Future Heart Failure Research Articles

Incident Heart Failure in Atherosclerotic Renal Artery Stenosis: A Post Hoc Analysis of the CORAL Trial.

Although renal artery stenosis (RAS) and heart failure (HF) have been linked, the incidence and predictors of HF among patients with RAS are not well described. Post hoc analysis of the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) multicenter, open-label, randomized controlled trial (RCT). Patients with atherosclerotic RAS and elevated blood pressure, chronic kidney disease, or both, and without a history of HF at enrollment. Medical therapy alone versus medical therapy plus renal artery stenting. Incident HF events. This analysis included 808 participants enrolled in the CORAL trial without evidence of baseline HF. During a median follow-up of 4.8 years, 54 participants (6.7%) developed incident HF. HF incidence rates did not differ by randomized intervention (HR, 0.84; 95% confidence interval [CI], 0.49-1.43 for stent arm with medical arm as reference). Baseline diabetes (subdistribution hazard ratio (sHR), 2.07; 95% CI, 1.20-3.58), albuminuria (sHR, 1.12 per doubling of urinary albumin-creatinine ratio, 95% CI, 1.02-1.24), lower eGFR (sHR, 0.78 per 10mL/min/1.73m2 estimated glomerular filtration rate calculated with cystatin C and creatinine, 95% CI, 0.69-0.88), and peripheral vascular disease (PVD) (sHR, 2.18, 95% CI, 1.21-3.91) were independent predictors of incident HF. Participants who experienced incident HF had greater kidney function decline before HF events. This is a post hoc analysis of a RCT. The number of HF events is small. In patients with RAS, rates of incident HF did not differ between participants randomized to optimal medical therapy alone versus optimal medical therapy plus renal artery stenting. The presence of diabetes, PVD, and worse kidney health at baseline were associated with future HF events.

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.

Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk. Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization. An AI-ECG model that defines cross-sectional left ventricular systolic dysfunction from 12-lead ECG images was used, and its association with incident HF was evaluated. Discrimination was assessed using Harrell's C-statistic. Pooled cohort equations to prevent HF (PCP-HF) were used as a comparator. Among 231 285 YNHHS patients, 4472 had primary HF hospitalizations over 4.5 years (inter-quartile range 2.5-6.6). In UKB and ELSA-Brasil, among 42 141 and 13 454 people, 46 and 31 developed HF over 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years. A positive AI-ECG screen portended a 4- to 24-fold higher risk of new-onset HF [age-, sex-adjusted hazard ratio: YNHHS, 3.88 (95% confidence interval 3.63-4.14); UKB, 12.85 (6.87-24.02); ELSA-Brasil, 23.50 (11.09-49.81)]. The association was consistent after accounting for comorbidities and the competing risk of death. Higher probabilities were associated with progressively higher HF risk. Model discrimination was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. In YNHHS and ELSA-Brasil, incorporating AI-ECG with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. An AI model applied to a single ECG image defined the risk of future HF, representing a digital biomarker for stratifying HF risk.

Differential determinants and prognostic value of aortic valve sclerosis over carotid atherosclerosis.

Aortic valve sclerosis (AVS) is a progressive atherosclerotic disease associated with future cardiovascular events (CVE). However, whether its development and prognostic value are independent of arterial atherosclerosis has not been thoroughly investigated. We evaluated the determinants and prognostic value of AVS in conjunction with carotid atherosclerosis. 4688 consecutive patients who underwent carotid ultrasonography and echocardiography were followed for an average of 3.6 ± 1.3 years, excluding those with bicuspid aortic valve, rheumatic heart disease, overt aortic stenosis, and prior aortic valve replacement. AVS was defined as any thickened cusps with hyper-echogenicity but a peak pressure gradient <2.5 m/s. The mean age of the patients was 61.1 ± 11.7 years, with 1836 (39 %) being women. Among them, 523 (11 %) had AVS. AVS was independently correlated with age, diabetes, lower high-density lipoprotein levels, and presence of carotid plaques. Moreover, it was significantly related to atherosclerotic CVE (acute coronary syndromes, and all-cause death; log-rank p = 0.011) and future heart failure (HF) admissions (p < 0.001). In multivariate analyses, AVS was significantly associated with atherosclerotic CVE regardless of age and sex but significantly attenuated by the presence of carotid plaques. Meanwhile, future HF admission was attenuated considerably after adjusting for E/e' and left ventricular (LV) ejection fraction. AVS has prognostic value for future CVE and HF. Atherosclerotic CVE was primarily mediated by vascular atherosclerosis; however, future HF admissions were mainly mediated by concomitant LV systolic and diastolic dysfunction, suggesting that AVS serves as a surrogate marker rather than an independent risk factor.

