Inequalities in care delivery and outcomes for myocardial infarction, heart failure, atrial fibrillation, and aortic stenosis in the United Kingdom

Cardiovascular diseases are a leading cause of death and disability globally, with inequalities in burden and care delivery evident in Europe. To address this challenge, The Lancet Regional Health—Europe convened experts from a range of countries to summarise the current state of knowledge on cardiovascular disease inequalities across Europe. This Series paper presents evidence from nationwide secondary care registries and primary care healthcare records regarding inequalities in care delivery and outcomes for myocardial infarction, heart failure, atrial fibrillation, and aortic stenosis in the National Health Service (NHS) across the United Kingdom (UK) by age, sex, ethnicity and geographical location. Data suggest that women and older people less frequently receive guideline-recommended treatment than men and younger people. There are limited publications about ethnicity in the UK for the studied disease areas. Finally, there is inter-healthcare provider variation in cardiovascular care provision, especially for transcatheter aortic valve implantation, which is associated with differing outcomes for patients with the same disease. Providing equitable care is a founding principle of the UK NHS, which is well positioned to deliver innovative policy responses to reverse observed inequalities. Understanding differences in care may enable the implementation of appropriate strategies to mitigate differences in outcomes

Incident cardiovascular, renal, metabolic diseases and death in individuals identified for risk-guided atrial fibrillation screening: a nationwide cohort study

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

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation

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

Prediction models for heart failure in the community: a systematic review and meta‐analysis

Aims: Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. Methods and Results: From inception to 3rd November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) Risk Score (summary c-statistic 0.802, 95% CI 0.707–0.883), graph-based attention model (GRAM; 0.791, 95% CI 0.677–0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792–0.843), PCP-HF white women model (0.852, 95% CI 0.804–0.895), and REverse Time AttentIoN Model (RETAIN; 0.839, 95% CI 0.748–0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC Risk Score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. Conclusions: Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research

Ectopically Expressed Meiosis-Specific Cancer Testis Antigen HORMAD1 Promotes Genomic Instability in Squamous Cell Carcinomas

Genomic instability is a prominent hallmark of cancer, however the mechanisms that drive and sustain this process remain elusive. Research demonstrates that numerous cancers with increased levels of genomic instability ectopically express meiosis-specific genes and undergo meiomitosis, the clash of mitotic and meiotic processes. These meiotic genes may represent novel therapeutic targets for the treatment of cancer. We studied the relationship between the expression of the meiosis protein HORMAD1 and genomic instability in squamous cell carcinomas (SCCs). First, we assessed markers of DNA damage and genomic instability following knockdown and overexpression of HORMAD1 in different cell lines representing SCCs and epithelial cancers. shRNA-mediated depletion of HORMAD1 expression resulted in increased genomic instability, DNA damage, increased sensitivity to etoposide, and decreased expression of DNA damage response/repair genes. Conversely, overexpression of HORMAD1 exhibited protective effects leading to decreased DNA damage, enhanced survival and decreased sensitivity to etoposide. Furthermore, we identified a meiotic molecular pathway that regulates HORMAD1 expression by targeting the upstream meiosis transcription factor STRA8. Our results highlight a specific relationship between HORMAD1 and genomic instability in SCCs, suggesting that selectively inhibiting HORMAD1, possibly, through STRA8 signaling, may provide a new paradigm of treatment options for HORMAD1-expressing SCCs