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Patients with heart failure with reduced ejection fraction (HFrEF) and worsening HF events (WHFE) represent a distinct subset of patients with a substantial comorbidity burden, greater potential for intolerance to medical therapy, and high risk of subsequent death, hospitalization and excessive healthcare costs. Although multiple therapies have been shown to be efficacious and safe in this high-risk population, there are limited real-world data regarding factors that impact clinical decision-making when initiating or modifying therapy. Likewise, prior analyses of US clinical practice support major gaps in medical therapy for HFrEF and few medication changes during longitudinal follow-up, yet granular data on reasons why clinicians do not initiate or up-titrate guideline-directed medication are lacking.We designed the CHART-HF study, an observational study of approximately 1,500 patients comparing patients with and without WHFE (WHFE defined as receipt of intravenous diuretics in the inpatient, outpatient, or emergency department setting) who had an index outpatient visit in the US between 2017 and 2019. Patient-level data on clinical characteristics, clinical outcomes, and therapy will be collected from 2 data sources: a single integrated health system, and a national panel of cardiologists. Furthermore, clinician-reported rationale for treatment decisions and the factors prioritized with selection and optimization of therapies in real-world practice will be obtained. To characterize elements of clinician decision-making not documented in the medical record, the panel of cardiologists will review records of patients seen under their care to explicitly note their primary reason for initiating, discontinuing, and titrating medications specific medications, as well as the reason for not making changes to each medication during the outpatient visit.Results from CHART-HF have the potential to detail real-world US practice patterns regarding care of patients with HFrEF with versus without a recent WHFE, to examine clinician-reported reasons for use and non-use of guideline-directed medical therapy, and to characterize the magnitude and nature of clinical inertia toward evidence-based medication changes for HFrEF.

The relation of high-sensitivity cardiac troponin I (hs-cTnI) concentration and presence of obstructive coronary artery disease (CAD) in patients without myocardial infarction (MI) is unclear. Study participants selected from patients free of MI who underwent coronary angiography with or without intervention were enrolled, and hs-cTnI measured. A gradient boosting model was implemented to build a model for detection of CAD. Cox proportional hazard regression was used to assess the association of hs-cTnI and adverse cardiovascular (CV) outcome. Among 978 study participants, 607 patients (62%) had CAD. Higher concentrations of hs-cTnI were associated with chronic kidney disease, heart failure, CAD, male gender, current tobacco use, anemia, age, and low-density lipoprotein cholesterol. History of CAD, male gender, type 2 diabetes mellitus, hs-cTnI, anemia, age, and high-density lipoprotein cholesterol were the most influential factors for detection of CAD. The gradient boosting model had an area under the curve of 0.82, accuracy of 75%, sensitivity of 88%, specificity of 52%, positive predictive value of 76%, and negative predictive value of 72% for detection of CAD. Increase in 1 log unit of hs-cTnI was significantly associated with increased risk of incident MI (hazard ratio [HR] 1.34, 95% confidence interval [CI] 1.22 to 1.47, p0.001), CV mortality (HR 1. 24, 95% CI 1.11 to 1.39, p0.001), and composite of incident MI or CV mortality (HR 1.29, 95% CI 1.20 to 1.40, p0.001). In conclusion, among patients without acute MI and CAD, higher concentrations of hs-cTnI were associated with the presence of CAD and linked to increased risk of future CV events. ClinicalTrials.gov Identifier: NCT00842868.

