligible for GLP-1 RA therapy. Conclusions: The machine learning OPTIMISE algorithm can identify people at higher risk of cardio-renal-metabolic diseases and death in UK primary care EHR data. On prospective evaluation higher risk individuals have unrecorded and undertreated cardio-renal-metabolic diseases, which are actionable targets for integrated multi-disciplinary preventative care.

BackgroundThe occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making.Methods and findingsThis nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only-as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI.ConclusionsIn this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study.

Introduction Atrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies.Methods and analysis We will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month prediction horizon, that is, a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states.Ethics and dissemination Permission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.Trial registration number A systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).

Objective To investigate the association between health-related quality of life (HRQoL) and major adverse cardiovascular and cerebrovascular events (MACCE) in individuals with ischaemic heart disease (IHD).Methods Medline(R), Embase, APA PsycINFO and CINAHL (EBSCO) from inception to 3 April 2023 were searched. Studies reporting association of HRQoL, using a generic or cardiac-specific tool, with MACCE or components of MACCE for individuals with IHD were eligible for inclusion. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale to assess the quality of the studies. Descriptive synthesis, evidence mapping and random-effects meta-analysis were performed stratified by HRQoL measures and effect estimates. Between-study heterogeneity was assessed using the Higgins I2 statistic.Results Fifty-one articles were included with a total of 134 740 participants from 53 countries. Meta-analysis of 23 studies found that the risk of MACCE increased with lower baseline HeartQoL score (HR 1.49, 95% CI 1.16 to 1.93) and Short Form Survey (SF-12) physical component score (PCS) (HR 1.39, 95% CI 1.28 to 1.51). Risk of all-cause mortality increased with a lower HeartQoL (HR 1.64, 95% CI 1.34 to 2.01), EuroQol 5-dimension (HR 1.17, 95% CI 1.12 to 1.22), SF-36 PCS (HR 1.29, 95% CI 1.19 to 1.41), SF-36 mental component score (HR 1.18, 95% CI 1.08 to 1.30).Conclusions This study found an inverse association between baseline values or change in HRQoL and MACCE or components of MACCE in individuals with IHD, albeit with between-study heterogeneity. Standardisation and routine assessment of HRQoL in clinical practice may help risk stratify individuals with IHD for tailored interventions.PROSPERO registration number CRD42021234638.

Introduction Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG monitoring across AF risk estimates and establish rates of recruitment and protocol adherence in a remote AF screening pathway.Methods and analysis The FIND-AF Pilot is an interventional, non-randomised, single-arm, open-label study that will recruit 1955 participants aged 30 years or older, without a history of AF and eligible for oral anticoagulation, identified as higher risk and lower risk by the FIND-AF risk score from their primary care EHRs, to a period of remote ECG monitoring with a Zenicor-ECG device. The primary outcome is AF diagnosis during ECG monitoring, and secondary outcomes include recruitment rates, withdrawal rates, adherence to ECG monitoring and prescription of oral anticoagulation to participants diagnosed with AF during ECG monitoring.Ethics and dissemination The study has ethical approval (the North West—Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder’s open access policy.Trial registration number NCT05898165.

Objective Risk-guided atrial fibrillation (AF) screening may be an opportunity to prevent adverse events in addition to stroke. We compared events rates for new diagnoses of cardio-renal-metabolic diseases and death in individuals identified at higher versus lower-predicted AF risk.Methods From the UK Clinical Practice Research Datalink-GOLD dataset, 2 January 1998–30 November 2018, we identified individuals aged ≥30 years without known AF. The risk of AF was estimated using the FIND-AF (Future Innovations in Novel Detection of Atrial Fibrillation) risk score. We calculated cumulative incidence rates and fit Fine and Gray’s models at 1, 5 and 10 years for nine diseases and death adjusting for competing risks.Results Of 416 228 individuals in the cohort, 82 942 were identified as higher risk for AF. Higher-predicted risk, compared with lower-predicted risk, was associated with incident chronic kidney disease (cumulative incidence per 1000 persons at 10 years 245.2; HR 6.85, 95% CI 6.70 to 7.00; median time to event 5.44 years), heart failure (124.7; 12.54, 12.08 to 13.01; 4.06), diabetes mellitus (123.3; 2.05, 2.00 to 2.10; 3.45), stroke/transient ischaemic attack (118.9; 8.07, 7.80 to 8.34; 4.27), myocardial infarction (69.6; 5.02, 4.82 to 5.22; 4.32), peripheral vascular disease (44.6; 6.62, 6.28 to 6.98; 4.28), valvular heart disease (37.8; 6.49, 6.14 to 6.85; 4.54), aortic stenosis (18.7; 9.98, 9.16 to 10.87; 4.41) and death from any cause (273.9; 10.45, 10.23 to 10.68; 4.75). The higher-risk group constituted 74% of deaths from cardiovascular or cerebrovascular causes (8582 of 11 676).Conclusions Individuals identified for risk-guided AF screening are at risk of new diseases across the cardio-renal-metabolic spectrum and death, and may benefit from interventions beyond ECG monitoring.