Association of varicose veins with the risk of heart failure: A nationwide cohort study.

Research investigating the association between varicose veins (VV) and heart failure has been limited. Here, we examine this association within a nationwide longitudinal cohort, hypothesizing an increased risk of heart failure associated with the presence of VV. Our study included 390,436 participants based on health screening results conducted from 2005 to 2010 in the South Korean health screening cohort database. Presence of VV was defined as having at least two claims based on International Classification of Diseases, Tenth Revision (ICD-10) codes I830-832, I839, or I868. Propensity score matching (PSM) at a ratio of 1:5 was employed to categorize the participants into two groups based on the presence of VV. The primary outcome, heart failure incidence, was defined as two or more claims with ICD-10 code I50 during follow-up. Among the participants, presence of VV was noted in 5,008 (1.28%) individuals. Over a median follow-up period of 13.33 years (interquartile range 10.4-16.26), 55,023 cases of heart failure (14.0%) occurred. In the multivariable analysis, the group with VV consistently showed an increased incidence risk of heart failure compared to the group without VV, both before (hazard ratios [HR], 1.174; 95% confidence interval [CI], 1.089-1.265) and after PSM (HR, 1.171; 95% CI, 1.070-1.283). Landmark analysis also found a consistent relationship between the presence of VV and the incidence risk of heart failure before (HR, 1.190; 95% CI, 1.103-1.285) and after PSM (HR, 1.144; 95% CI, 1.044-1.254). This study revealed a significant increase in the risk of heart failure among patients with VV in the general population of South Korea. Given that the presence of VV is likely associated with an increased risk of heart failure in the general population, the potential for future heart failure should be taken into consideration when VV are present.

Effectiveness of a nurse practitioner-led collaborative health care model on self-care, functional status, rehospitalization and medical costs in heart failure patients: A randomized controlled trial.

Heart failure is a serious and common condition that has garnered significant attention in the global public health domain. It often results in impaired function and reduced cardiac function status, leading to difficulties in self-care and diminished quality of life. To effectively address these complex challenges, the collaborative health care model has been proposed. This approach has proven effective in reducing rehospitalization and lowering medical costs. To evaluate the effects of a nurse practitioner-led collaborative health care model on the self-care, functional status, rehospitalization and medical costs of patients with heart failure. A randomized controlled trial design. Cardiology department of a regional teaching hospital in Southern Taiwan. 100 patients diagnosed with heart failure. Patients diagnosed with heart failure were recruited through random allocation and. randomly assigned to two groups. The control group included 50 patients who received routine nursing guidance; the experimental group also included 50 patients who participated in a 12-week collaborative health care program. Key outcomes, including self-care, functional status, rehospitalization, and medical costs, which were evaluated at 12, 16, and 20weeks post-discharge. The intervention of the collaborative healthcare program significantly impacted self-care, functional status, rehospitalization, and medical costs. Significant improvements in self-care and functional status were observed at 20weeks (Self-Care: β=31.52, 95% CI: 25.96 to 37.07, p<0.001; Functional Status: χ2=22.42, p<0.001). Regarding rehospitalization, the average rehospitalization duration for the experimental group significantly increased compared to 1.45months for the control group, with the experimental group averaging 3.00months at the 20-week follow-up. Moreover, the experimental group also demonstrated a reduction in rehospitalization medical costs, particularly with significant effects observed in the early stages of intervention (β=-6147.94, 95% CI: -10,763.99 to -1531.88, p=0.009). The use of a nurse practitioner-led collaborative health care model significantly improved self-care, function status and reduced rehospitalization while effectively lowering medical costs for patients with heart failure. Through professional team communication and collaboration, this approach provides more effective and comprehensive care, enhances patient self-management capabilities, and improves overall treatment outcomes. These results hold significant implications for clinical practice and provide empirical support for future heart failure care programs, warranting their widespread implementation in clinical settings. This study was registered on ClinicalTrials.gov under the identifier NCT04860596 on April 22, 2021, and participant recruitment was initiated in April 2023. Effectiveness of a Nurse Practitioner-Led Collaborative Care Model: Reduces rehospitalization and medical costs, while improving self-care and functional status in heart failure patients. A Randomized Controlled Trial. #HeartFailure #HealthCare #SelfCare.

Investigation of left ventricular ejection fraction in a Swiss heart failure population: Insights into mortality and sex differences.