Contains fulltext : 283514.pdf (Publisher’s version ) (Closed access) BACKGROUND: Inflammation, measured by traditional biomarkers such as C-reactive protein, has been linked to cardiovascular (CV) events. Recent technological advancement has allowed for measuring larger numbers of inflammatory biomarkers. A contemporary evaluation with established and novel biomarkers of inflammation is needed. METHODS: 1,090 individuals who underwent coronary angiography were enrolled. Twenty-four inflammatory biomarkers were collected prior to angiography. Unsupervised machine learning cluster analyses determined unique patterns of inflammatory biomarkers. Cox proportional hazard regression assessed both association of inflammatory biomarker clusters and individual biomarker associations with major adverse cardiovascular events (MACE; non-fatal myocardial infarction or stroke, and CV death) during a median follow-up of 3.67 years. RESULTS: Four distinct clusters were recognized. Incremental increases in inflammatory biomarkers were observed from cluster 1 to cluster 4. During follow-up, 263 MACE were ascertained. Considering cluster 1 as a reference, study participants with inflammatory cluster 2 (Hazard ratio [HR] 1.55, 95% confidence interval [CI]: 1.01-2.37), cluster 3 (HR 1.89, CI: 1.25-2.85), and cluster 4 (HR 2.93, CI: 1.95-4.42) were at increased risk of MACE. Interleukin (IL)-1α IL-6, IL-8, IL-10, IL-12, Adhesion molecule-1 high-sensitivity C-reactive protein, ferritin, myeloperoxidase, macrophage inflammatory protein (MIP)-1a, MIP 3, and macrophage colony-stimulating factor-1 were independently associated with MACE. CONCLUSIONS: Among persons undergoing coronary angiography procedures, distinct clusters of inflammatory biomarker distributions with significant prognostic meaning may be identified. These results may identify unique targets for anti-inflammatory treatments aimed at CV disease.

Objectives The goal of this study was to assess the predictive power of N-terminal pro–B-type natriuretic peptide (NT-proBNP) and the decision cutoffs in heart failure (HF) across body mas...

Background Higher body mass index (BMI) is associated with better outcome compared with normal weight in patients with HF and other chronic diseases. It remains uncertain whether the apparent protective role of obesity relates to the absence of comorbidities. Therefore, we investigated the effect of BMI on outcome in younger patients without co-morbidities as compared to older patients with co-morbidities in a large heart failure (HF) population. Methods In an individual patient data analysis from pooled cohorts, 5,819 patients with chronic HF and data available on BMI, co-morbidities and outcome were analysed. Patients were divided into four groups based on BMI (i.e. ≤ 18.5 kg/m2, 18.5–25.0 kg/m2; 25.0–30.0 kg/m2; 30.0 kg/m2). Primary endpoints included all-cause mortality and HF hospitalization-free survival. Results Mean age was 65 ± 12 years, with a majority of males (78%), ischaemic HF and HF with reduced ejection fraction. Frequency of all-cause mortality or HF hospitalization was significantly worse in the lowest two BMI groups as compared to the other two groups; however, this effect was only seen in patients older than 75 years or having at least one relevant co-morbidity, and not in younger patients with HF only. After including medications and N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin concentrations into the model, the prognostic impact of BMI was largely absent even in the elderly group with co-morbidity. Conclusions The present study suggests that obesity is a marker of less advanced disease, but does not have an independent protective effect in patients with chronic HF. Graphic abstract Categories of BMI are only predictive of poor outcome in patients aged > 75 years or with at least one co-morbidity (bottom), but not in those aged

Background: Novel targeted treatments increase the need for prompt hypertrophic cardiomyopathy (HCM) detection. However, its low prevalence (0.5%) and resemblance to common diseases present challenges that may benefit from automated machine learning–based approaches. We aimed to develop machine learning models to detect HCM and to differentiate it from other cardiac conditions using ECGs and echocardiograms, with robust generalizability across multiple cohorts. Methods: Single-institution HCM ECG models were trained and validated on external data. Multi-institution models for ECG and echocardiogram were trained on data from 3 academic medical centers in the United States and Japan using a federated learning approach, which enables training on distributed data without data sharing. Models were validated on held-out test sets for each institution and from a fourth academic medical center and were further evaluated for discrimination of HCM from aortic stenosis, hypertension, and cardiac amyloidosis. Last, automated detection was compared with manual interpretation by 3 cardiologists on a data set with a realistic HCM prevalence. Results: We identified 74 376 ECGs for 56 129 patients and 8392 echocardiograms for 6825 patients at the 4 academic medical centers. Although ECG models trained on data from each institution displayed excellent discrimination of HCM on internal test data (C statistics, 0.88–0.93), the generalizability was limited, most notably for a model trained in Japan and tested in the United States (C statistic, 0.79–0.82). When trained in a federated manner, discrimination of HCM was excellent across all institutions (C statistics, 0.90–0.96 and 0.90–0.96 for ECG and echocardiogram model, respectively), including for phenotypic subgroups. The models further discriminated HCM from hypertension, aortic stenosis, and cardiac amyloidosis (C statistics, 0.84, 0.83, and 0.88, respectively, for ECG and 0.93, 0.94, 0.85, respectively, for echocardiogram). Analysis of electrocardiography-echocardiography paired data from 11 823 patients from an external institution indicated a higher sensitivity of automated HCM detection at a given positive predictive value compared with cardiologists (0.98 versus 0.81 at a positive predictive value of 0.01 for ECG and 0.78 versus 0.59 at a positive predictive value of 0.24 for echocardiogram). Conclusions: Federated learning improved the generalizability of models that use ECGs and echocardiograms to detect and differentiate HCM from other causes of hypertrophy compared with training within a single institution.