Objectives To investigate sex-based differences in baseline values and longitudinal trajectories of health-related quality of life (HRQoL) in a large cohort of myocardial infarction (MI) survivors after adjusting for other important factors.Design Longitudinal cohort study.Setting Population-based longitudinal study the Evaluation of the Methods and Management of Acute Coronary Events study linked with national cardiovascular registry. Data were collected from 77 hospitals in England between 1 November 2011 and 24 June 2015.Participants 9551 patients with MI. Patients were eligible for the study if they were ≥18 years of age.Primary and secondary outcome measures HRQoL was measured by EuroQol five-dimension, visual analogue scale (EQ-5D, EQ VAS) survey at baseline, 1, 6 and 12 months after discharge. Multi-level linear and logistic regression models coupled with inverse probability weighted propensity scoring were used to evaluate sex differences in HRQoL following MI.Results Of the 9551 patients with MI and complete data on sex, 25.1% (2,397) were women. At baseline, women reported lower HRQoL (EQ VAS (mean (SD) 59.8 (20.4) vs 64.5 (20.9)) (median (IQR) 60.00 (50.00–75.00) vs 70.00 (50.00–80.00))) (EQ-5D (mean (SD) 0.66 (0.31) vs 0.74 (0.28)) (median (IQR) 0.73 (0.52–0.85) vs 0.81 (0.62–1.00))) and were more likely to report problems in each HRQoL domain compared with men. In the covariate balanced and adjusted multi-level model sex differences in HRQoL persisted during follow-up, with lower EQ VAS and EQ-5D scores in women compared with men (adjusted EQ VAS model sex coefficient: −4.41, 95% CI −5.16 to −3.66 and adjusted EQ-5D model sex coefficient: −0.07, 95% CI −0.08 to −0.06).Conclusions Women have lower HRQoL compared with men at baseline and during 12 months follow-up after MI. Tailored interventions for women following an MI could improve their quality of life.Trial registration number ClinicalTrials.gov (NCT04598048, NCT01808027, NCT01819103

