Translating Pressure Into Practice: Operational Characteristics of Ambulatory Hemodynamic Monitoring Program in the United States.

BACKGROUND: Ambulatory hemodynamic monitoring (AHM) using an implantable pulmonary artery pressure sensor (CardioMEMS) is effective in improving outcomes for patients with heart failure. The operations of AHM programs are crucial to clinical efficacy of AHM yet have not been described. METHODS AND RESULTS: An anonymous, voluntary, web-based survey was developed and emailed to clinicians at AHM centers in the United States. Survey questions were related to program volume, staffing, monitoring practices, and patient selection criteria. Fifty-four respondents (40%) completed the survey. Respondents were 44% (n = 24) advanced HF cardiologists and 30% (n = 16) advanced nurse practitioners. Most respondents practice at a center that implants left ventricular assist devices (70%) or performs heart transplantation (54%). Advanced practice providers provide day-to-day monitoring and management in most programs (78%), and use of protocol-driven care is limited (28%). Perceived patient nonadherence and inadequate insurance coverage are cited as the primary barriers to AHM. CONCLUSIONS: Despite broad US Food and Drug Administration approval for patients with symptoms and at increased risk for worsening heart failure, the adoption of pulmonary artery pressure monitoring is concentrated at advanced heart failure centers, and modest numbers of patients are implanted at most centers. Understanding and addressing the barriers to referral of eligible patients and to broader adoption in community heart failure programs is needed to maximize the clinical benefits of AHM.

Cell-specific discrimination of desmosterol and desmosterol mimetics confers selective regulation of LXR and SREBP in macrophages.

Activation of liver X receptors (LXRs) with synthetic agonists promotes reverse cholesterol transport and protects against atherosclerosis in mouse models. Most synthetic LXR agonists also cause marked hypertriglyceridemia by inducing the expression of sterol regulatory element-binding protein (SREBP)1c and downstream genes that drive fatty acid biosynthesis. Recent studies demonstrated that desmosterol, an intermediate in the cholesterol biosynthetic pathway that suppresses SREBP processing by binding to SCAP, also binds and activates LXRs and is the most abundant LXR ligand in macrophage foam cells. Here we explore the potential of increasing endogenous desmosterol production or mimicking its activity as a means of inducing LXR activity while simultaneously suppressing SREBP1c-induced hypertriglyceridemia. Unexpectedly, while desmosterol strongly activated LXR target genes and suppressed SREBP pathways in mouse and human macrophages, it had almost no activity in mouse or human hepatocytes in vitro. We further demonstrate that sterol-based selective modulators of LXRs have biochemical and transcriptional properties predicted of desmosterol mimetics and selectively regulate LXR function in macrophages in vitro and in vivo. These studies thereby reveal cell-specific discrimination of endogenous and synthetic regulators of LXRs and SREBPs, providing a molecular basis for dissociation of LXR functions in macrophages from those in the liver that lead to hypertriglyceridemia.

Monogenic and Polygenic Models of Coronary Artery Disease.

PURPOSE OF THE REVIEW: Coronary artery disease (CAD) is a common disease globally attributable to the interplay of complex genetic and lifestyle factors. Here, we review how genomic sequencing advances have broadened the fundamental understanding of the monogenic and polygenic contributions to CAD and how these insights can be utilized, in part by creating polygenic risk estimates, for improved disease risk stratification at the individual patient level. RECENT FINDINGS: Initial studies linking premature CAD with rare familial cases of elevated blood lipids highlighted high-risk monogenic contributions, predominantly presenting as familial hypercholesterolemia (FH). More commonly CAD genetic risk is a function of multiple, higher frequency variants each imparting lower magnitude of risk, which can be combined to form polygenic risk scores (PRS) conveying significant risk to individuals at the extremes. However, gaps remain in clinical validation of PRSs, most notably in non-European populations. With improved and more broadly utilized genomic sequencing technologies, the genetic underpinnings of coronary artery disease are being unraveled. As a result, polygenic risk estimation is poised to become a widely used and powerful tool in the clinical setting. While the use of PRSs to augment current clinical risk stratification for optimization of cardiovascular disease risk by lifestyle change or therapeutic targeting is promising, we await adequately powered, prospective studies, demonstrating the clinical utility of polygenic risk estimation in practice.