BackgroundThe global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing the ongoing coronavirus disease 2019 (COVID-19) pandemic has raised serious concern for patients with chronic disease. A correlation has been identified between the severity of COVID-19 and a patient’s preexisting comorbidities. Although COVID-19 primarily involves the respiratory system, dysfunction in multiple organ systems is common, particularly in the cardiovascular, gastrointestinal, immune, renal, and nervous systems. Patients with amyloid transthyretin (ATTR) amyloidosis represent a population particularly vulnerable to COVID-19 morbidity due to the multisystem nature of ATTR amyloidosis.Main bodyATTR amyloidosis is a clinically heterogeneous progressive disease, resulting from the accumulation of amyloid fibrils in various organs and tissues. Amyloid deposition causes multisystem clinical manifestations, including cardiomyopathy and polyneuropathy, along with gastrointestinal symptoms and renal dysfunction. Given the potential for exacerbation of organ dysfunction, physicians note possible unique challenges in the management of patients with ATTR amyloidosis who develop multiorgan complications from COVID-19. While the interplay between COVID-19 and ATTR amyloidosis is still being evaluated, physicians should consider that the heightened susceptibility of patients with ATTR amyloidosis to multiorgan complications might increase their risk for poor outcomes with COVID-19.ConclusionPatients with ATTR amyloidosis are suspected to have a higher risk of morbidity and mortality due to age and underlying ATTR amyloidosis-related organ dysfunction. While further research is needed to characterize this risk and management implications, ATTR amyloidosis patients might require specialized management if they develop COVID-19. The risks of delaying diagnosis or interrupting treatment for patients with ATTR amyloidosis should be balanced with the risk of exposure in the health care setting. Both physicians and patients must adapt to a new construct for care during and possibly after the pandemic to ensure optimal health for patients with ATTR amyloidosis, minimizing treatment interruptions.

Understanding trends in cardiovascular (CV) risk factors and CV disease according to age, sex, race, and ethnicity is important for policy planning and public health interventions.The goal of this study was to project the number of people with CV risk factors and disease and further explore sex, race, and ethnical disparities.The prevalence of CV risk factors (diabetes mellitus, hypertension, dyslipidemia, and obesity) and CV disease (ischemic heart disease, heart failure, myocardial infarction, and stroke) according to age, sex, race, and ethnicity was estimated by using logistic regression models based on 2013-2018 National Health and Nutrition Examination Survey data and further combining them with 2020 U.S. Census projection counts for years 2025-2060.By the year 2060, compared with the year 2025, the number of people with diabetes mellitus will increase by 39.3% (39.2 million [M] to 54.6M), hypertension by 27.2% (127.8M to 162.5M), dyslipidemia by 27.5% (98.6M to 125.7M), and obesity by 18.3% (106.3M to 125.7M). Concurrently, projected prevalence will similarly increase compared with 2025 for ischemic heart disease by 31.1% (21.9M to 28.7M), heart failure by 33.0% (9.7M to 12.9M), myocardial infarction by 30.1% (12.3M to 16.0M), and stroke by 34.3% (10.8M to 14.5M). Among White individuals, the prevalence of CV risk factors and disease is projected to decrease, whereas significant increases are projected in racial and ethnic minorities.Large future increases in CV risk factors and CV disease prevalence are projected, disproportionately affecting racial and ethnic minorities. Future health policies and public health efforts should take these results into account to provide quality, affordable, and accessible health care.

This is the peer reviewed version of the following article: Aimo, A., Januzzi, J.L., Vergaro, G., Richards, A.M., Lam, C.S.P., Latini, R. ... Emdin, M. (2020). Circulating levels and prognostic value of soluble ST2 in heart failure are less influenced by age than N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T, European Journal of Heart Failure, which has been published in final form at https://doi.org/10.1002/ejhf.1701. . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Aims - N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP), high‐sensitivity troponin T (hs‐TnT) and soluble suppression of tumorigenesis‐2 (sST2) predict outcome in chronic heart failure (HF). We assessed the influence of age on circulating levels and prognostic significance of these biomarkers. Methods and results - Individual data from 5301 patients with chronic HF and NT‐proBNP, hs‐TnT, and sST2 data were evaluated. Patients were stratified according to age: Conclusions - Soluble ST2 is less influenced by age than NT‐proBNP or hs‐TnT; all these biomarkers predict outcome regardless of age. The use of age‐ and outcome‐specific cut‐offs of NT‐proBNP, hs‐TnT and sST2 allows more accurate risk stratification than NT‐proBNP alone or the combination of NT‐proBNP and hs‐TnT.

Aims Concentrations of insulin‐like growth factor binding protein‐7 (IGFBP7) have been linked to abnormal cardiac structure and function in patients with chronic heart failure (HF), but cardiovascular correlates of the biomarker in patients with more acute presentations are lacking. We aimed to determine the relationship between IGFBP7 concentrations and cardiac structure and to evaluate the impact of IGFBP7 on the diagnosis of acute HF among patients with acute dyspnoea. Methods and results In this pre‐specified subgroup analysis of the International Collaborative of N‐terminal pro‐B‐type Natriuretic Peptide Re‐evaluation of Acute Diagnostic Cut‐Offs in the Emergency Department (ICON‐RELOADED) study, we included 271 patients with and without acute HF. All patients presented to an emergency department with acute dyspnoea, had blood samples for IGFBP7 measurement, and detailed echocardiographic evaluation. Higher IGFBP7 concentrations were associated with numerous cardiac abnormalities, including increased left atrial volume index (LAVi; r = 0.49, P

Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function. Accurate segmentation of cardiac structures from cine MRI is a crucial step for early diagnosis and prognosis of CVD, and has been greatly improved with convolutional neural networks (CNN). There, however, are a number of limitations identified in CNN models, such as limited interpretability and high complexity, thus limiting their use in clinical practice. In this work, to address the limitations, we propose a lightweight and interpretable machine learning model, successive subspace learning with the subspace approximation with adjusted bias (Saab) transform, for accurate and efficient segmentation from cine MRI. Specifically, our segmentation framework is comprised of the following steps: (1) sequential expansion of near-to-far neighborhood at different resolutions; (2) channel-wise subspace approximation using the Saab transform for unsupervised dimension reduction; (3) class-wise entropy guided feature selection for supervised dimension reduction; (4) concatenation of features and pixel-wise classification with gradient boost; and (5) conditional random field for post-processing. Experimental results on the ACDC 2017 segmentation database, showed that our framework performed better than state-of-the-art U-Net models with 200$\times$ fewer parameters in delineating the left ventricle, right ventricle, and myocardium, thus showing its potential to be used in clinical practice., Comment: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021)

Aims Limited evidence exists on sex-related differences in clinical value of biomarkers in chronic heart failure (HF). We aimed to define plasma levels, determinants, and optimal prognostic cut-offs of soluble suppression of tumourigenesis-2 (sST2), high-sensitivity troponin T (hs-TnT), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in female and male chronic HF patients. Methods and results Individual data of patients from the BIOS (Biomarkers In Heart Failure Outpatient Study) Consortium with sST2, hs-TnT, and NT-proBNP measured were analysed. The primary endpoint was a composite of 1-year cardiovascular death and HF hospitalization. The secondary endpoints were 5-year cardiovascular and all-cause death. The cohort included 4540 patients (age: 67 ± 12 years, LVEF 33 ± 13%, 1111 women, 25%). Women showed lower sST2 (24 vs. 27 ng/ml, P Conclusions In patients with chronic HF, levels of sST2 and hs-TnT, but not of NT-proBNP are lower in women. Lower sST2 and hs-TnT and higher NT-proBNP cut-offs for risk stratification could be used in women.