Background: National clinical audit programmes aim to improve patient care by reviewing performance against explicit standards and directing action towards areas not meeting those standards. Their impact can be improved by (1) optimising feedback content and format, (2) strengthening audit cycles and (3) embedding randomised trials evaluating different ways of delivering feedback. Objectives: The objectives were to (1) develop and evaluate the effects of modifications to feedback on recipient responses, (2) identify ways of strengthening feedback cycles for two national audits and (3) explore opportunities, costs and benefits of national audit participation in a programme of trials. Design: An online fractional factorial screening experiment (objective 1) and qualitative interviews (objectives 2 and 3). Setting and participants: Participants were clinicians and managers involved in five national clinical audits – the National Comparative Audit of Blood Transfusions, the Paediatric Intensive Care Audit Network, the Myocardial Ischaemia National Audit Project, the Trauma Audit & Research Network and the National Diabetes Audit – (objective 1); and clinicians, members of the public and researchers (objectives 2 and 3). Interventions: We selected and developed six online feedback modifications through three rounds of user testing. We randomised participants to one of 32 combinations of the following recommended specific actions: comparators reinforcing desired behaviour change; multimodal feedback; minimised extraneous cognitive load for feedback recipients; short, actionable messages followed by optional detail; and incorporating ‘the patient voice’ (objective 1). Main outcome measures: The outcomes were intended actions, including enactment of audit standards (primary outcome), comprehension, user experience and engagement (objective 1). Results: For objective 1, the primary analysis included 638 randomised participants, of whom 566 completed the outcome questionnaire. No modification independently increased intended enactment of audit standards. Minimised cognitive load improved comprehension (+0.1; p = 0.014) and plans to bring audit findings to colleagues’ attention (+0.13, on a –3 to +3 scale; p = 0.016). We observed important cumulative synergistic and antagonistic interactions between modifications, participant role and national audit. The analysis in objective 2 included 19 interviews assessing the Trauma Audit Research Network and the National Diabetes Audit. The identified ways of strengthening audit cycles included making performance data easier to understand and guiding action planning. The analysis in objective 3 identified four conditions for effective collaboration from 31 interviews: compromise – recognising capacity and constraints; logistics – enabling data sharing, audit quality and funding; leadership – engaging local stakeholders; and relationships – agreeing shared priorities and needs. The perceived benefits of collaboration outweighed the risks. Limitations: The online experiment assessed intended enactment as a predictor of actual clinical behaviour. Interviews and surveys were subject to social desirability bias. Conclusions: National audit impacts may be enhanced by strengthening all aspects of feedback cycles, particularly effective feedback, and considering how different ways of reinforcing feedback act together. Future work: Embedded randomised trials evaluating different ways of delivering feedback within national clinical audits are acceptable and may offer efficient, evidence-based and cumulative improvements in outcomes. Trial registration: This trial is registered as ISRCTN41584028. Funding details: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 15. See the NIHR Journals Library website for further project information.

Background There are a paucity of randomised data on the optimal timing of invasive coronary angiography (ICA) in higher-risk patients with non-ST elevation myocardial infarction (N-STEMI). International guideline recommendations for early ICA are primarily based on retrospective subgroup analyses of neutral trials.Aims The RAPID N-STEMI trial aims to determine whether very early percutaneous revascularisation improves clinical outcomes as compared with a standard of care strategy in higher-risk N-STEMI patients.Methods and analysis RAPID N-STEMI is a prospective, multicentre, open-label, randomised-controlled, pragmatic strategy trial. Higher-risk N-STEMI patients, as defined by Global Registry of Acute Coronary Events 2.0 score ≥118, or >90 with at least one additional high-risk feature, were randomised to either: very early ICA±revascularisation or standard of care timing of ICA±revascularisation. The primary outcome is the proportion of participants with at least one of the following events (all-cause mortality, non-fatal myocardial infarction and hospital admission for heart failure) at 12 months. Key secondary outcomes include major bleeding and stroke. A hypothesis generating cardiac magnetic resonance (CMR) substudy will provide mechanistic data on infarct size, myocardial salvage and residual ischaemia post percutaneous coronary intervention. On 7 April 2021, the sponsor discontinued enrolment due to the impact of the COVID-19 pandemic and lower than expected event rates. 425 patients were enrolled, and 61 patients underwent CMR.Ethics and dissemination The trial has been reviewed and approved by the East of England Cambridge East Research Ethics Committee (18/EE/0222). The study results will be submitted for publication within 6 months of completion.Trial registration number NCT03707314; Pre-results.