Understanding heart failure (HF) characteristics is essential to improve patient outcomes. Categorizing HF beyond left ventricular ejection fraction (LVEF) is challenging due to heterogeneous clinical presentation and aetiologies. Despite global studies on HF, the role of LVEF on mortality remains controversial. We explored the association of LVEF with mortality, considering sex differences and comorbidities in a cohort from the largest tertiary cardiovascular centre in Switzerland. HF patients admitted to the University Hospital of Bern from January 2015 to December 2019 were evaluated. LVEF was used to classify patients into HF with preserved ejection fraction (HFpEF), HF with mid-range ejection fraction (HFmrEF) and HF with reduced preserved ejection fraction (HFrEF) categories. Cox proportional hazard models and time-stratified analyses adjusted for potential confounders were employed. A total of 5824 HF patients were included, and 2912 died over a median follow-up time of 3.39years. Mortality rates across LVEF categories showed no significant differences, while overall, women showed significantly higher mortality; 30day mortality was lower in the HFpEF category [hazard ratio (HR) 0.67, 95% confidence interval (CI): 0.52-0.88, P=0.003], with persistent effects upon stratification in males (HR 0.59, 95% CI: 0.42-0.81, P<0.001) and non-diabetics (HR 0.62, 95% CI: 0.44-0.87, P=0.005). An isolated reduction in HFpEF mortality was observed in females after 1year (HR 0.72, 95% CI: 0.53-0.98, P=0.035). The prognostic role of LVEF on all-cause mortality remains unclear, while differences in mortality rate distribution between women and men mirror established HF pathophysiological sex differences. Future HF studies should focus on HF aetiology and include measures beyond LVEF for comprehensive characterization.

New and future heart failure drugs.

In the past decade, our understanding of heart failure pathophysiology has advanced significantly, resulting in the development of new medications such as angiotensin-neprilysin inhibitors, sodium-glucose cotransporter-2 inhibitors and oral soluble guanylate cyclase stimulators. Backed by positive findings from large randomized controlled trials, recommendations for their use were recently included in the 2022 AHA/ACC/HFSA guidelines and 2023 ESC guidelines for management of heart failure. Promising drugs for future heart failure treatment include agents that modulate the neurohormonal system, vasodilators, anti-inflammatory drugs, mitotropes, which improve deranged energy metabolism of the failing heart, and myotropes, which increase cardiac contractility by affecting cardiac sarcomere function. Here, we discuss these new and future heart failure drugs. We explain their mechanisms of action, critically evaluate their performance in clinical trials and summarize the clinical scenarios in which the latest guidelines recommend their use. This Review aims to offer clinicians and researchers a comprehensive overview of novel therapeutic classes in heart failure treatment.

Cardiac structural, functional and energetic assessments during and after pregnancy in women with gestational diabetes mellitus, preeclampsia and healthy pregnancy

GDM and preeclampsia are common complications of pregnancy, for which overweight/obesity is a common risk factor. Both conditions are associated with a two-to-four-fold increase in future incident heart failure, which may be linked to early maladaptive myocardial changes. To determine maternal myocardial structural, functional, and energetic responses to pregnancies complicated by gestational diabetes mellitus (GDM) or preeclampsia compared to healthy pregnancies (HP) at third-trimester of pregnancy and twelve-months postpartum. Thirty-eight women with HP, 30 GDM, 20 preeclampsia, 10 non-pregnant controls with overweight (Overweight-NC) and 10 with normal-weight were recruited. Cardiovascular magnetic resonance spectroscopy and imaging were used to define myocardial energetics (phosphocreatine: ATP ratio [PCr/ATP]), left ventricular (LV) volumes, mass, and ejection fraction and global longitudinal shortening (GLS). Pregnancy groups underwent repeat scans twelve-months postpartum, nulliparous-controls were assessed once. During third-trimester, compared to HP, women with either GDM or preeclampsia displayed higher BMI, higher LV-mass (HP:90[85,94]g, GDM:103[96,112], Preeclampsia:118[111,125]g; P=0.001), and lower PCr/ATP (HP:2.2[2.1,2.4], GDM:1.9[1.7,2], Preeclampsia:1.9[1.8,2.1];P=0.0004) and GLS (HP:20[18,21]%, GDM:18[17,19]%, Preeclampsia:16[14,17]%;P=0.01). Post-pregnancy, no group saw significant changes in LV-mass, PCr/ATP or GLS. There were no significant differences in LV-mass, PCr/ATP or GLS between the GDM and preeclampsia groups during or post-pregnancy. Moreover, the Overweight-NC showed no significant differences in LV-mass ( 53[43,63])g, PCr/ATP (2.0[1.8,2.2]) or GLS (-19[17,21]%) compared to GDM or preeclampsia groups during or post-pregnancy. Women with GDM or preeclampsia exhibit similar myocardial phenotypes during pregnancy with persistent subclinical alterations in LV mass, energetics and GLS twelve-months postpartum. These myocardial alterations are similar to those detected in Overweight-NC, potentially suggesting the myocardial changes may predominantly be driven by overweight/obesity.

