Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial.

Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from the RATE-AF trial wearables study, which was designed to compare heart rate in older, multimorbid patients with permanent atrial fibrillation and heart failure who were randomized to treatment with either digoxin or beta-blockers. Heart rate (n = 143,379,796) and physical activity (n = 23,704,307) intervals were obtained from 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a wrist-worn wearable linked to a smartphone for 20 weeks. Heart rates in participants treated with digoxin versus beta-blockers were not significantly different (regression coefficient 1.22 (95% confidence interval (CI) -2.82 to 5.27; P = 0.55); adjusted 0.66 (95% CI -3.45 to 4.77; P = 0.75)). No difference in heart rate was observed between the two groups of patients after accounting for physical activity (P = 0.74) or patients with high activity levels (≥30,000 steps per week; P = 0.97). Using a convolutional neural network designed to account for missing data, we found that wearable device data could predict New York Heart Association functional class 5 months after baseline assessment similarly to standard clinical measures of electrocardiographic heart rate and 6-minute walk test (F1 score 0.56 (95% CI 0.41 to 0.70) versus 0.55 (95% CI 0.41 to 0.68); P = 0.88 for comparison). The results of this study indicate that digoxin and beta-blockers have equivalent effects on heart rate in atrial fibrillation at rest and on exertion, and suggest that dynamic monitoring of individuals with arrhythmia using wearable technology could be an alternative to in-person assessment. ClinicalTrials.gov identifier: NCT02391337 .

Design and deployment of the STEEER-AF trial to evaluate and improve guideline adherence: A cluster-randomised trial by the European Society of Cardiology and European Heart Rhythm Association.

AIMS: To describe the rationale, design, delivery and baseline characteristics of STEEER-AF (Stroke prevention and rhythm control Treatment: Evaluation of an Educational programme of the European Society of Cardiology [ESC] in a cluster-Randomised trial in patients with Atrial Fibrillation). METHODS & RESULTS: STEEER-AF is a pragmatic trial designed to objectively and robustly determine whether guidelines are adhered to in routine practice, and evaluate a targeted educational programme for healthcare professionals. Seventy centres were randomised in 6 countries (France, Germany, Italy, Poland, Spain and United Kingdom; 2022-2023). STEEER-AF centres recruited 1732 patients with a diagnosis of atrial fibrillation (AF), with mean age 68.9 years (SD 11.7), CHA2DS2-VASc score 3.2 (SD 1.8) and 647 (37%) women. 843 patients (49%) were in AF and 760 (44%) in sinus rhythm at enrolment. Oral anticoagulant therapy was prescribed in 1,543 patients (89%), with the majority receiving direct oral anticoagulants (1,378; 89%). Previous cardioversion, antiarrhythmic drug therapy or ablation was recorded in 836 patients (48.3%). 551 patients (31.8%) were currently receiving an antiarrhythmic drug, and 446 (25.8%) were scheduled to receive a future cardioversion or ablation. The educational programme engaged 195 healthcare professionals across centres randomised to the intervention group, consisting of bespoke interactive online learning and reinforcement activities, supported by national expert trainers. CONCLUSION: The STEEER-AF trial was successfully deployed across six European countries to investigate guideline adherence in real-world practice, and evaluate if a structured educational programme for healthcare professionals can improve patient-level care. REGISTRATION: Clinicaltrials.gov NCT04396418.

Thromboembolic events and vascular dementia in patients with atrial fibrillation and low apparent stroke risk.

The prevention of thromboembolism in atrial fibrillation (AF) is typically restricted to patients with specific risk factors and ignores outcomes such as vascular dementia. This population-based cohort study used electronic healthcare records from 5,199,994 primary care patients (UK; 2005-2020). A total of 290,525 (5.6%) had a diagnosis of AF and were aged 40-75 years, of which 36,340 had no history of stroke, a low perceived risk of stroke based on clinical risk factors and no oral anticoagulant prescription. Matching was performed for age, sex and region to 117,298 controls without AF. During 5 years median follow-up (831,005 person-years), incident stroke occurred in 3.8% with AF versus 1.5% control (adjusted hazard ratio (HR) 2.06, 95% confidence interval (CI) 1.91-2.21; P < 0.001), arterial thromboembolism 0.3% versus 0.1% (HR 2.39, 95% CI 1.83-3.11; P < 0.001), and all-cause mortality 8.9% versus 5.0% (HR 1.44, 95% CI 1.38-1.50; P < 0.001). AF was associated with all-cause dementia (HR 1.17, 95% CI 1.04-1.32; P = 0.010), driven by vascular dementia (HR 1.68, 95% CI 1.33-2.12; P < 0.001) rather than Alzheimer's disease (HR 0.85, 95% CI 0.70-1.03; P = 0.09). Death and thromboembolic outcomes, including vascular dementia, are substantially increased in patients with AF despite a lack of conventional stroke risk factors.