Atrial Fibrillation Genomics: Discovery and Translation.

PURPOSE OF REVIEW: Our understanding of the fundamental cellular and molecular factors leading to atrial fibrillation (AF) remains stagnant despite significant advancement in ablation and device technologies. Diagnosis and prevention strategies fall behind that of treatment, but expanding knowledge in AF genetics holds the potential to drive progress. We aim to review how an understanding of the genetic contributions to AF can guide an approach to individualized risk stratification and novel avenues in drug discovery. RECENT FINDINGS: Rare familial forms of AF identified monogenic contributions to the development of AF. Genome-wide association studies (GWAS) further identified single-nucleotide polymorphisms (SNPs) suggesting polygenic and multiplex nature of this common disease. Polygenic risk scores accounting for the multitude of associated SNPs that each confer mildly elevated risk have been developed to translate genetic information into clinical practice, though shortcomings remain. Additionally, novel laboratory methods have been empowered by recent genetic findings to enhance drug discovery efforts. AF is increasingly recognized as a disease with a significant genetic component. With expanding sequencing technologies and accessibility, polygenic risk scores can help identify high risk individuals. Advancement in digital health tools, artificial intelligence and machine learning based on standard electrocardiograms, and genomic driven drug discovery may be integrated to deliver a sophisticated level of precision medicine in this modern era of emphasis on prevention. Randomized, prospective studies to demonstrate clinical benefits of these available tools are needed to validate this approach.

Understanding the use of observational and randomized data in cardiovascular medicine.

The availability of large datasets from multiple sources [e.g. registries, biobanks, electronic health records (EHRs), claims or billing databases, implantable devices, wearable sensors, and mobile apps], coupled with advances in computing and analytic technologies, have provided new opportunities for conducting innovative health research. Equally, improved digital access to health information has facilitated the conduct of efficient randomized controlled trials (RCTs) upon which clinical management decisions can be based, for instance, by permitting the identification of eligible patients for recruitment and/or linkage for follow-up via their EHRs. Given these advances in cardiovascular data science and the complexities they behold, it is important that health professionals have clarity on the appropriate use and interpretation of observational, so-called 'real-world', and randomized data in cardiovascular medicine. The Cardiovascular Roundtable of the European Society of Cardiology (ESC) held a workshop to explore the future of RCTs and the current and emerging opportunities for gathering and exploiting complex observational datasets in cardiovascular research. The aim of this article is to provide a perspective on the appropriate use of randomized and observational data and to outline the ESC plans for supporting the collection and availability of clinical data to monitor and improve the quality of care of patients with cardiovascular disease in Europe and provide an infrastructure for undertaking pragmatic RCTs. Moreover, the ESC continues to campaign for greater engagement amongst regulators, industry, patients, and health professionals in the development and application of a more efficient regulatory framework that is able to take maximal advantage of new opportunities for improving the design and efficiency of observational studies and RCT in patients with cardiovascular disease.

Technology-Enabled Clinical Trials: Transforming Medical Evidence Generation.

The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.

Validation of a genetic risk score for atrial fibrillation: A prospective multicenter cohort study.

BACKGROUND: Atrial fibrillation (AF) is the most commonly encountered arrhythmia and is associated with an elevated risk of stroke. Improving the identification of patients with the highest risk for AF to enable appropriate surveillance and treatment, if necessary, is critical to reducing AF-associated morbidity and mortality. Multiple common single nucleotide polymorphisms (SNPs) are unequivocally associated with the lifetime risk of AF. In the current study we aimed to prospectively validate an AF genetic risk score (GRS) in previously undiagnosed patients at risk for AF. METHODS AND FINDINGS: Individuals 40 years of age or older with 1 clinical risk factor for AF, presenting with symptoms of AF, or with a first diagnosis of AF, were enrolled for genetic testing and ambulatory cardiac rhythm monitoring with an adhesive patch monitor or a long-term Holter monitor (mean wear time 10 days 21 hours and 13 days 18 hours, respectively). An AF event was the first diagnosis of AF by ECG, patch monitor, or long-term Holter monitor. The AF GRS was determined for each participant based on the weighted contribution of 12 genetic risk loci. Of 904 participants, 85 manifested AF. Their mean age was 66.2 (SD 11.8) years; 38% of participants were male. Participants in the highest quintile of AF GRS were more likely (odds ratio 3.11; 95% CI 1.27-7.58; p = 0.01) to have had an AF event than participants in the lowest quintile after adjusting for age, sex, smoking status, BMI, hypertension, diabetes mellitus, heart failure, and prior myocardial infarction. Study limitations included an ethnically homogenous population, a restricted rhythm monitoring period, and the evolving discovery of SNPs associated with AF. CONCLUSIONS: Prospective assessment of a GRS for AF identified participants with elevated risk of AF beyond established clinical criteria. Accordingly, a GRS for AF could be incorporated into overall risk assessment to better identify patients at the highest risk of developing AF, although further testing in larger populations is needed to confirm these findings. TRIAL REGISTRATION: ClinicalTrials.gov NCT01970969.