Abstract 4114417: Polygenic Risk in Heart Failure – Evaluating the Ability of a Polygenic Risk Score to Identify Risk of Future Heart Failure Events in 60,000 Patients from 6 TIMI Randomized Trials

Background: The ability of polygenic risk scores (PRS) to predict heart failure (HF) beyond classic risk factors is unclear. Aims: To evaluate the risk of a HF PRS with incident hospitalization for HF (HHF) in patients with established cardiovascular disease. Methods: We analyzed a 59 SNP HF PRS (HERMES collaboration, Henry et al) in genotyped patients from six multinational TIMI randomized controlled trials (DECLARE, ENGAGE AF, FOURIER, PEGASUS, SAVOR, SOLID). Patients were stratified to low (quintile, Q1), intermediate (Q2-Q4), or high (Q5) genetic risk. We investigated the association of the HF PRS with incident HHF (median of 2.5 years) i) stratified by prior HF, and ii) across NTproBNP concentrations at baseline, using Cox-proportional hazard models. Results are reported as HR [95% CI], per 1-SD or with Q1 as a reference, and adjusted for ancestry (principal components 1-5), age, sex, trial, BMI, smoking, systolic BP, prior CAD, DM, AF, and eGFR. Results: In 59,906 pts (median age 66 years; 71% male; 26% prior HF), the HF PRS was associated with incident HHF (HR adj per 1-SD 1.08 [1.03, 1.13], p&lt;0.001). Compared to low genetic risk pts, the HR adj for HHF was 1.12 [1.00, 1.26] in intermediate and 1.17 [1.02, 1.35] in high-risk pts. There were significant interactions between the HF PRS and prior HF (P int 0.001), and NTproBNP (P int 0.038), respectively. In pts without prior HF (Fig A), compared to low-risk pts, the risk was increased in high- (HR adj 1.56 [1.25, 1.94], p&lt;0.001) and intermediate-risk pts (HR adj 1.27 [1.05, 1.54], p=0.013). In contrast, there was no significant association in pts with known HF at baseline ( Fig B ). Similarly, the HF PRS was significantly associated with HHF in pts with low but not elevated NTproBNP at baseline ( Fig C ). Conclusion: Our study confirms the role of polygenic risk for HF and demonstrates that a 59 SNP PRS can identify pts at increased risk of future HF events beyond traditional clinical factors, specifically in patients without established HF or elevated NTproBNP. These findings suggest a potential application of a HF PRS for screening and early identification of pts at increased risk for HF.

Prediction of mortality, future arrhythmia and cardiovascular disease: an artificial intelligence-enhanced electrocardiography platform

Abstract Background Artificial intelligence-enhanced electrocardiography (AI-ECG) can be used to identify existing disease, but could additionally be used to predict occurrence of future disease and death. Purpose We developed an AI-ECG platform that predicts mortality and a wide spectrum of future arrhythmia and cardiovascular disease. Methods The AI-ECG risk estimation platform (AIRE) was developed in a dataset of 1,163,401 ECGs from 189,539 patients from a secondary care setting in the USA. Uniquely, AIRE uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves (Fig 1A). AIRE was fine-tuned to additionally predict the endpoints described below. Results AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776) and can differentiate prognosis in high- and low-risk subjects, even when only cardiologist-defined normal ECGs are considered (Fig 2B). Additionally, in Cox regression analyses, AIRE was superior to conventional risk factors (Fig 1B). AIRE accurately predicted future ventricular arrhythmia (c-index 0.760 (0.756-0.763)), future complete heart block (CHB) (0.809 (0.805-0.814), future atrial fibrillation (0.753 (0.751-0.756), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.789)) . AIRE was superior to left ventricular ejection fraction (LVEF) in predicting risk of future ventricular arrhythmias in subjects with LVEF &amp;lt;50% (C-index 0.649 (0.638 – 0.660) vs 0.615 (0.603 – 0.626) p &amp;lt; 0.0001 ). For CHB prediction, AIRE was able to further risk stratify subjects with bifascicular block and syncope into low (1.9% 5 year CHB), intermediate (8.8%), and high risk (20.7%) groups, and could guide treatment decisions. We externally validated our findings in four diverse, transnational cohorts from Brazil and the UK, including volunteers, primary care subjects and patients with Chagas disease, and found AIRE accurately predicted all-cause mortality in all four external cohorts (Fig 2). Non-mortality endpoints were successful externally validated in the UKB. In explainability analyses, we found features of QRS morphology, low QRS voltage, poor precordial R wave progression, inverted and biphasic T waves and ST segment changes were the most significant morphological features associated with high predicted mortality (Fig 1D). Through phenome- and genome-wide association studies (GWAS), we identified candidate biological pathways including changes in cardiac structure (LV mass, LV trabeculation and left atrial size). GWAS identified variants adjacent to VGLL2, KCNQ1, CCDC91 and TBX3 which have been described in association with QT interval, biological aging and metabolic syndrome (Fig 1E). Conclusion AIRE could be used worldwide across a range of clinical contexts for risk estimation of not only mortality but a wide spectrum of future arrhythmia and cardiovascular diseases.Figure 1Figure 2

Sex related cardiovascular risk is non-dichotomous: artificial intelligence enhanced electrocardiography reveals continuum of risk in females

Abstract Background Females are typically underserved in cardiovascular medicine and often considered lower risk of cardiovascular disease. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. Purpose We aimed to develop an artificial intelligence-enhanced ECG (AI-ECG)-derived continuous sex score that can capture the continuum of risk and sex phenotypes within each sex, with the particular goal of addressing the female disadvantage. Methods We trained a convolution neural network to identify sex using the 12-lead ECG. The derivation cohort was 1,163,401 ECGs from 189,538 subjects from a USA secondary care hospital cohort and validated inthe UK Biobank (UKB). The model outputs a continuous value from 0 to 1 for sex prediction. The AI-ECG sex discordance score is the difference between predicted sex and biological sex. Results AI-ECG accurately identified sex in both the USA cohort (AUROC 0.943 (0.942-0.943)) and UKB datasets (n = 42,386, AUROC 0.971 (0.969-0.972)). In explainability analyses we found QRS duration, T wave morphology, QT interval, heart rate and QRS voltage as the most important factors in contributing to AI-ECG sex identification. In females only but not in males, sex discordance score was associated with a covariate-adjusted increased risk of cardiovascular death in outpatients with normal ECGs from the USA cohort, (Females hazard ratio (HR) 1.64 (1.18-2.29) p = 0.003, Males HR 1.04 (0.66-1.63), p = 0.86). This pattern persisted in the UKB: Females HR 1.39 (1.08-1.78) p = 0.01, Males HR 0.97 (0.77-1.22), p = 0.80, Fig 1). In phenome-wide association studies, we found females with a higher sex score discordance were more likely to have heart failure or myocardial infarction. Covariate-adjusted cox models confirmed the association of sex score discordance with increased future heart failure (HR 1.21 (1.07-1.37) p = 0.002 and future myocardial infarction (HR 1.32 (1.11-1.56) p = 0.001). In age- and body size-adjusted analyses, females with higher sex discordance score had increased left ventricular mass, and chamber volumes as well as increased muscle mass, reduced body fat percentage (Fig 2). In a genome-wide association study of sex discordance score we identified variants adjacent to IGF1R and NDRG4 in females, which have been previously associated with LV mass and sex hormone levels (Fig 2). Conclusion We describe sex discordance score as a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. Sex discordance score may help uncover potential underlying mechanisms of cardiovascular disease among females, and future clinical studies are needed to determine its potential utility in identification of females at higher risk of cardiovascular events. Rather than exacerbating biases, AI-ECG has the potential to reduce female cardiovascular health inequalities.

Prevalence and implications of cardiomyopathy-associated genetic variants in early-onset atrial fibrillation

Abstract Background Atrial fibrillation (AF) is a common and morbid arrhythmia. Despite a substantial heritable component, genetics does not currently play a role in personalized medicine for AF. Rare genetic variants in genes associated with inherited cardiomyopathies (CMP) are enriched in AF patients. However, prior studies on rare variants in AF mainly relied on hospital-based populations, and knowledge on clinical outcomes remains limited. Purpose We aimed to evaluate the potential diagnostic yield and prognostic implication of pathogenic or likely pathogenic (PLP) genetic variants for CMP in persons with AF, using large population-based cohorts. Methods This study utilized sequencing data and electronic health records (EHR) from the All of Us Researh Program (AllofUs) and UK Biobank (UKB). Using ClinVar, we curated a set of PLP variants in 36 CMP-associated genes and assessed the carrier prevalence overall, in participants with AF, and in participants with early-onset AF (below 65, 55 and 45 years). Next, we used Cox proportional hazards models to evaluate the association between variant carrier status and incident risk of heart failure or CMP, after AF diagnosis. In all time-to-event analyses, individuals with prevalent heart failure or CMP at AF diagnosis were excluded and models were adjusted for age, sex, and principal components of ancestry. Sensitivity analyses were performed requiring a minimum period from AF to HF or CMP, of 1, 3 and 6 months. Results After excluding related individuals, we identified 32,281 (8.19%) and 9,666 (6.06%) AF cases in UKB and AllofUs, respectively. Compared to the overall populations (UKB: 1.15%; AllofUs: 1.36%), PLP variants for CMP were about ~5 times more prevalent in AF with onset before 45 (UKB: 4.98%; AllofUs: 5.26%), ~3 times more prevalent in AF with onset before 55 (UKB: 3.41%; AllofUs: 4.34%) and ~2 times more prevalent in all AF patients (UKB: 2.03%; AllofUs: 2.67%). In turn, AF patients carrying PLP variants had increased risk of developing heart failure or CMP, after AF diagnosis (AllofUs HR 1.81, 1.44-2.29, p&amp;lt;1e10-6, UKB:HR=1.39 1.08-1.78, p&amp;lt;0.01), which remained present when requiring a minimum of 6 months after AF diagnosis (AllofUs:HR 1.70, 1.29-2.25, p&amp;lt;0.001; UKB:HR=1.59, 1.21-2.07, p&amp;lt;0.001). Effect sizes were greater when limiting analysis to AF patients with onset before 65 (cases=1021, HR 1.91, 1.35-2.68, p&amp;lt;0.0001) and before 55 years (cases=384, HR 2.53, 1.55-4.1, p &amp;lt;0.001) in AllofUs. UK Biobank did not reflect this increase (cases&amp;lt;65=513, HR&amp;lt;65=1.27, 0.63-2.39, p&amp;lt;65&amp;gt;0.05). Considering only HF events in AllofUs, the effect remained present (HR 1.70, 1.32-2.19, p&amp;lt;0.0001). Conclusion The yield of CMP-associated PLP variants is substantial among persons with early-onset AF, even when considering a 55 year cutoff. AF patients with PLP variants have an increased risk of future heart failure or CMP. Our results further support the clinical relevance of genetic testing in early-onset AF.Prevalence of CMP variants in AF by age

Detection and risk stratification of cardiac amyloidosis patients by integration of imaging and non-imaging data using a machine learning approach

Abstract Background With the advent of amyloid-targeting therapies, early and reliable diagnosis as well as precise risk estimation of cardiac amyloidosis (CA) have become of substantial importance. While the current diagnostic approach relies on difficult-to-standardize, visual interpretation of 99mTc-scintigraphy, risk assessment is largely based on a combination of blood and imaging parameters. Purpose Aim of this study is to assess the possibility of machine learning-based integration of scintigraphy, echocardiography, and non-imaging parameters such as blood parameters and comorbidities for the detection and risk stratification of patients with CA. Methods This study included all patients who underwent 99mTc-scintigraphy at a university affiliated tertiary care centre between 2010 and 2023. Two machine learning models were developed, and the relative predictive importance of their parameters were assessed (Figure 1): First, a model to detect patients with CA-suggestive uptake (Perugini grade 2/3) on 99mTc-scintigraphy scans; Second, a model to assess the risk of patients with CA-suggestive uptake for future heart failure hospitalization (HFH). A total of 58 features were extracted from electronic health records including blood, echocardiographic, demographic parameters and comorbidities. Scintigraphy imaging features were extracted from the raw imaging data using a deep learning approach. For the time to HFH prediction, a random survival forest machine learning model was employed. Results Overall, 12 380 consecutively enrolled patients were included, 279 (2.3%) of which were affected by CA-suggestive uptake. The machine learning model showed higher accuracy for the detection of CA (AUC 0.96 [95% CI 0.95-0.97], sensitivity 0.78 [95% CI 0.77-0.80] and specificity 0.99 [95% CI 0.99-0.99]. In the outcome analysis, the machine model showed good accuracy in predicting future HFH (C-index 0.71 [95% CI 0.68-0.75]). Parameter importance for the time to event analysis revealed right ventricular diameter, previous diagnosis of chronic heart failure and creatine kinase as the three most important predictors (Figure 2). Conclusions Detection of patients with cardiac amyloidosis and their risk estimation for heart failure can be aided using machine learning-based integration of imaging and non-imaging data. Our findings may guide novel approaches for assessing disease progression and treatment response.