Remote monitoring of atrial fibrillation recurrence using mHealth technology (REMOTE-AF).

Aims: This proof-of-concept study sought to evaluate changes in heart rate (HR) obtained from a consumer wearable device and compare against implantable loop recorder (ILR)-detected recurrence of atrial fibrillation (AF) and atrial tachycardia (AT) after AF ablation. Methods and results: REMOTE-AF (NCT05037136) was a prospectively designed sub-study of the CASA-AF randomized controlled trial (NCT04280042). Participants without a permanent pacemaker had an ILR implanted at their index ablation procedure for longstanding persistent AF. Heart rate and step count were continuously monitored using photoplethysmography (PPG) from a commercially available wrist-worn wearable. Photoplethysmography-recorded HR data were pre-processed with noise filtration and episodes at 1-min interval over 30 min of HR elevations (Z-score = 2) were compared with corresponding ILR data. Thirty-five patients were enrolled, with mean age 70.3 ± 6.8 years and median follow-up 10 months (interquartile range 8-12 months). Implantable loop recorder analysis revealed 17 out of 35 patients (49%) had recurrence of AF/AT. Compared with ILR recurrence, wearable-derived elevations in HR ≥ 110 beats per minute had a sensitivity of 95.3%, specificity 54.1%, positive predictive value (PPV) 15.8%, negative predictive value (NPV) 99.2%, and overall accuracy 57.4%. With PPG-recorded HR elevation spikes (non-exercise related), the sensitivity was 87.5%, specificity 62.2%, PPV 39.2%, NPV 92.3%, and overall accuracy 64.0% in the entire patient cohort. In the AF/AT recurrence only group, sensitivity was 87.6%, specificity 68.3%, PPV 53.6%, NPV 93.0%, and overall accuracy 75.0%. Conclusion: Consumer wearable devices have the potential to contribute to arrhythmia detection after AF ablation. Study Registration: ClinicalTrials.gov Identifier: NCT05037136 https://clinicaltrials.gov/ct2/show/NCT05037136.

Development of automated neural network prediction for echocardiographic left ventricular ejection fraction.

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function.

Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference.

AIMS: Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). METHODS AND RESULTS: Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF. CONCLUSIONS: Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF.

Efficacy of decentralised home-based antihypertensive treatment in older adults with multimorbidity and polypharmacy (ATEMPT): an open-label randomised controlled pilot trial.