The 2013 (with updates in 2016 and 2017) American College of Cardiology/American Heart Association and 2016 European Society of Cardiology guidelines provide practical evidence-based clinical guidelines for the diagnosis and treatment of both acute and chronic heart failure (HF). Both guidelines address noninvasive and invasive testing to establish the diagnosis of HF with reduced ejection fraction and HF with preserved ejection fraction. Extensive trial evidence supports the use of guideline-directed medical therapy and device-based therapies for the optimal management of patients with HF with reduced ejection fraction. Specific recommendations are also provided for HF with preserved ejection fraction although the evidence is substantially weaker. Management of medical comorbidities is now addressed in both guidelines. Acute HF and end-stage disease requiring advanced therapies are also discussed. This review compares specific recommendations across the spectrum of HF phenotypes and disease severity, highlights areas where differences exist, and lists consequential studies published since the latest guidelines.

Background With sacubitril/valsartan treatment, B-type natriuretic peptide (BNP) concentrations increase; it remains unclear whether change in BNP concentrations is similar across all assays for its measurement. Effects of sacubitril/valsartan on atrial natriuretic peptide (ANP) concentrations in patients are unknown. Lastly, the impact of neprilysin inhibition on mid-regional pro-ANP (MR-proANP), N-terminal pro-BNP (NT-proBNP), proBNP1-108, or C-type natriuretic peptide (CNP) is not well understood. Objectives This study sought to examine the effects of sacubitril/valsartan on results from different natriuretic peptide assays. Methods Twenty-three consecutive stable patients with heart failure and reduced ejection fraction were initiated and titrated on sacubitril/valsartan. Change in ANP, MR-proANP, BNP (using 5 assays), NT-proBNP (3 assays), proBNP1-108, and CNP were measured over 3 visits. Results Average time to 3 follow-up visits was 22, 46, and 84 days. ANP rapidly and substantially increased with initiation and titration of sacubitril/valsartan, more than doubling by the first follow-up visit (+105.8%). Magnitude of ANP increase was greatest in those with concentrations above the median at baseline (+188%) compared with those with lower baseline concentrations (+44%); ANP increases were sustained. Treatment with sacubitril/valsartan led to inconsistent changes in BNP, which varied across methods assessed. Concentrations of MR-proANP, NT-proBNP, and proBNP1-108 variably declined after treatment; whereas CNP concentrations showed no consistent change. Conclusions Initiation and titration of sacubitril/valsartan led to variable changes in concentrations of multiple natriuretic peptides. These results provide important insights into the effects of sacubitril/valsartan treatment on individual patient results, and further suggest the benefit of neprilysin inhibition may be partially mediated by increased ANP concentrations.

Among patients with acute dyspnea, concentrations of N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T, and insulin-like growth factor binding protein-7 predict cardiovascular outcomes and death. Understanding the optimal means to interpret these elevated biomarkers in patients presenting with acute dyspnea remains unknown.Concentrations of NT-proBNP, high-sensitivity cardiac troponin T, and insulin-like growth factor binding protein-7 were analyzed in 1448 patients presenting with acute dyspnea from the prospective, multicenter International Collaborative of NT-proBNP-Re-evaluation of Acute Diagnostic Cut-Offs in the Emergency Department (ICON-RELOADED) Study. Eight biogroups were derived based upon patterns in biomarker elevation at presentation and compared for differences in baseline characteristics. Of 441 patients with elevations in all 3 biomarkers, 218 (49.4%) were diagnosed with acute heart failure (HF). The frequency of acute HF diagnosis in this biogroup was higher than those with elevations in 2 biomarkers (18.8%, 44 of 234), 1 biomarker (3.8%, 10 of 260), or no elevated biomarkers (0.4%, 2 of 513). The absolute number of elevated biomarkers on admission was prognostic of the composite end point of mortality and HF rehospitalization. In adjusted models, patients with one, 2, and 3 elevated biomarkers had 3.74 (95% confidence interval [CI], 1.26-11.1, P = .017), 12.3 (95% CI, 4.60-32.9, P.001), and 12.6 (95% CI, 4.54-35.0, P.001) fold increased risk of 180-day mortality or HF rehospitalization.A multimarker panel of NT-proBNP, hsTnT, and IGBFP7 provides unique clinical, diagnostic, and prognostic information in patients presenting with acute dyspnea. Differences in the number of elevated biomarkers at presentation may allow for more efficient clinical risk stratification of short-term mortality and HF rehospitalization.