Dissociated sterol-based liver X receptor agonists as therapeutics for chronic inflammatory diseases.

Liver X receptor (LXR), a nuclear hormone receptor, is an essential regulator of immune responses. Activation of LXR-mediated transcription by synthetic agonists, such as T0901317 and GW3965, attenuates progression of inflammatory disease in animal models. However, the adverse effects of these conventional LXR agonists in elevating liver lipids have impeded exploitation of this intriguing mechanism for chronic therapy. Here, we explore the ability of a series of sterol-based LXR agonists to alleviate inflammatory conditions in mice without hepatotoxicity. We show that oral treatment with sterol-based LXR agonists in mice significantly reduces dextran sulfate sodium colitis-induced body weight loss, which is accompanied by reduced expression of inflammatory markers in the large intestine. The anti-inflammatory property of these agonists is recapitulated in vitro in mouse lamina propria mononuclear cells, human colonic epithelial cells, and human peripheral blood mononuclear cells. In addition, treatment with LXR agonists dramatically suppresses inflammatory cytokine expression in a model of traumatic brain injury. Importantly, in both disease models, the sterol-based agonists do not affect the liver, and the conventional agonist T0901317 results in significant liver lipid accumulation and injury. Overall, these results provide evidence for the development of sterol-based LXR agonists as novel therapeutics for chronic inflammatory diseases.-Yu, S., Li, S., Henke, A., Muse, E. D., Cheng, B., Welzel, G., Chatterjee, A. K., Wang, D., Roland, J., Glass, C. K., Tremblay, M. Dissociated sterol-based liver X receptor agonists as therapeutics for chronic inflammatory diseases.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

A pilot study exploring novel contexts for out-of-office blood pressure measurement.

Introduction: Out-of-office blood pressure (BP) monitoring is increasingly valuable in the diagnosis and management of hypertension. With advances in wearable BP technologies, the ability to gain insight into BP outside of traditional centers of care has expanded greatly. Methods: Here we explore the usability of a novel, wrist-worn BP cuff monitor for out-of-office data collection with participants following digital cues rather than in-person instruction. Transmitted measurements were used to evaluate BP variation with the time of day and day of week, BP variation with mood, and orthostatic measurements. Results: Fifty participants, with a mean age of 44.5 years, were enrolled and received the BP monitor. 82% of the participants transmitted data via the smartphone application, and the median wear time of the device during the 4-week study was 11 days (IQR 8-17). Discussion: This prospective digital pilot study illustrates the usability of wearable oscillometric BP technology combined with digital cues via a smartphone application to obtain complex out-of-office BP measurements, including orthostatic vital signs and BP associated with emotion. 25 out of 32 participants who attempted orthostatic vital signs based on in-app instruction were able to do so correctly, while 24 participants transmitted BP readings associated with emotion, with a significant difference in BP noted between calm and stressed emotional states.

AI-enhanced reconstruction of the 12-lead electrocardiogram via 3-leads with accurate clinical assessment.

The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of ECG features consistent with acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC = 0.95). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4 ± 5.0% in identifying ECG features of ST-segment elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6 ± 4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings.

AI-Enhanced Reconstruction of the 12-Lead Electrocardiogram via 3-Leads with Accurate Clinical Assessment.

The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a full 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed synthetic 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC=0.94). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4±5.0% in identifying ST elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6±4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings.