Key Considerations for Designing Clinical Studies to Evaluate Digital Health Solutions.

Evidence of clinical impact is critical to unlock the potential of digital health solutions (DHSs), yet many solutions are failing to deliver positive clinical results. We argue in this viewpoint that this failure is linked to current approaches to DHS evaluation design, which neglect numerous key characteristics (KCs) requiring specific scientific and design considerations. We first delineate the KCs of DHSs: (1) they are implemented at health care system and patient levels; (2) they are "complex" interventions; (3) they can drive multiple clinical outcomes indirectly through a multitude of smaller clinical benefits; (4) their mechanism of action can vary between individuals and change over time based on patient needs; and (5) they develop through short, iterative cycles-optimally within a real-world use context. Following our objective to drive better alignment between clinical evaluation design and the unique traits of DHSs, we then provide methodological suggestions that better address these KCs, including tips on mechanism-of-action mapping, alternative randomization methods, control-arm adaptations, and novel end-point selection, as well as innovative methods utilizing real-world data and platform research.

Rationale and design of the PACIFIC-PRESERVED (PhenomApping, ClassIFication and Innovation for Cardiac dysfunction in patients with heart failure and PRESERVED left ventricular ejection fraction) study.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome that is poorly defined, reflecting an incomplete understanding of its pathophysiology. AIM: To redefine the phenotypic spectrum of HFpEF. METHODS: The PACIFIC-PRESERVED study is a prospective multicentre cohort study designed to perform multidimensional deep phenotyping of patients diagnosed with HFpEF (left ventricular ejection fraction≥50%), patients with heart failure with reduced ejection fraction (left ventricular ejection fraction≤40%) and subjects without overt heart failure (3:2:1 ratio). The study proposes prospective investigations in patients during a 1-day hospital stay: physical examination; electrocardiogram; performance-based tests; blood samples; cardiac magnetic resonance imaging; transthoracic echocardiography (rest and low-level exercise); myocardial shear wave elastography; chest computed tomography; and non-invasive measurement of arterial stiffness. Dyspnoea, depression, general health and quality of life will be assessed by dedicated questionnaires. A biobank will be established. After the hospital stay, patients are asked to wear a connected garment (with digital sensors) to collect electrocardiography, pulmonary and activity variables in real-life conditions (for up to 14 days). Data will be centralized for machine-learning-based analyses, with the aim of reclassifying HFpEF into more distinct subgroups, improving understanding of the disease mechanisms and identifying new biological pathways and molecular targets. The study will also serve as a platform to enable the development of innovative technologies and strategies for the diagnosis and stratification of patients with HFpEF. CONCLUSIONS: PACIFIC-PRESERVED is a prospective multicentre phenomapping study, using novel analytical techniques, which will provide a unique data resource to better define HFpEF and identify new clinically meaningful subgroups of patients.

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.

"Click and mortar" opportunities for digitization and consumerism in trials.

BACKGROUND: Digitization (using novel digital tools and strategies) and consumerism (taking a consumer-oriented approach) are increasingly commonplace in clinical trials, but the implications of these changes are not well described. METHODS: We assembled a group of trial experts from academia, industry, non-profit, and government to discuss implications of this changing trial landscape and provide guidance. RESULTS: Digitization and consumerism can increase the volume and diversity of trial participants and expedite recruitment. However, downstream bottlenecks, challenges with retention, and serious issues with equity, ethics, and security can result. A "click and mortar" approach, combining approaches from novel and traditional trials with the thoughtful use of technology, may optimally balance opportunities and challenges facing many trials. CONCLUSION: We offer expert guidance and three "click and mortar" approaches to digital, consumer-oriented trials. More guidance and research are needed to navigate the associated opportunities and challenges.

Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study.

BACKGROUND: Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and presentations, or with clinical and non-clinical validation by different machine learning methods. Using our published framework, we aimed to discover heart failure subtypes and validate them upon population representative data. METHODS: In this external, prognostic, and genetic validation study we analysed individuals aged 30 years or older with incident heart failure from two population-based databases in the UK (Clinical Practice Research Datalink [CPRD] and The Health Improvement Network [THIN]) from 1998 to 2018. Pre-heart failure and post-heart failure factors (n=645) included demographic information, history, examination, blood laboratory values, and medications. We identified subtypes using four unsupervised machine learning methods (K-means, hierarchical, K-Medoids, and mixture model clustering) with 87 of 645 factors in each dataset. We evaluated subtypes for (1) external validity (across datasets); (2) prognostic validity (predictive accuracy for 1-year mortality); and (3) genetic validity (UK Biobank), association with polygenic risk score (PRS) for heart failure-related traits (n=11), and single nucleotide polymorphisms (n=12). FINDINGS: We included 188 800, 124 262, and 9573 individuals with incident heart failure from CPRD, THIN, and UK Biobank, respectively, between Jan 1, 1998, and Jan 1, 2018. After identifying five clusters, we labelled heart failure subtypes as (1) early onset, (2) late onset, (3) atrial fibrillation related, (4) metabolic, and (5) cardiometabolic. In the external validity analysis, subtypes were similar across datasets (c-statistics: THIN model in CPRD ranged from 0·79 [subtype 3] to 0·94 [subtype 1], and CPRD model in THIN ranged from 0·79 [subtype 1] to 0·92 [subtypes 2 and 5]). In the prognostic validity analysis, 1-year all-cause mortality after heart failure diagnosis (subtype 1 0·20 [95% CI 0·14-0·25], subtype 2 0·46 [0·43-0·49], subtype 3 0·61 [0·57-0·64], subtype 4 0·11 [0·07-0·16], and subtype 5 0·37 [0·32-0·41]) differed across subtypes in CPRD and THIN data, as did risk of non-fatal cardiovascular diseases and all-cause hospitalisation. In the genetic validity analysis the atrial fibrillation-related subtype showed associations with the related PRS. Late onset and cardiometabolic subtypes were the most similar and strongly associated with PRS for hypertension, myocardial infarction, and obesity (p<0·0009). We developed a prototype app for routine clinical use, which could enable evaluation of effectiveness and cost-effectiveness. INTERPRETATION: Across four methods and three datasets, including genetic data, in the largest study of incident heart failure to date, we identified five machine learning-informed subtypes, which might inform aetiological research, clinical risk prediction, and the design of heart failure trials. FUNDING: European Union Innovative Medicines Initiative-2.

Sex differences in the generalizability of randomized clinical trials in heart failure with reduced ejection fraction.

AIMS: In order to understand how sex differences impact the generalizability of randomized clinical trials (RCTs) in patients with heart failure (HF) and reduced ejection fraction (HFrEF), we sought to compare clinical characteristics and clinical outcomes between RCTs and HF observational registries stratified by sex. METHODS AND RESULTS: Data from two HF registries and five HFrEF RCTs were used to create three subpopulations: one RCT population (n = 16 917; 21.7% females), registry patients eligible for RCT inclusion (n = 26 104; 31.8% females), and registry patients ineligible for RCT inclusion (n = 20 810; 30.2% females). Clinical endpoints included all-cause mortality, cardiovascular mortality, and first HF hospitalization at 1 year. Males and females were equally eligible for trial enrolment (56.9% of females and 55.1% of males in the registries). One-year mortality rates were 5.6%, 14.0%, and 28.6% for females and 6.9%, 10.7%, and 24.6% for males in the RCT, RCT-eligible, and RCT-ineligible groups, respectively. After adjusting for 11 HF prognostic variables, RCT females showed higher survival compared to RCT-eligible females (standardized mortality ratio [SMR] 0.72; 95% confidence interval [CI] 0.62-0.83), while RCT males showed higher adjusted mortality rates compared to RCT-eligible males (SMR 1.16; 95% CI 1.09-1.24). Similar results were also found for cardiovascular mortality (SMR 0.89; 95% CI 0.76-1.03 for females, SMR 1.43; 95% CI 1.33-1.53 for males). CONCLUSION: Generalizability of HFrEF RCTs differed substantially between the sexes, with females having lower trial participation and female trial participants having lower mortality rates compared to similar females in the registries, while males had higher than expected cardiovascular mortality rates in RCTs compared to similar males in registries.

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.

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

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.

What Is Normal for an Aging Heart?: A Prospective CMR Cohort Study.

BACKGROUND: This study aims to investigate normal changes throughout aging of the heart in cardiac magnetic resonance (CMR) imaging in healthy volunteers. While type 2 diabetes mellitus is a frequent finding in the elderly population, also the influence of this circumstance in otherwise healthy persons is part of our study. METHODS: In this prospective single-center trial, 75 healthy subjects in distinct age groups and 10 otherwise healthy diabetics were enrolled. All subjects underwent functional, flow sensitive, native T2- and T1-mapping in a 1.5T CMR scanner. RESULTS: No differences in right and left ventricular ejection fractions were observed between aging healthy groups. Bi-ventricular volumes lowered significantly (p<0.001) between the age groups. There was also a significant decrease in myocardial T1 values, aortic distensibility, and left ventricular peak diastolic strain rates. There were no differences in T2 mapping and the other deformation parameters. Patients with type 2 diabetes mellitus had lower end-diastolic volume indexes; all the other measurements were comparable. CONCLUSIONS: Aging processes in the healthy heart involve a decrease in ventricular volumes, with ejection fractions remaining normal. Stiffening of the myocardium and aorta and a decrease in T1 values are potential indications of age-related remodeling. Type 2 diabetes mellitus seems to have no major influence on aging processes of the heart. TRIAL REGISTRATION: EudraCT Identifier: EudraCT 2017-000045-42.

A population-based study of 92 clinically recognized risk factors for heart failure: co-occurrence, prognosis and preventive potential.

AIMS: Primary prevention strategies for heart failure (HF) have had limited success, possibly due to a wide range of underlying risk factors (RFs). Systematic evaluations of the prognostic burden and preventive potential across this wide range of risk factors are lacking. We aimed at estimating evidence, prevalence and co-occurrence for primary prevention and impact on prognosis of RFs for incident HF. METHODS AND RESULTS: We systematically reviewed trials and observational evidence of primary HF prevention across 92 putative aetiologic RFs for HF identified from US and European clinical practice guidelines. We identified 170 885 individuals aged ≥30 years with incident HF from 1997 to 2017, using linked primary and secondary care UK electronic health records (EHR) and rule-based phenotypes (ICD-10, Read Version 2, OPCS-4 procedure and medication codes) for each of 92 RFs. Only 10/92 factors had high quality observational evidence for association with incident HF; 7 had effective randomized controlled trial (RCT)-based interventions for HF prevention (RCT-HF), and 6 for cardiovascular disease prevention, but not HF (RCT-CVD), and the remainder had no RCT-based preventive interventions (RCT-0). We were able to map 91/92 risk factors to EHR using 5961 terms, and 88/91 factors were represented by at least one patient. In the 5 years prior to HF diagnosis, 44.3% had ≥4 RFs. By RCT evidence, the most common RCT-HF RFs were hypertension (48.5%), stable angina (34.9%), unstable angina (16.8%), myocardial infarction (15.8%), and diabetes (15.1%); RCT-CVD RFs were smoking (46.4%) and obesity (29.9%); and RCT-0 RFs were atrial arrhythmias (17.2%), cancer (16.5%), heavy alcohol intake (14.9%). Mortality at 1 year varied across all 91 factors (lowest: pregnancy-related hormonal disorder 4.2%; highest: phaeochromocytoma 73.7%). Among new HF cases, 28.5% had no RCT-HF RFs and 38.6% had no RCT-CVD RFs. 15.6% had either no RF or only RCT-0 RFs. CONCLUSION: One in six individuals with HF have no recorded RFs or RFs without trials. We provide a systematic map of primary preventive opportunities across a wide range of RFs for HF, demonstrating a high burden of co-occurrence and the need for trials tackling multiple RFs.

Patients' and public views and attitudes towards the sharing of health data for research: a narrative review of the empirical evidence.

INTRODUCTION: International sharing of health data opens the door to the study of the so-called 'Big Data', which holds great promise for improving patient-centred care. Failure of recent data sharing initiatives indicates an urgent need to invest in societal trust in researchers and institutions. Key to an informed understanding of such a 'social license' is identifying the views patients and the public may hold with regard to data sharing for health research. METHODS: We performed a narrative review of the empirical evidence addressing patients' and public views and attitudes towards the use of health data for research purposes. The literature databases PubMed (MEDLINE), Embase, Scopus and Google Scholar were searched in April 2019 to identify relevant publications. Patients' and public attitudes were extracted from selected references and thematically categorised. RESULTS: Twenty-seven papers were included for review, including both qualitative and quantitative studies and systematic reviews. Results suggest widespread-though conditional-support among patients and the public for data sharing for health research. Despite the fact that participants recognise actual or potential benefits of data research, they expressed concerns about breaches of confidentiality and potential abuses of the data. Studies showed agreement on the following conditions: value, privacy, risk minimisation, data security, transparency, control, information, trust, responsibility and accountability. CONCLUSIONS: Our results indicate that a social license for data-intensive health research cannot simply be presumed. To strengthen the social license, identified conditions ought to be operationalised in a governance framework that incorporates the diverse patient and public values, needs and interests.

Generalizability of randomized controlled trials in heart failure with reduced ejection fraction.

BACKGROUND: Heart failure (HF) trials have stringent inclusion and exclusion criteria, but limited data exist regarding generalizability of trials. We compared patient characteristics and outcomes between patients with HF and reduced ejection fraction (HFrEF) in trials and observational registries. METHODS AND RESULTS: Individual patient data for 16 922 patients from five randomized clinical trials and 46 914 patients from two HF registries were included. The registry patients were categorized into trial-eligible and non-eligible groups using the most commonly used inclusion and exclusion criteria. A total of 26 104 (56%) registry patients fulfilled the eligibility criteria. Unadjusted all-cause mortality rates at 1 year were lowest in the trial population (7%), followed by trial-eligible patients (12%) and trial-non-eligible registry patients (26%). After adjustment for age and sex, all-cause mortality rates were similar between trial participants and trial-eligible registry patients [standardized mortality ratio (SMR) 0.97; 95% confidence interval (CI) 0.92-1.03] but cardiovascular mortality was higher in trial participants (SMR 1.19; 1.12-1.27). After full case-mix adjustment, the SMR for cardiovascular mortality remained higher in the trials at 1.28 (1.20-1.37) compared to RCT-eligible registry patients. CONCLUSION: In contemporary HF registries, over half of HFrEF patients would have been eligible for trial enrolment. Crude clinical event rates were lower in the trials, but, after adjustment for case-mix, trial participants had similar rates of survival as registries. Despite this, they had about 30% higher cardiovascular mortality rates. Age and sex were the main drivers of differences in clinical outcomes between HF trials and observational HF registries.

Carboxypeptidase U (TAFIa) Is Rapidly Activated and Deactivated Following Thrombolysis and Thrombectomy in Stroke Patients.

The antifibrinolytic enzyme carboxypeptidase U (CPU, TAFIa, CPB2) is an appealing target for the treatment of acute ischemic stroke (AIS). Increased insights in CPU activation and inactivation during thrombolysis (rtPA) with or without endovascular thrombectomy (EVT) are required to develop CPU inhibitors as profibrinolytic agents with optimal benefits/risks. Therefore, CPU kinetics during ischemic stroke treatment were evaluated. AIS patients with documented cerebral artery occlusion receiving rtPA (N = 20) or rtPA + EVT (N = 16) were included. CPU activation during thrombolysis was measured by an ultrasensitive HPLC-based CPU activity method and by an ELISA measuring both CPU and inactivated CPU (CPU + CPUi). Intravenous blood samples were collected at admission and throughout the first 24 h. Additional in situ blood samples were collected in the rtPA + EVT cohort proximal from the thrombus. The NIHSS score was determined at baseline and 24 h. CPU activity and CPU + CPUi levels increased upon rtPA administration and reached peak values at the end of thrombolysis (1 h). High inter-individual variability was observed in both groups. CPU activity decreased rapidly within 3 h, while CPU + CPUi levels were still elevated at 7 h. CPU activity or CPU + CPUi levels were similar in in situ and peripheral samples. No correlation between CPU or CPU + CPUi and NIHSS or thrombus localization was found. The CPU system was rapidly activated and deactivated following thrombolysis and thrombectomy in stroke patients, suggesting that a CPU inhibitor would have to be administered during rtPA infusion and over the next few hours. The high CPU generation variability suggests that some patients may not respond to the treatment. EudraCT number 2017-002760-41.

Use of Actigraphy (Wearable Digital Sensors to Monitor Activity) in Heart Failure Randomized Clinical Trials: A Scoping Review.