Although individually uncommon, rare diseases collectively affect over 350 million patients worldwide and are increasingly the target of therapeutic development efforts1. Unfortunately, the pursuit and use of such therapies have been hindered by a common challenge: patients with specific rare diseases are difficult to identify, especially if the conditions resemble more prevalent disorders. Cardiac amyloidosis is one such rare disease, which is characterized by deposition of misfolded proteins within the heart muscle resulting in heart failure and death2–4. In recent years, specific therapies have emerged for cardiac amyloidosis5,6and several more are under investigation7–9, but because cardiac amyloidosis is mistaken for common forms of heart failure, it is typically diagnosed late in its course. As a possible solution, artificial intelligence methods could enable automated detection of rare diseases, but model performance must address low disease prevalence. Here we present an automated multi-modality pipeline for cardiac amyloidosis detection using two neural-network models; one using electrocardiograms (ECG) and the second using echocardiographic videos as input. These models were trained and validated on 3 and 5 academic medical centers (AMC), respectively, in the United States and Japan. Both models had excellent accuracy for detecting cardiac amyloidosis with C-statistics of 0.85-0.92 and 0.91-1.00 for the ECG and echocardiography models, respectively, with the latter outperforming expert diagnosis. Simulating deployment on 13,906 and 7,775 patients with ECG-echocardiography paired data for AMC2 and AMC3 indicated a positive predictive value (PPV) for the ECG model of 4% and 3% at 61% and 54% recall, respectively. Pre-screening with ECG enhanced the echocardiography model performance from PPV 23% and 20% to PPV 58% and 53% at 64% recall, respectively for AMC2 and AMC3. In conclusion, we have developed a robust pipeline to augment detection of cardiac amyloidosis, which should serve as a generalizable strategy for other rare and intermediate frequency cardiac diseases with established or emerging therapies.

Background Chronic obstructive pulmonary disease (COPD) is a frequent comorbidity in patients with heart failure (HF). We assessed the influence of COPD on circulating levels and prognostic value of 3 HF biomarkers: N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hs-TnT), and soluble suppression of tumorigenesis-2 (sST2). Methods Individual data from patients with chronic HF, known COPD status and NT-proBNP, hs-TnT, sST2 values (n=13328) were analysed. Results As compared to patients without COPD, those with COPD (n=2155, 16%) were older (age 71 years [64–77] vs. 66 [57–75]; p Conclusions Among patients with HF, those with COPD have higher circulating cardiac biomarkers. Patient classification based on COPD-specific cut-offs refines risk reclassification for all-cause and cardiovascular mortality and HF hospitalization and might be helpful for decision making and management. Funding Acknowledgement Type of funding sources: None.

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Background: Proteomics have already provided novel insights into the pathophysiology of heart failure (HF) with reduced ejection fraction. Previous studies have evaluated cross-sectional protein signatures of HF, but few have characterized proteomic changes following HF with reduced ejection fraction treatment with ARNI (angiotensin receptor/neprilysin inhibitor) therapy or left ventricular assist devices. Methods: In this retrospective omics study, we performed targeted proteomics (N=625) of whole blood sera from patients with American College of Cardiology/American Heart Association stage D (N=29) and stage C (N=12) HF using proximity extension assays. Samples were obtained before and after (median=82 days) left ventricular assist device implantation (stage D; primary analysis) and ARNI therapy initiation (stage C; matched reference). Oblique principal component analysis and point biserial correlations were used for feature extraction and selection; standardized mean differences were used to assess within and between-group differences; and enrichment analysis was used to generate and cluster Gene Ontology terms. Results: Core sets of proteins were identified for stage C (N=9 proteins) and stage D (N=18) HF; additionally, a core set of 5 shared HF proteins (NT-proBNP [N-terminal pro-B type natriuretic peptide], ESM [endothelial cell-specific molecule]-1, cathepsin L1, osteopontin, and MCSF-1) was also identified. For patients with stage D HF, moderate (δ, 0.40–0.60) and moderate-to-large (δ, 0.60–0.80) sized differences were observed in 8 of their 18 core proteins after left ventricular assist devices implantation. Additionally, specific protein groups reached concentration levels equivalent ( g Conclusions: HF with reduced ejection fraction severity associates with distinct proteomic signatures that reflect underlying disease attributes; these core signatures may be useful for monitoring changes in cardiac function following initiation on ARNI or left ventricular assist device implantation.

Background Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs‐cTn (high‐sensitivity cardiac troponin)–based proteomic model to diagnose obstructive coronary artery disease. Methods and Results In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs‐cTnI [high‐sensitivity cardiac troponin I], adiponectin, and kidney injury molecule‐1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% ( P P Conclusions A model including hs‐cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs‐cTnI concentrations.

BACKGROUND Due to the complexity and chronicity of heart failure, engaging yet simple patient self-management tools are needed. OBJECTIVE This study aimed to assess the feasibility and patient engagement with a smartphone app designed for heart failure. METHODS Patients with heart failure were randomized to intervention (smartphone with the Habits Heart App installed and Bluetooth-linked scale) or control (paper education material) groups. All intervention group patients were interviewed and monitored closely for app feasibility while receiving standard of care heart failure management by cardiologists. The Atlanta Heart Failure Knowledge Test, a quality of life survey (Kansas City Cardiomyopathy Questionnaire), and weight were assessed at baseline and final visits. RESULTS Patients (N=28 patients; intervention: n=15; control: n=13) with heart failure (with reduced ejection fraction: 15/28, 54%; male: 20/28, 71%, female: 8/28, 29%; median age 63 years) were enrolled, and 82% of patients (N=23; intervention: 12/15, 80%; control: 11/13, 85%) completed both baseline and final visits (median follow up 60 days). In the intervention group, 2 out of the 12 patients who completed the study did not use the app after study onboarding due to illnesses and hospitalizations. Of the remaining 10 patients who used the app, 5 patients logged ≥1 interaction with the app per day on average, and 2 patients logged an interaction with the app every other day on average. The intervention group averaged 403 screen views (per patient) in 56 distinct sessions, 5-minute session durations, and 22 weight entries per patient. There was a direct correlation between duration of app use and improvement in heart failure knowledge (Atlanta Heart Failure Knowledge Test score; ρ=0.59, P=.04) and quality of life (Kansas City Cardiomyopathy Questionnaire score; ρ=0.63, P=.03). The correlation between app use and weight change was ρ=–0.40 (P=.19). Only 1 out of 11 patients in the control group retained education material by the follow-up visit. CONCLUSIONS The Habits Heart App with a Bluetooth-linked scale is a feasible way to engage patients in heart failure management, and barriers to app engagement were identified. A larger multicenter study may be warranted to evaluate the effectiveness of the app. CLINICALTRIAL ClinicalTrials.gov NCT03238729; http://clinicaltrials.gov/ct2/show/NCT03238729

Background Chronic obstructive pulmonary disease (COPD) is common in patients with chronic heart failure (CHF). Purpose We aimed to explore the impact of COPD on HF biomarkers (N-terminal fraction of pro-B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin T [hs-TnT], and soluble suppression of tumorigenesis-2 [sST2]) and outcome. Methods Individual data from 14 cohorts of patients with stable chronic HF and NT-proBNP and hs-TnT values were analysed. Patients with known COPD status were evaluated. Results Patients (n=13,178) were aged 67 years (58–75), 75% males, and 76%, 11%, 13% with HF with reduced, mid-range, or preserved ejection fraction (HFrEF/HFmrEF/HFpEF), respectively. Patients with COPD were older than those without COPD (age 71 years [64–77] vs. 66 [57–75]; p Over a median follow-up of 2.1 years (1.5–3.7, range 0–18 years), 3,865/12,489 patients (31%) died; among them, 2,443/12,450 (20%) died for cardiovascular causes; 3,373/12,469 patients (27%) were hospitalized for HF over 35 months (15–63, range 0–216 months). Patients with COPD had a significantly higher all-cause mortality, cardiovascular mortality, and worse survival free from HF hospitalization (all p Conclusions COPD in HF is characterized by higher NT-proBNP, hs-TnT and sST2 levels. COPD adds prognostic significance over NT-proBNP alone, but not over the combination of NT-proBNP, hs-TnT, and sST2. Funding Acknowledgement Type of funding source: None

Central Illustration: The Potential Implications of Including Type 2 Myocardial Infarction in the Hospital Readmission Reduction Program. Abbreviations: CABG= Coronary Artery Bypass Grafting, COPD= Chronic Obstructive Pulmonary Disease, HF= Heart Failure, HRRP= Hospital Readmissions Reduction Program, ICD: International Statistical Classification of Diseases and Related Health Problems, T1MI= Type 1 Myocardial Infarction, T2MI= Type 2 Myocardial Infarction. [Image: see text]