Basal Microvascular Resistance ― Another Invasively Measured Physiological Index for Predicting Future Heart Failure Events ―

Basal Microvascular Resistance ― Another Invasively Measured Physiological Index for Predicting Future Heart Failure Events ―

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients. AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome. AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation. British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

Representation of women and racial minorities in SGLT2 inhibitors and heart failure clinical trials

BackgroundInadequate representation of women and racial minorities in heart failure (HF) clinical trials continues to limit the generalizability of the results. This could create a disparity in treatment for future heart failure therapies and devices. The study aims to assess the representation of women and racial minorities in recent heart failure studies involving sodium-glucose cotransporter-2 (SGLT-2) inhibitors. MethodsPubMed was used to search randomized controlled trials (RCTs) looking at SGLT-2 inhibitors and heart failure, which were published from inception to August 2024. ResultsA total of 43 RCTs with 27,703 participants were identified. The studies were published between 2018 and 2024. Seven studies (41 %) were multi-country, with 45 countries represented. The overall proportion of women enrolled in the studies was 35.6 %. The proportion of women was 24.06 % in studies that recruited only patients with HFrEF, 44.33 % in those that recruited only patients with HFpEF, and 41.4 % in those that recruited both HFrEF and HFpEF. Data on race was partially reported in 25 studies (58 %). 76 % of the pharmaceutical industry-funded studies reported race data. However, only 33.3 % of the unfunded or non-industry-funded studies reported race data. In the studies that reported race data, 72.91 % were Caucasians, 15.48 % were Asians, 5.62 % were African-American and 4.1 % were mixed race or others.In the bivariate analysis, race was more likely to be reported in studies done in the US (p < 0.001), multi-country studies (p = 0.013), and studies sponsored by pharmaceutical companies. More than a third of the study participants were more likely to be women in more recently published studies than older studies (p < 0.001). Additionally, more than a third of the study participants were more likely to be women in studies done in the US (p = 0.055). The multivariate analysis showed an increased odds of having more than a third of the study participants being women in more recently published studies (OR 1.83, 95 % CI 1.06–3.17, p = 0.031) and in studies done in the US (OR 7.69, 95 % CI 1.53–38.59, p = 0.013). ConclusionOur study found that women and racial minority individuals have remained underrepresented in recent heart failure studies. Although some progress has been made over the years, more work is needed to improve data reporting and address barriers to enrollment for women and racial minority individuals in clinical trials.

Screening for Heart Failure: Biomarkers to Detect Heightened Risk in the General Population.

Heart failure (HF) represents a growing global burden of morbidity and mortality. Identifying individuals at risk for HF development is increasingly important, particularly given the advent of disease-modifying therapies for HF as well as its major risk factors such as obesity actalnd diabetes. We aim to review the key circulating biomarkers associated with future HF which may contribute to HF risk prediction. While current guidelines recommend the use of natriuretic peptides and cardiac troponins in HF risk stratification, there are a diverse array of other emerging protein, metabolic, transcriptomic, and genomic biomarkers of future HF development. These biomarkers not only lend insight into the underlying pathophysiology of HF, which spans inflammation to cardiac fibrosis, but also offer an opportunity to further refine HF risk in addition to established biomarkers. As evolving techniques in molecular biology enable an increased understanding of the complex biologic contributions to HF pathophysiology, there is an important opportunity to construct integrated clinical and multi-omic models to best capture HF risk. Moving forward, future studies should seek to understand the contributions of sex differences, underlying comorbidity burden, and HF subtypes to an individual's HF risk. Further studies are necessary to fully define the clinical utility of biomarker screening approaches to refine HF risk assessment, as well as to link risk assessment directly to preventive strategies for HF.

Metabolomic profiling identifies novel metabolites associated with cardiac dysfunction

Metabolic comorbidities, such as obesity and diabetes, are associated with subclinical alterations in both cardiac structure/function and natriuretic peptides prior to the onset of heart failure (HF). Despite this, the exact metabolic pathways of cardiac dysfunction which precede HF are not well-defined. Among older individuals without HF in the Multi-Ethnic Study of Atherosclerosis (MESA), we evaluated the associations of 47 circulating metabolites measured by 1H-NMR with echocardiographic measures of cardiac structure and function. We then evaluated associations of significant metabolites with circulating N-terminal pro-B-type natriuretic peptide (NT-proBNP). In a separate cohort, we evaluated differences between top metabolites in patients with HF with preserved ejection fraction (HFpEF) and comorbidity-matched controls. Genetic variants associated with top metabolites (mQTLs) were then related to echocardiographic measures and NT-proBNP. Among 3440 individuals with metabolic and echocardiographic data in MESA (62 ± 10 years, 52% female, 38% White), 10 metabolites broadly reflective of glucose and amino acid metabolism were associated with at least 1 measure of cardiac structure or function. Of these 10 metabolites, 4 (myo-inositol, glucose, dimethylsulfone, carnitine) were associated with higher NT-proBNP and 2 (d-mannose, acetone) were associated with lower NT-proBNP. In a separate cohort, patients with HFpEF had higher circulating myo-inositol levels compared with comorbidity-matched controls. Genetic analyses revealed that 1 of 6 known myo-inositol mQTLs conferred risk of higher NT-proBNP. In conclusion, metabolomic profiling identifies several novel metabolites associated with cardiac dysfunction in a cohort at high risk for HF, revealing pathways potentially relevant to future HF risk.