BACKGROUND: Actigraphy-based measurements of physiologic parameters may enable design of patient-centric heart failure (HF) clinical trials. Recently, the Heart Failure Collaboratory focused on recommendations for meaningful change and use of actigraphy as an end point in HF clinical trials. We aimed to evaluate randomized controlled trials (RCTs) that have quantified the impact of HF interventions using actigraphy. METHODS: Using a scoping review strategy, we evaluated the use of actigraphy in HF RCTs. Studies were identified through electronic searches of Embase, OVID Medline, PubMed, and Cochrane Review. Data on trial characteristics and results were collected. RESULTS: We identified 11 RCTs with a total of 1,455 participants. The risk of bias across the included trials was high overall. All trials had the primary outcomes reflecting measures of either physical activity (n = 8), sleep (n = 2), or both (n = 1). Five trials evaluated response to pharmacologic therapies compared with placebo, 3 evaluated physical activity interventions, 2 evaluated group or cognitive therapy, and 1 evaluated sleep-ventilation strategy. Sample sizes ranged from 30 to 619 participants. There was significant heterogeneity relating to device type, body placement site, and handling of missing actigraphy data. Duration of monitoring ranged from 48 hours to 12 weeks. None of the studies evaluating pharmacologic therapies (n = 5) demonstrated a significant improvement of actigraphy-based primary end point measurements. CONCLUSIONS: There is significant heterogeneity in the use, methodology, and results of actigraphy-based HF RCTs. Our results highlight the need to develop, standardize, and validate actigraphy-specific outcomes for use in HF clinical trials.

Aging increases circulating BH2 without modifying BH4 levels and impairs peripheral vascular function in healthy adults.

Little is known about the mechanisms of aging on vascular beds and its relationship with tetra and di-hydrobiopterin (BH4 and BH2) levels. This observational clinical study analyzed the impact of aging on plasma and platelet biopterins, cutaneous blood flow (CBF), and coronary flow reserve (CFR) in healthy adults. The study enrolled healthy adults in 3 age groups: 18-30, 50-59, and 60-70 years (n = 25/group). Biopterins were assessed by LC-MS/MS using newly defined pre-analytical conditions limiting BH4 oxidation and improving long-term stability. CBF was measured by Laser Speckle Contrast Imaging coupled with acetylcholine-iontophoresis and CFR by adenosine stress cardiac magnetic resonance. In healthy adults, aging (60-70 years vs 18-30 years) significantly increased platelet BH2 (+75%, P = 0.033) and BH2 + BH4 (+31%, P = 0.033), and to a lesser extent plasma BH2 (+29%, P = 0.009) without affecting BH4 and BH4/BH2. Simultaneously, CBF was decreased (-23%, P = 0.004) but not CFR, CBF being inversely correlated with platelet BH2 (r = -0.42, P = 0.001) and BH2 + BH4 (r = -0.41, P = 0.002). The proportion of adults with abnormal platelet BH2 increased with age (+28% in 60-70y). These abnormal BH2 levels were significantly associated with reduced CBF and CFR (-16%, P = 0.03 and -26%, P = 0.02). In conclusion, our study showed that age-related peripheral endothelial dysfunction was associated with an increase in circulating BH2 without decreasing BH4, the effect being more marked in platelets, the most relevant blood compartment to assess biopterin bioavailability. Peripheral but not coronary vascular function is progressively impaired with aging in healthy adults. All these findings support biopterins as therapeutic targets to improve vascular function.

A genetic model of ivabradine recapitulates results from randomized clinical trials.

BACKGROUND: Naturally occurring human genetic variants provide a valuable tool to identify drug targets and guide drug prioritization and clinical trial design. Ivabradine is a heart rate lowering drug with protective effects on heart failure despite increasing the risk of atrial fibrillation. In patients with coronary artery disease without heart failure, the drug does not protect against major cardiovascular adverse events prompting questions about the ability of genetics to have predicted those effects. This study evaluates the effect of a variant in HCN4, ivabradine's drug target, on safety and efficacy endpoints. METHODS: We used genetic association testing and Mendelian randomization to predict the effect of ivabradine and heart rate lowering on cardiovascular outcomes. RESULTS: Using data from the UK Biobank and large GWAS consortia, we evaluated the effect of a heart rate-reducing genetic variant at the HCN4 locus encoding ivabradine's drug target. These genetic association analyses showed increases in risk for atrial fibrillation (OR 1.09, 95% CI: 1.06-1.13, P = 9.3 ×10-9) in the UK Biobank. In a cause-specific competing risk model to account for the increased risk of atrial fibrillation, the HCN4 variant reduced incident heart failure in participants that did not develop atrial fibrillation (HR 0.90, 95% CI: 0.83-0.98, P = 0.013). In contrast, the same heart rate reducing HCN4 variant did not prevent a composite endpoint of myocardial infarction or cardiovascular death (OR 0.99, 95% CI: 0.93-1.04, P = 0.61). CONCLUSION: Genetic modelling of ivabradine recapitulates its benefits in heart failure, promotion of atrial fibrillation, and neutral effect on myocardial infarction.

Genetic drug target validation using Mendelian randomisation.

Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the 'no horizontal pleiotropy assumption' is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses.

Comparison of Outcome Adjudication by Investigators and by a Central End Point Committee in Heart Failure Trials: Experience of the SHIFT Heart Failure Study.

BACKGROUND: The usefulness of adjudication by central end point committees (CECs) is poorly assessed in heart failure (HF) trials. We aimed to assess its impact on the outcome of the SHIFT trial (Systolic HF Treatment With the If Inhibitor Ivabradine Trial). METHODS: SHIFT was a randomized placebo-controlled trial investigating the effect of ivabradine in 6505 HF patients with reduced ejection fraction. Prespecified end points, reported by investigators (all cardiologists) using specific case report form pages, included all-cause and specific causes of deaths and hospitalizations. The primary end point was a composite of cardiovascular deaths or hospitalizations for worsening HF. We compared the adjudication of prespecified end points made by investigators and by the CEC. RESULTS: Investigators identified 7529 prespecified end points, 6793 of which were confirmed by the CEC: 98.1% of cardiovascular deaths, 88.6% of all hospitalizations, and 84.4% of hospitalizations for worsening HF. These differences had no meaningful impact on the study results; hazard ratio for the primary composite end point: investigators, 0.83 (95% CI, 0.76-0.91) versus CEC, 0.82 (95% CI, 0.75-0.90), with similar results for each component of the primary end point (hazard ratio of 0.92 versus 0.91 for cardiovascular death and 0.78 versus 0.74 for hospitalization for worsening HF). CONCLUSIONS: Central adjudication by a CEC in the SHIFT study confirmed most of cardiovascular deaths and worsening HF hospitalizations assessed by cardiologists and did not result in a significant change of the final result as compared to investigator judgment. In this context, the benefits of CEC in blinded HF trials should be reconsidered. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02441218. URL: http://www.isrctn.com/ISRCTN70429960; Unique identifier: ISRCTN70429960.

Heart rate as an independent predictor of long term mortality of acute heart failure patients in sinus rhythm according to their ejection fraction: data from the AHEAD registry.

BACKGROUND: Heart rate (HR) at admission in patients with acute heart failure (AHF) has been shown to be an important risk marker of in-hospital mortality. However, its relation with mid and long-term prognosis as well as the impact of Ejection Fraction (EF) is unknown. Our objective was to study the relationship between long-term survival and HR at admission depending on EF in a cohort of patients hospitalized for AHF. METHODS: We analyzed the data of 2335 patients in sinus rhythm hospitalized for AHF from AHEAD registry. Patients with cardiogenic shock and AHF from surgical or non-cardiac etiology were excluded. RESULTS: Survival rates at 6 and 12 months were 84.8% and 78% respectively. Increased age, decreased diastolic BP, lack of PCI during hospitalization, increased creatinine level and increased HR (with different cut-offs according to EF categories) were found as predictors whatever the EF at 6 and 12 months. Optimal prognostic cut-offs of heart rate were identified for Heart Failure with reduced EF at 100 bpm, for Heart Failure with mid-range EF at 90 bpm and for Heart Failure with preserved EF at 80 bpm for both 6 and 12 months. CONCLUSION: Our study suggests that HR at admission appears to be an independent prognostic parameter in AHF patients in sinus rhythm irrespective of EF and can be used to classify patients according to the severity of the disease.