BACKGROUND: Older patients with multimorbidity and polypharmacy have been under-represented in clinical trials. We aimed to assess the effect of different intensities of antihypertensive treatment on changes in blood pressure, major safety outcomes, and patient-reported outcomes in this population. METHODS: ATEMPT was a decentralised, two-armed, parallel-group, open-label randomised controlled pilot trial conducted in the Thames Valley area, South East England. Individuals aged 65 years or older with multimorbidity (three or more chronic conditions) or polypharmacy (five or more types of medications) and a systolic blood pressure of 115-165 mm Hg were eligible for inclusion. Participants were identified through a search of national hospital discharge databases, identification of patients registered with an online pharmacy, and via targeted advertising on social media platforms. Participants were randomly assigned to receive up to two more classes versus up to two fewer classes of antihypertensive medications. Apart from routine home visits for conducting the baseline assessment, all communication, monitoring, and management of participants by the trial team was conducted remotely. The primary outcome was change in home-measured blood pressure. FINDINGS: Between Dec 15, 2020, and Aug 31, 2022, 230 participants were randomly assigned (n=126 to more vs n=104 to fewer antihypertensive medications). The frequency of serious adverse events was similar across both groups; no cardiovascular events occurred in the more antihypertensive drugs group, compared with six in the fewer antihypertensive drugs group, of which two were fatal. Over a 13-month follow-up period, the mean systolic blood pressure in the group allocated to receive more antihypertensive medications decreased from 134·5 mm Hg (SD 10·7) at baseline to 122·1 mm Hg (10·5). By contrast, in the group allocated to receive fewer antihypertensive medications, it remained relatively unchanged, moving from 134·8 mm Hg (SD 11·2) at baseline to 132·9 mm Hg (15·3); this corresponded to a mean difference of -10·7 mm Hg (95% CI -17·5 to -4·0). INTERPRETATION: Remotely delivered antihypertensive treatment substantially reduced systolic blood pressure in older adults who are often less represented in trials, with no increase in the risk of serious adverse events. The results of this trial will inform a larger clinical trial focusing on assessing major cardiovascular events, safety, physical functioning, and cognitive function that is currently in the planning stages. These results also underscore the efficiency of decentralised trial designs, which might be of broader interest in other settings. FUNDING: National Institute for Health Research Oxford Biomedical Research Centre and the Oxford Martin School.

Unraveling the relationships between alpha- and beta-adrenergic modulation and the risk of heart failure.

Background: The effects of α and ß adrenergic receptor modulation on the risk of developing heart failure (HF) remains uncertain due to a lack of randomized controlled trials. This study aimed to estimate the effects of α and ß adrenergic receptors modulation on the risk of HF and to provide proof of principle for genetic target validation studies in HF. Methods: Genetic variants within the cis regions encoding the adrenergic receptors α1A, α2B, ß1, and ß2 associated with blood pressure in a 757,601-participant genome-wide association study (GWAS) were selected as instruments to perform a drug target Mendelian randomization study. Effects of these variants on HF risk were derived from the HERMES GWAS (542,362 controls; 40,805 HF cases). Results: Lower α1A or ß1 activity was associated with reduced HF risk: odds ratio (OR) 0.83 (95% CI 0.74-0.93, P = 0.001) and 0.95 (95% CI 0.93-0.97, P = 8 × 10-6). Conversely, lower α2B activity was associated with increased HF risk: OR 1.09 (95% CI 1.05-1.12, P = 3 × 10-7). No evidence of an effect of lower ß2 activity on HF risk was found: OR 0.99 (95% CI 0.92-1.07, P = 0.95). Complementary analyses showed that these effects were consistent with those on left ventricular dimensions and acted independently of any potential effect on coronary artery disease. Conclusions: This study provides genetic evidence that α1A or ß1 receptor inhibition will likely decrease HF risk, while lower α2B activity may increase this risk. Genetic variant analysis can assist with drug development for HF prevention.

Efficacy and safety of intravenous beta-blockers in acute atrial fibrillation and flutter is dependent on beta-1 selectivity: a systematic review and meta-analysis of randomised trials.

BACKGROUND: Intravenous beta-blockers are commonly used to manage patients with acute atrial fibrillation (AF) and atrial flutter (AFl), but the choice of specific agent is often not evidence-based. METHODS: A prospectively-registered systematic review and meta-analysis of randomised trials (PROSPERO: CRD42020204772) to compare the safety and efficacy of intravenous beta-blockers against alternative pharmacological agents. RESULTS: Twelve trials comparing beta-blockers with diltiazem, digoxin, verapamil, anti-arrhythmic drugs and placebo were included, with variable risk of bias and 1152 participants. With high heterogeneity (I2 = 87%; p < 0.001), there was no difference in the primary outcomes of heart rate reduction (standardised mean difference - 0.65 beats/minute compared to control, 95% CI - 1.63 to 0.32; p = 0.19) or the proportion that achieved target heart rate (risk ratio [RR] 0.85, 95% CI 0.36-1.97; p = 0.70). Conventional selective beta-1 blockers were inferior for target heart rate reduction versus control (RR 0.33, 0.17-0.64; p < 0.001), whereas super-selective beta-1 blockers were superior (RR 1.98, 1.54-2.54; p < 0.001). There was no significant difference between beta-blockers and comparators for secondary outcomes of conversion to sinus rhythm (RR 1.15, 0.90-1.46; p = 0.28), hypotension (RR 1.85, 0.87-3.93; p = 0.11), bradycardia (RR 1.29, 0.25-6.82; p = 0.76) or adverse events leading to drug discontinuation (RR 1.03, 0.49-2.17; p = 0.93). The incidence of hypotension and bradycardia were greater with non-selective beta-blockers (p = 0.031 and p < 0.001). CONCLUSIONS: Across all intravenous beta-blockers, there was no difference with other medications for acute heart rate control in atrial fibrillation and flutter. Efficacy and safety may be improved by choosing beta-blockers with higher beta-1 selectivity.

Diagnosis and practical management of digoxin toxicity: a narrative review and consensus.

There are currently no universally accepted guidelines for the management of digoxin toxicity. In the absence of clinical practice guidelines, a set of consensus recommendations for management of digoxin toxicity in the clinical setting were developed through a modified Delphi approach. The recommendations highlight the importance of early recognition of signs of potentially life-threatening toxicity that requires immediate treatment with digoxin-specific antibodies. The consensus identifies a straightforward approach to dosing immune antibody fragments according to the presence or absence of signs of life-threatening toxicity. Supportive measures and management of specific signs of toxicity are also covered.

Systematic review of the incidence and clinical risk predictors of atrial fibrillation and permanent pacemaker implantation for bradycardia in Fabry disease.

INTRODUCTION: Fabry disease (FD) is an X-linked lysosomal storage disorder caused by enzyme deficiency, leading to glycosphingolipid accumulation. Cardiac accumulation triggers local tissue injury, electrical instability and arrhythmia. Bradyarrhythmia and atrial fibrillation (AF) incidence are reported in up to 16% and 13%, respectively. OBJECTIVE: We conducted a systematic review evaluating AF burden and bradycardia requiring permanent pacemaker (PPM) implantation and report any predictive risk factors identified. METHODS: We conducted a literature search on studies in adults with FD published from inception to July 2019. Study outcomes included AF or bradycardia requiring therapy. Databases included Embase, Medline, PubMed, Web of Science, CINAHL and Cochrane. The Risk of Bias Agreement tool for Non-Randomised Studies (RoBANS) was utilised to assess bias across key areas. RESULTS: 11 studies were included, eight providing data on AF incidence or PPM implantation. Weighted estimate of event rates for AF were 12.2% and 10% for PPM. Age was associated with AF (OR 1.05-1.20 per 1-year increase in age) and a risk factor for PPM implantation (composite OR 1.03). Left ventricular hypertrophy (LVH) was associated with AF and PPM implantation. CONCLUSION: Evidence supporting AF and bradycardia requiring pacemaker implantation is limited to single-centre studies. Incidence is variable and choice of diagnostic modality plays a role in detection rate. Predictors for AF (age, LVH and atrial dilatation) and PPM (age, LVH and PR/QRS interval) were identified but strength of association was low. Incidence of AF and PPM implantation in FD are variably reported with arrhythmia burden likely much higher than previously thought. PROSPERO DATABASE: CRD42019132045.

Real world evidence: Perspectives from a European Society of Cardiology Cardiovascular Round Table with contribution from the European Medicines Agency.

Real world data (RWD) refers to healthcare information that is routinely collected in electronic healthcare records (EHR), hospital and pharmacy records, patient and disease registries, and health insurance databases. The collection and analysis of this vast amount of data is an important complement to that obtained from conventional randomised controlled trials (RCT). Real world data has been used for healthcare quality improvements, to conduct clinical trials, to support drug and device development, and to inform medical guidelines. The utility of RWD may be facilitated by common data models, which standardise format and content, and allow data from different health systems to be analysed together. The European Society of Cardiology (ESC) supports the use of RWD in collaboration with national cardiac societies, regulatory authorities, and industry to encourage continuous quality of care improvements at the hospital and country level, to conduct registry-based randomised clinical trials (R-RCT) and to facilitate safety surveillance of novel drugs and devices. The European Medicines Agency (EMA) is developing systems and processes to enable the use of RWD that can help in trial planning, defining clinical contexts, and enhancing outcome assessments. RWD can also contribute to the measurement of the impact of regulatory actions, such as contraindications or restriction of indications by looking at medicines use patterns over time across European Member States. A number of other initiatives from the European Commission and the EMA are underway to strengthen the EU's health security framework, and foster the collection and utilisation of RWD.

Repurposing digoxin for geroprotection in patients with frailty and multimorbidity.

The geroscience hypothesis proposes biological hallmarks of ageing are modifiable. Increasing evidence supports targeting these hallmarks with therapeutics could prevent and ameliorate age-related conditions - collectively termed "geroprotector drugs". Cellular senescence is a hallmark with considerable potential to be modified with geroprotector drugs. Senotherapeutics are drugs that target cellular senescence for therapeutic benefit. Repurposing commonly used medications with secondary geroprotector properties is a strategy of interest to promote incorporation of geroprotector drugs into clinical practice. One candidate is the cardiac glycoside digoxin. Evidence in mouse models of pulmonary fibrosis, Alzheimer's disease, arthritis and atherosclerosis support digoxin as a senotherapeutic agent. Proposed senolytic mechanisms are upregulation of intrinsic apoptotic pathways and promoting intracellular acidification. Digoxin also appears to have a senomorphic mechanism - altering the T cell pool to ameliorate pro-inflammatory SASP. Despite being widely prescribed to treat atrial fibrillation and heart failure, often in multimorbid older adults, it is not known whether digoxin exerts senotherapeutic effects in humans. Further cellular and animal studies, and ultimately clinical trials with participation of pre-frail older adults, are required to identify whether digoxin has senotherapeutic effect at low dose. This paper reviews the biological mechanisms identified in preliminary cellular and animal studies that support repurposing digoxin as a geroprotector in patients with frailty and multimorbidity.

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference.

Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related complications cause unacceptable morbidity and mortality even on optimal current therapy. This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evidence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impairment, which can be facilitated by AI.

Post-mortem examination of high mortality in patients with heart failure and atrial fibrillation.

BACKGROUND: The prevalence of combined heart failure (HF) and atrial fibrillation (AF) is rising, and these patients suffer from high rates of mortality. This study aims to provide robust data on factors associated with death, uniquely supported by post-mortem examination. METHODS: A retrospective cohort study of hospitalized adults with a clinical diagnosis of HF and AF at a tertiary centre in Romania between 2014 and 2017. A standardized post-mortem examination was performed where death occurred within 24 h of admission, when the cause of death was not clear or by physician request. National records were used to collect mortality data, subsequently categorized and analysed as HF-related death, vascular death and non-cardiovascular death using Cox proportional hazards regression. RESULTS: A total of 1009 consecutive patients with a mean age of 73 ± 11 years, 47% women, NYHA class 3.0 ± 0.9, left ventricular ejection fraction (LVEF) 40.1 ± 11.0% and 100% anticoagulated were followed up for 1.5 ± 0.9 years. A total of 291 (29%) died, with post-mortems performed on 186 (64%). Baseline factors associated with mortality were dependent on the cause of death. HF-related death in 136 (47%) was associated with higher NYHA class (hazard ratio [HR] 2.45 per one class increase, 95% CI 1.73-3.46; p < 0.001) and lower LVEF (0.95 per 1% increase, 0.93-0.97; p < 0.001). Vascular death occurred in 75 (26%) and was associated with hypertension (HR 2.83, 1.36-5.90; p = 0.005) and higher LVEF (1.08 per 1% increase, 1.05-1.11; p < 0.001). Non-cardiovascular death in 80 (28%) was associated with clinical obesity (HR 2.20, 1.21-4.00; p = 0.010) and higher LVEF (1.10 per 1% increase, 1.06-1.13; p < 0.001). Across all causes, there was no relationship between mortality and AF type (p = 0.77), HF type (p = 0.85) or LVEF (p = 0.58). CONCLUSIONS: Supported by post-mortem data, the cause of death in HF and AF patients is heterogeneous, and the relationships with typical markers of mortality are critically dependent on the mode of death. The poor prognosis in this group demands further attention to improve management beyond anticoagulation.

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.