Accelerometer-measured physical activity in patients with heart failure and reduced ejection fraction: Determinants and relationship with patient-reported health status

Background: Accelerometer-measured physical activity is an increasingly used endpoint in heart failure (HF) trials. We investigated the determinants of accelerometer-measured physical activity and the relationship with patient-reported health status. Methods: Post-hoc analysis of the Empire HF trial, including outpatients with HF with reduced ejection fraction (HFrEF). Physical activity was quantified as average accelerometer counts per minute (CPM) with higher values representing higher activity. We investigated associations between activity level and clinical variables, including age, sex, and body mass index, as well as patient-reported health status assessed by Kansas City Cardiomyopathy Questionnaire (KCCQ). Results: Complete data were available in 180 (95%) patients (86% male, mean age 65 year). Baseline median physical activity level was 1318 CPM (Q1-Q3 1111–1585). Age and anemia were independently associated with activity level (β-coefficients: -10 CPM per year age increase [95% CI -16 to -5.1], p=0.00015, and -126 CPM for anemia [95% CI -9.1 to -244], p=0.035). Significant independent associations were observed between activity level and all KCCQ summary scores (β-coefficient point estimates of 3.7, 4.6, and 4.9 CPM, all p<0.02). For 12-week changes in KCCQ-summary scores, only the KCCQ-CSS was associated with activity level; mean increase of 17.5 CPM [95% CI 1.5 to 34.0], p=0.032, per 5-point increase in KCCQ-CSS. Associations were not modified by treatment allocation (interaction p-values>0.05). Conclusions: In patients with HFrEF, older age and anemia were independently associated with lower activity. Moreover, physical activity only weakly increased with better health status, suggesting that changes in physical activity reflect improvements in patients’ health status to a limited degree. This highlights the need to better understand the endpoint with regards to all other health parameters to ease interpretation in future HF trials.

Association of baseline and longitudinal changes in insulin-like growth factor-binding protein-7 with the risk of incident heart failure: Data from the PREVEND study.

Senescence is a major risk factor for heart failure (HF), and insulin-like growth factor-binding protein-7 (IGFBP7) has been identified as an important senescence-inducing factor. The aim of this study was to examine the value of baseline and repeat IGFBP7 measurements in predicting future HF among community-dwelling Dutch adults from the Prevention of Renal and Vascular End-stage Disease (PREVEND) study. Individuals without prevalent HF who attended PREVEND visits 2 and 4 median of 5.1 years apart (25th-75th percentile, 4.9-5.2) with measurements of IGFBP7 were included. We used Cox proportional hazards models to investigate the association between IGFBP7 and HF incidence. A total of 6125 participants attending visit 2 (mean ± standard deviation [SD] age 53.1 ± 12.2 years; 3151 [51.4%] men) were followed for a median of 8.4 (7.8-8.9) years, and 194 participants (3.2%) developed incident HF. Median baseline IGFBP7 concentration was 87.0 (75.1-97.3) ng/ml, and baseline IGFBP7 levels were significantly associated with risk for incident HF (HF risk factors adjusted hazard ratio [HR] per 1 SD change in log-transformed IGFBP7: 1.22, 95% confidence interval [CI] 1.03-1.46). Baseline IGFBP7 was also significantly associated with incident HF in individuals with N-terminal pro-B-type natriuretic peptide <125 ng/L. Among 3879 participants attending both visits 2 and 4 (mean ± SD age 57.5 ± 11.3 years; 1952 [50.3%] men), 93 individuals developed HF (after visit 4) during a median follow-up of 3.2 (2.8-3.9) years. Median increase in IGFBP7 concentration between visits was 0.68 (-7.09 to 8.36) ng/ml, and changes in IGFBP7 levels were significantly associated with risk for incident HF (HF risk factors adjusted HR per 1 SD change in log-transformed IGFBP7: 1.68, 95% CI 1.19-2.36). Both baseline as well as repeat IGFBP7 measurements provide information about the risk of developing HF.