Learning objectives After participating in this activity, learners should be better able to:• Identify the relationships between depression, anxiety, and heart failure (HF).• Assess methods for accurately diagnosing depression and anxiety disorders in patients with HF.• Evaluate current evidence for treatment of anxiety and depression in patients with HF. Background In patients with heart failure (HF), depression and anxiety disorders are common and associated with adverse outcomes such as reduced adherence to treatment, poor function, increased hospitalizations, and elevated mortality. Despite the adverse impact of these disorders, anxiety and depression remain underdiagnosed and undertreated in HF patients. Methods We performed a targeted literature review to (1) identify associations between depression, anxiety, and HF, (2) examine mechanisms mediating relationships between these conditions and medical outcomes, (3) identify methods for accurately diagnosing depression and anxiety disorders in HF, and (4) review current evidence for treatments of these conditions in this population. Results Both depression and anxiety disorders are associated with the development and progression of HF, including increased rates of mortality, likely mediated through both physiologic and behavioral mechanisms. Given the overlap between cardiac and psychiatric symptoms, accurately diagnosing depression or anxiety disorders in HF patients can be challenging. Adherence to formal diagnostic criteria and utilization of a clinical interview are the best courses of action in the evaluation process. There is limited evidence for the efficacy of pharmacologic and psychotherapy in patients with HF. However, cognitive-behavioral therapy has been shown to improve mental health outcomes in patients with HF, and selective serotonin reuptake inhibitors appear safe in this cohort. Conclusions Depression and anxiety disorders in HF patients are common, underrecognized, and linked to adverse outcomes. Further research to improve detection and develop effective treatments for these disorders in HF patients is badly needed.

Globally, death rates from cardiovascular disease are increasing, rising 41% between 1990 and 2013, and are often attributed, at least in part, to poor diet quality. With urbanization, economic development, and mass marketing, global dietary patterns have become more Westernized to include more sugar-sweetened beverages, highly processed foods, animal-based foods, and fewer fruits and vegetables, which has contributed to increasing cardiovascular disease globally. In this paper, we will examine the trends occurring globally in the realm of nutrition and cardiovascular disease prevention and also present new data that international nutrition knowledge amongst cardiovascular disease providers is limited. In turn, this lack of knowledge has resulted in less patient education and counseling, which is having profound effects on cardiovascular disease prevention efforts worldwide.

Background The meaning of high‐sensitivity troponin I (hsTnI) concentrations in patients without acute myocardial infarction ( MI ) requires clarity. We hypothesized that among patients referred for diagnostic coronary angiography without acute MI , hsTnI concentrations would correlate with prevalent coronary artery disease ( CAD ) and predict incident cardiovascular events and mortality. Methods and Results We measured hsTnI using a single‐molecule counting assay (99th percentile, 6 ng/L) in samples from 991 patients obtained at the time of angiography. Concentrations of hsTnI were assessed relative to the severity of CAD and prognosis during mean follow‐up of 3.7 years. Median hsTnI concentration was 4.19 ng/L; 38% of patients had hsTnI concentrations ≥99th percentile. Across increasing hsTnI quartiles, patients had higher prevalence of angiographic CAD ; in multivariate models, hsTnI ≥99th percentile independently predicted obstructive CAD (odds ratio: 2.57; P MI (hazard ratio [ HR ]: 2.68; P HR: 2.29; P =0.001), and all‐cause death ( HR: 1.84; P =0.004). In those with >70% coronary stenosis, hsTnI ≥99th percentile independently predicted incident MI ( HR: 1.87; P =0.01), cardiovascular mortality ( HR: 2.74; P =0.001), and the composite end point of MI and all‐cause death ( HR: 2.06; P MI ( HR : 8.41; P HR : 3.60; P =0.03), and the composite end point of MI and all‐cause death ( HR: 3.62; P Conclusions In a large prospective cohort of patients who were free of prevalent MI and undergoing diagnostic coronary angiography, hsTnI concentrations were associated with higher prevalence of CAD and predicted incident MI , cardiovascular death, and all‐cause death. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 00842868.