Background: National audits aim to reduce variations in quality by stimulating quality improvement. However, varying provider engagement with audit data means that this is not being realised. Aim: The aim of the study was to develop and evaluate a quality dashboard (i.e. QualDash) to support clinical teams’ and managers’ use of national audit data. Design: The study was a realist evaluation and biography of artefacts study. Setting: The study involved five NHS acute trusts. Methods and results: In phase 1, we developed a theory of national audits through interviews. Data use was supported by data access, audit staff skilled to produce data visualisations, data timeliness and quality, and the importance of perceived metrics. Data were mainly used by clinical teams. Organisational-level staff questioned the legitimacy of national audits. In phase 2, QualDash was co-designed and the QualDash theory was developed. QualDash provides interactive customisable visualisations to enable the exploration of relationships between variables. Locating QualDash on site servers gave users control of data upload frequency. In phase 3, we developed an adoption strategy through focus groups. ‘Champions’, awareness-raising through e-bulletins and demonstrations, and quick reference tools were agreed. In phase 4, we tested the QualDash theory using a mixed-methods evaluation. Constraints on use were metric configurations that did not match users’ expectations, affecting champions’ willingness to promote QualDash, and limited computing resources. Easy customisability supported use. The greatest use was where data use was previously constrained. In these contexts, report preparation time was reduced and efforts to improve data quality were supported, although the interrupted time series analysis did not show improved data quality. Twenty-three questionnaires were returned, revealing positive perceptions of ease of use and usefulness. In phase 5, the feasibility of conducting a cluster randomised controlled trial of QualDash was assessed. Interviews were undertaken to understand how QualDash could be revised to support a region-wide Gold Command. Requirements included multiple real-time data sources and functionality to help to identify priorities. Conclusions: Audits seeking to widen engagement may find the following strategies beneficial: involving a range of professional groups in choosing metrics; real-time reporting; presenting ‘headline’ metrics important to organisational-level staff; using routinely collected clinical data to populate data fields; and dashboards that help staff to explore and report audit data. Those designing dashboards may find it beneficial to include the following: ‘at a glance’ visualisation of key metrics; visualisations configured in line with existing visualisations that teams use, with clear labelling; functionality that supports the creation of reports and presentations; the ability to explore relationships between variables and drill down to look at subgroups; and low requirements for computing resources. Organisations introducing a dashboard may find the following strategies beneficial: clinical champion to promote use; testing with real data by audit staff; establishing routines for integrating use into work practices; involving audit staff in adoption activities; and allowing customisation. Limitations: The COVID-19 pandemic stopped phase 4 data collection, limiting our ability to further test and refine the QualDash theory. Questionnaire results should be treated with caution because of the small, possibly biased, sample. Control sites for the interrupted time series analysis were not possible because of research and development delays. One intervention site did not submit data. Limited uptake meant that assessing the impact on more measures was not appropriate. Future work: The extent to which national audit dashboards are used and the strategies national audits use to encourage uptake, a realist review of the impact of dashboards, and rigorous evaluations of the impact of dashboards and the effectiveness of adoption strategies should be explored. Study registration: This study is registered as ISRCTN18289782. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 12. See the NIHR Journals Library website for further project information.

BackgroundDeaths in the first year of the Coronavirus Disease 2019 (COVID-19) pandemic in England and Wales were unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated to date, as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups.Methods and findingsWe used national mortality registers in England and Wales, from 27 December 2014 until 25 December 2020, covering 3,265,937 deaths. YLLs (main outcome) were calculated using 2019 single year sex-specific life tables for England and Wales. Interrupted time-series analyses, with panel time-series models, were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7 March 2020 and 25 December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease and diabetes, cancer, and other indirect deaths (all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group. Between 7 March 2020 and 25 December 2020, there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England and Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from 916 (95% CI: 820 to 1,012) for the least deprived quintile to 1,645 (95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, a mean of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, a mean of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. For all-cause mortality, estimated deaths in the most deprived compared to the most affluent areas were much higher in younger age groups, but similar for those aged 85 or over. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in the North West. Limitations include the quasi-experimental nature of the research design and the requirement for accurate and timely recording.ConclusionsIn this study, we observed strong socioeconomic and geographical health inequalities in YLL, during the first calendar year of the COVID-19 pandemic. These were in line with long-standing existing inequalities in England and Wales, with the most deprived areas reporting the largest numbers in potential YLL.

BackgroundDashboards can support data-driven quality improvements in health care. They visualize data in ways intended to ease cognitive load and support data comprehension, but how they are best integrated into working practices needs further investigation. ObjectiveThis paper reports the findings of a realist evaluation of a web-based quality dashboard (QualDash) developed to support the use of national audit data in quality improvement. MethodsQualDash was co-designed with data users and installed in 8 clinical services (3 pediatric intensive care units and 5 cardiology services) across 5 health care organizations (sites A-E) in England between July and December 2019. Champions were identified to support adoption. Data to evaluate QualDash were collected between July 2019 and August 2021 and consisted of 148.5 hours of observations including hospital wards and clinical governance meetings, log files that captured the extent of use of QualDash over 12 months, and a questionnaire designed to assess the dashboard’s perceived usefulness and ease of use. Guided by the principles of realist evaluation, data were analyzed to understand how, why, and in what circumstances QualDash supported the use of national audit data in quality improvement. ResultsThe observations revealed that variation across sites in the amount and type of resources available to support data use, alongside staff interactions with QualDash, shaped its use and impact. Sites resourced with skilled audit support staff and established reporting systems (sites A and C) continued to use existing processes to report data. A number of constraints influenced use of QualDash in these sites including that some dashboard metrics were not configured in line with user expectations and staff were not fully aware how QualDash could be used to facilitate their work. In less well-resourced services, QualDash automated parts of their reporting process, streamlining the work of audit support staff (site B), and, in some cases, highlighted issues with data completeness that the service worked to address (site E). Questionnaire responses received from 23 participants indicated that QualDash was perceived as useful and easy to use despite its variable use in practice. ConclusionsWeb-based dashboards have the potential to support data-driven improvement, providing access to visualizations that can help users address key questions about care quality. Findings from this study point to ways in which dashboard design might be improved to optimize use and impact in different contexts; this includes using data meaningful to stakeholders in the co-design process and actively engaging staff knowledgeable about current data use and routines in the scrutiny of the dashboard metrics and functions. In addition, consideration should be given to the processes of data collection and upload that underpin the quality of the data visualized and consequently its potential to stimulate quality improvement. International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2019-033208

BackgroundAlthough many studies have described patient-level risk factors for outcomes in heart failure (HF), health care structural determinants remain largely unexplored. This research reports patient-, hospital- and country-level characteristics associated with 1-year all-cause mortality among patients with chronic HF, and investigates geographic and hospital variation in mortality.Methods and findingsWe included 9,277 patients with chronic HF enrolled between May 2011 and November 2017 in the prospective cohort study European Society of Cardiology Heart Failure Long Term registry across 142 hospitals, located in 22 countries. Mean age of the selected outpatients was 65 years (sd 13.2) and 28% were female. The all-cause 1-year mortality rate per 100 person-years was 7.1 (95% confidence interval (CI) 6.6-7.7), and varied between countries (median 6.8, IQR 5.6-11.2) and hospitals (median 7.8, IQR 5.2-12.4). Mortality was associated with age (incidence rate ratio 1.03, 95% CI 1.02-1.04), diabetes mellitus (1.37, 1.15-1.63), peripheral artery disease (1.56, 1.27-1.92), New York Heart Association class III/IV (1.91, 1.60-2.30), treatment with angiotensin-converting enzyme inhibitor and angiotensin receptor antagonists (0.71, 0.57-0.87) and HF clinic (0.64, 0.46-0.89). No other hospital-level characteristics, and no country-level healthcare characteristics were associated with 1-year mortality, with case-mix standardised variance between countries being very low (1.83e-06) and higher for hospitals (0.372).ConclusionsAll-cause mortality at 1 year among outpatients with chronic HF varies between countries and hospitals, and is associated with patient characteristics and the availability of hospital HF clinics. After full adjustment for clinical, hospital and country variables, between-country variance was negligible while between-hospital variance was evident.

Introduction A key priority for the UK National Health Service and patients is to ensure that medicines are used safely and effectively. However, medication changes are not always optimally communicated and implemented when patients transfer from hospital into community settings. Heart failure is a common reason for admission to hospital. Patients with heart failure have a high burden of morbidity, mortality and complex pharmacotherapeutic regimens. The Improving the Safety and Continuity Of Medicines management at Transitions of care programme comprises a cluster randomised controlled trial which will test the effectiveness of a complex behavioural intervention aimed at improving medications management at the interface between hospitals discharge and community care. We will conduct a rigorous process evaluation to inform interpretation of the trial findings, inform implementation of the intervention on a wider scale and aid dissemination of the intervention.Methods and analysis The process evaluation will be conducted in six purposively selected intervention sites (ie, hospital trusts and associated community pharmacies) using a mixed-methods design. Fidelity and barriers/enablers of implementation of the Medicines at Transitions Intervention (MaTI) will be explored using observation, interviews (20 patients, 40 healthcare professionals), surveys and routine trial data collection on adherence to MaTI. A parallel mixed analysis will be applied. Qualitative data will be thematically analysed using Framework analysis and survey data will be analysed descriptively. Data will be synthesised, triangulated and mapped to the Consolidated Framework for Implementation Research where appropriate. The process evaluation commenced on June 2018 and is due to end on February 2021.Ethics and dissemination Approved by Research Ethics Committee and the UK Health Research Authority REC: 18/YH/0017/IRAS: 231 431. Findings will be disseminated via academic and policy conferences, peer-reviewed publications and social media.Trial registration number ISRCTN66212970.

Introduction For non-ST-segment elevation acute coronary syndrome (NSTEACS) there is a gap between the use of class I guideline recommended therapies and clinical practice. The Global Registry of Acute Coronary Events (GRACE) risk score is recommended in international guidelines for the risk stratification of NSTEACS, but its impact on adherence to guideline-indicated treatments and reducing adverse clinical outcomes is unknown. The objective of the UK GRACE Risk Score Intervention Study (UKGRIS) trial is to assess the effectiveness of the GRACE risk score tool and associated treatment recommendations on the use of guideline-indicated care and clinical outcomes.Methods and analysis The UKGRIS, a parallel-group cluster randomised registry-based controlled trial, will allocate hospitals in a 1:1 ratio to manage NSTEACS by standard care or according to the GRACE risk score and associated international guidelines. UKGRIS will recruit a minimum of 3000 patients from at least 30 English National Health Service hospitals and collect healthcare data from national electronic health records. The co-primary endpoints are the use of guideline-indicated therapies, and the composite of cardiovascular death, non-fatal myocardial infarction, new onset heart failure hospitalisation or cardiovascular readmission at 12 months. Secondary endpoints include duration of inpatient hospital stay over 12 months, EQ-5D-5L responses and utilities, unscheduled revascularisation and the components of the composite endpoint over 12 months follow-up.Ethics and dissemination The study has ethical approval (North East - Tyne & Wear South Research Ethics Committee reference: 14/NE/1180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the funder’s open access policy.Trial registration number ISRCTN29731761; Pre-results.

Background Bleeding risk stratification in acute coronary syndrome is of highest clinical interest but current risk scores have limitations. We sought to develop and validate a new in‐hospital bleeding risk score for patients with acute myocardial infarction. Methods and Results From the nationwide SWEDEHEART (Swedish Web‐System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies) register, 97,597 patients with acute myocardial infarction enrolled from 2009 until 2014 were selected. A full model with 23 predictor variables and 8 interaction terms was fitted using logistic regression. The full model was approximated by a model with 5 predictors and 1 interaction term. Calibration, discrimination, and clinical utility was evaluated and compared with the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) and CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes With Early Implementation of the ACC/AHA Guidelines) scores. Internal and temporal validity was assessed. In‐hospital major bleeding, defined as fatal, intracranial, or requiring surgery or blood transfusion, occurred in 1356 patients (1.4%). The 5 predictors in the approximate model that constituted the SWEDEHEART score were hemoglobin, age, sex, creatinine, and C‐reactive protein. The ACTION and CRUSADE scores were poorly calibrated in the derivation cohort and therefore were recalibrated. The SWEDEHEART score showed higher discriminative ability than both recalibrated scores, overall (C‐index 0.80 versus 0.73/0.72) and in all predefined subgroups. Decision curve analysis demonstrated consistently positive and higher net benefit for the SWEDEHEART score compared with both recalibrated scores across all clinically relevant decision thresholds. The original ACTION and CRUSADE scores showed negative net benefit. Conclusions The 5‐item SWEDEHEART score discriminates in‐hospital major bleeding in patients with acute myocardial infarction and has superior model performance compared with the recalibrated ACTION and CRUSADE scores.

BackgroundThere is limited knowledge of the scale and impact of multimorbidity for patients who have had an acute myocardial infarction (AMI). Therefore, this study aimed to determine the extent to which multimorbidity is associated with long-term survival following AMI.Methods and findingsThis national observational study included 693,388 patients (median age 70.7 years, 452,896 [65.5%] male) from the Myocardial Ischaemia National Audit Project (England and Wales) who were admitted with AMI between 1 January 2003 and 30 June 2013. There were 412,809 (59.5%) patients with multimorbidity at the time of admission with AMI, i.e., having at least 1 of the following long-term health conditions: diabetes, chronic obstructive pulmonary disease or asthma, heart failure, renal failure, cerebrovascular disease, peripheral vascular disease, or hypertension. Those with heart failure, renal failure, or cerebrovascular disease had the worst outcomes (39.5 [95% CI 39.0-40.0], 38.2 [27.7-26.8], and 26.6 [25.2-26.4] deaths per 100 person-years, respectively). Latent class analysis revealed 3 multimorbidity phenotype clusters: (1) a high multimorbidity class, with concomitant heart failure, peripheral vascular disease, and hypertension, (2) a medium multimorbidity class, with peripheral vascular disease and hypertension, and (3) a low multimorbidity class. Patients in class 1 were less likely to receive pharmacological therapies compared with class 2 and 3 patients (including aspirin, 83.8% versus 87.3% and 87.2%, respectively; β-blockers, 74.0% versus 80.9% and 81.4%; and statins, 80.6% versus 85.9% and 85.2%). Flexible parametric survival modelling indicated that patients in class 1 and class 2 had a 2.4-fold (95% CI 2.3-2.5) and 1.5-fold (95% CI 1.4-1.5) increased risk of death and a loss in life expectancy of 2.89 and 1.52 years, respectively, compared with those in class 3 over the 8.4-year follow-up period. The study was limited to all-cause mortality due to the lack of available cause-specific mortality data. However, we isolated the disease-specific association with mortality by providing the loss in life expectancy following AMI according to multimorbidity phenotype cluster compared with the general age-, sex-, and year-matched population.ConclusionsMultimorbidity among patients with AMI was common, and conferred an accumulative increased risk of death. Three multimorbidity phenotype clusters that were significantly associated with loss in life expectancy were identified and should be a concomitant treatment target to improve cardiovascular outcomes.Trial registrationClinicalTrials.gov NCT03037255.

Background: Each year, approximately 30,000 people have an out-of-hospital cardiac arrest (OHCA) that is treated by UK ambulance services. Across all cases of OHCA, survival to hospital discharge is less than 10%. Acute coronary syndrome (ACS) is a common cause of OHCA. Objectives: To explore factors that influence survival in patients who initially survive an OHCA attributable to ACS. Data source: Data collected by the Myocardial Ischaemia National Audit Project (MINAP) between 2003 and 2015. Participants: Adult patients who had a first OHCA attributable to ACS and who were successfully resuscitated and admitted to hospital. Main outcome measures: Hospital mortality, neurological outcome at hospital discharge, and time to all-cause mortality. Methods: We undertook a cohort study using data from the MINAP registry. MINAP is a national audit that collects data on patients admitted to English, Welsh and Northern Irish hospitals with myocardial ischaemia. From the data set, we identified patients who had an OHCA. We used imputation to address data missingness across the data set. We analysed data using multilevel logistic regression to identify modifiable and non-modifiable factors that affect outcome. Results: Between 2003 and 2015, 1,127,140 patient cases were included in the MINAP data set. Of these, 17,604 OHCA cases met the study inclusion criteria. Overall hospital survival was 71.3%. Across hospitals with at least 60 cases, hospital survival ranged from 34% to 89% (median 71.4%, interquartile range 60.7–76.9%). Modelling, which adjusted for patient and treatment characteristics, could account for only 36.1% of this variability. For the primary outcome, the key modifiable factors associated with reduced mortality were reperfusion treatment [primary percutaneous coronary intervention (pPCI) or thrombolysis] and admission under a cardiologist. Admission to a high-volume cardiac arrest hospital did not influence survival. Sensitivity analyses showed that reperfusion was associated with reduced mortality among patients with a ST elevation myocardial infarction (STEMI), but there was no evidence of a reduction in mortality in patients who did not present with a STEMI. Limitations: This was an observational study, such that unmeasured confounders may have influenced study findings. Differences in case identification processes at hospitals may contribute to an ascertainment bias. Conclusions: In OHCA patients who have had a cardiac arrest attributable to ACS, there is evidence of variability in survival between hospitals, which cannot be fully explained by variables captured in the MINAP data set. Our findings provide some support for the current practice of transferring resuscitated patients with a STEMI to a hospital that can deliver pPCI. In contrast, it may be reasonable to transfer patients without a STEMI to the nearest appropriate hospital. Future work: There is a need for clinical trials to examine the clinical effectiveness and cost-effectiveness of invasive reperfusion strategies in resuscitated OHCA patients of cardiac cause who have not had a STEMI. Funding: The National Institute for Health Research Health Services and Delivery Research programme.

BackgroundThis study assessed sex differences in treatments, all‐cause mortality, relative survival, and excess mortality following acute myocardial infarction. Methods and ResultsA population‐based cohort of all hospitals providing acute myocardial infarction care in Sweden (SWEDEHEART [Swedish Web System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies]) from 2003 to 2013 was included in the analysis. Excess mortality rate ratios (EMRRs), adjusted for clinical characteristics and guideline‐indicated treatments after matching by age, sex, and year to background mortality data, were estimated. Although there were no sex differences in all‐cause mortality adjusted for age, year of hospitalization, and comorbidities for ST‐segment–elevation myocardial infarction (STEMI) and non‐STEMI at 1 year (mortality rate ratio: 1.01 [95% confidence interval (CI), 0.96–1.05] and 0.97 [95% CI, 0.95–0.99], respectively) and 5 years (mortality rate ratio: 1.03 [95% CI, 0.99–1.07] and 0.97 [95% CI, 0.95–0.99], respectively), excess mortality was higher among women compared with men for STEMI and non‐STEMI at 1 year (EMRR: 1.89 [95% CI, 1.66–2.16] and 1.20 [95% CI, 1.16–1.24], respectively) and 5 years (EMRR: 1.60 [95% CI, 1.48–1.72] and 1.26 [95% CI, 1.21–1.32], respectively). After further adjustment for the use of guideline‐indicated treatments, excess mortality among women with non‐STEMI was not significant at 1 year (EMRR: 1.01 [95% CI, 0.97–1.04]) and slightly higher at 5 years (EMRR: 1.07 [95% CI, 1.02–1.12]). For STEMI, adjustment for treatments attenuated the excess mortality for women at 1 year (EMRR: 1.43 [95% CI, 1.26–1.62]) and 5 years (EMRR: 1.31 [95% CI, 1.19–1.43]). ConclusionsWomen with acute myocardial infarction did not have statistically different all‐cause mortality, but had higher excess mortality compared with men that was attenuated after adjustment for the use of guideline‐indicated treatments. This suggests that improved adherence to guideline recommendations for the treatment of acute myocardial infarction may reduce premature cardiovascular death among women. Clinical Trial RegistrationURL: https://www.clinicaltrials.gov. Unique identifier: NCT02952417.

ABSTRACT Objectives Health services researchers are increasingly engaging with the emerging field of data science, but relatively few have the expertise to understand how innovative data linkage methodologies can, and cannot, be successfully applied in practice. There is little published guidance written specifically for this purpose. We aimed to develop study design criteria to help researchers in considering whether these methods are appropriate for their projects. A secondary objective was to test the criteria in a case study, and evaluate the application of the data linkage approach. Approach Clinical procedures requiring further research (according to the National Institute for Health and Care Excellence) were assessed against newly-developed CINDER criteria (Coverage; Identifiers; Numbers; Data; Existing records; Retrieval) to check the suitability of using data linkage methods. The CALON (Cardiac Ablation: Linking Outcomes for NICE) study was then established to evaluate outcomes of cardiac ablation procedures. Records from the UK’s Cardiac Rhythm Management (CRM) register were linked to routinely-recorded primary care, secondary care and mortality data in the Secure Anonymised Information Linkage (SAIL) Databank. Demographic profiles of patients identified from the register were compared with a group identified from SAIL. Outpatient attendances before and after ablation were compared using a Generalised Linear Mixed Model, assuming significance of p0.05); mean age differed by 1.6 years (95% CI 0.23-3.04; p=0.02). Patients accessed 26.7% fewer hospital outpatient appointments after ablation (95% CI 23.4 to 29.8; p