Citations

BACKGROUND: This study aimed to evaluate the prevalence of pathogenic/likely pathogenic cardiomyopathy variant carriers in a multiancestry US population and examine the risk of adverse clinical outcomes. METHODS: This retrospective cohort study included multiancestry US adults aged ≥18 years with sequencing data from the All of Us Research Program. Pathogenic/likely pathogenic variants in cardiomyopathy genes were identified using the ClinVar database. The primary outcome was heart failure. Secondary outcomes included cardiomyopathy and arrhythmia. Outcomes were identified from electronic health records. Interval-censored Cox models, taking age on the timescale, were used to assess the risk of outcomes in pathogenic/likely pathogenic variant carriers with noncarriers as the reference group. RESULTS: Among 167 435 individuals (median age, 55.2 [39.5–66.3] years; 61.7% female; 40.7% non-European ancestry) included, the prevalence of pathogenic/likely pathogenic cardiomyopathy variant carriers was 0.7% in the overall population and 0.8%, 0.8%, 0.5%, and 1.2% among European, African, East Asian, and South Asian ancestry individuals, respectively. Over their lifetime, there were 12 867 heart failure events (205 in carriers and 12 662 in noncarriers), with an incidence rate of 3.05 (95% CI, 2.66–3.49) per 1000 person-years in carriers and 1.37 (95% CI, 1.35–1.40) in noncarriers (adjusted hazard ratio, 2.30 [95% CI, 2.04–2.60]). Cardiomyopathy occurred in 5164 (161 in carriers and 5003 in noncarriers), with an incidence rate of 2.38 (95% CI, 2.04–2.78) per 1000 person-years among carriers and 0.54 (95% CI, 0.53–0.56) in noncarriers (adjusted hazard ratio, 4.31 [95% CI, 3.73–4.97]). There were 19 405 arrhythmia events (263 in carriers and 19 142 in noncarriers), with an incidence rate of 3.93 (95% CI, 3.48–4.44) per 1000 person-years among carriers and 2.09 (95% CI, 2.06–2.12) in noncarriers (adjusted hazard ratio, 2.12 [95% CI, 1.78–2.53]). CONCLUSIONS: Pathogenic/likely pathogenic cardiomyopathy variant carriers have an increased risk of heart failure, cardiomyopathy, and arrhythmias. Despite the modest overall prevalence, the associated risks suggest potential benefits of targeted genetic screening for early detection and management.

Objective: Four hypertension-mediated left ventricular hypertrophy (LVH) phenotypes have been reported using cardiac magnetic resonance (CMR): normal LV, LV remodelling, eccentric and concentric LVH, with varying prognostic implications. The electrocardiogram (ECG) is routinely used to detect LVH; however, its capacity to differentiate between LVH phenotypes is unknown. This study aimed to classify hypertension-mediated LVH from the ECG using machine learning and test for associations of ECG-predicted phenotypes with incident cardiovascular outcomes. Methods: ECG biomarkers were extracted from the 12-lead ECG of 20 439 hypertensive patients in UK Biobank (UKB). Classification models integrating ECG and clinical variables were built using logistic regression, support vector machine (SVM), and random forest. The models were trained in 80% of the participants, and the remaining 20% formed the test set. External validation was sought in 877 hypertensive participants from the Study of Health in Pomerania (SHIP). In the UKB test set, we tested for associations between ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure. Results: Among UKB participants 19 408 had normal LV, 758 LV remodelling, 181 eccentric and 92 concentric LVH. Classification performance of the three models was comparable in UKB. SVM (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) was taken forward for external validation with similar results in SHIP. There was superior prediction of eccentric LVH in both cohorts. In the UKB test set, ECG-predicted eccentric LVH was associated with heart failure (hazard ratio 3.42, 95% CI 1.06–9.86). Conclusion: ECG-based ML classifiers represent a potentially accessible screening strategy for the early detection of hypertension-mediated LVH phenotypes.

Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems’ requirements that could enhance and accelerate the integration of these platforms.

Abstract Dilated cardiomyopathy (DCM) is a clinical diagnosis characterized by the presence of left ventricular dilatation and systolic dysfunction unexplained by abnormal loading conditions or coronary artery disease. However, a broad range of phenotypic manifestations, encompassing isolated scar, DCM with preserved ejection fraction, and overt DCM, should be regarded as a diagnostic classification representing a broad spectrum of underlying aetiologies, including both inherited and acquired heart muscle disorders. A multimodal non-invasive imaging approach is essential for accurate morpho-functional assessment of cardiac chambers and is key to establish the cardiac phenotype and to rule out an underlying ischaemic aetiology. Furthermore, advanced imaging techniques enable deep cardiovascular phenotyping and non-invasive tissue characterization. The aim of this review is to propose a systematic approach to the diagnosis of DCM, emphasizing the importance of genetics and clinical findings for a precise and practical clinical approach. Also, we strive to qualify the role of cardiac imaging in the diagnosis of DCM, particularly on the relevance of novel techniques and clinical utility of actionable parameters to improve current diagnostic schemes and risk stratification algorithms. We further elaborate on the role of cardiac imaging to deliver optimal guidance to aetiology-based therapeutic approaches, verification of treatment response and disease progression monitoring.

Abstract Aims In light of recent advances in imaging techniques, molecular understanding and therapeutic options in hypertrophic cardiomyopathy (HCM), we performed a systematic review of current guidelines for the diagnosis and management of HCM in order to identify consensus and discrepant areas in the clinical practice guidelines. Methods and results We systematically reviewed the English language guidelines and recommendations for the management of HCM in adults. MEDLINE and EMBASE databases were searched for guidelines published in the last 10 years. Following a systematic search, three guidelines on the diagnosis and management of HCM were identified, all of which were robustly developed (AGREE rigour of development score ≥50%). These guidelines were authored by the major European (European Society of Cardiology; 2023), American (American Heart Association /American College of Cardiology/American Medical Society for Sports Medicine /Heart Rhythm Society/Pediatric and Congenital Electrophysiology Society/Society for Cardiovascular Magnetic Resonance; 2024), and Japanese [Japanese Circulation Society (JCS)/Japanese Heart Failure Society (JHFS); 2018] cardiovascular societies. There was broad consensus on echocardiographic recommendations, the medical and invasive management of HCM, the application of genetic testing and family screening, and exercise and reproductive recommendations in HCM. There were areas of variability in the definition and diagnostic criteria for HCM, cardiovascular magnetic resonance imaging recommendations, and assessment of sudden cardiac death (SCD) risk and prevention strategies. Due to the JCS/JHFS guidelines being older, there are no recommendations on the use of cardiac myosin ATPase inhibitors. Conclusion Contemporary guidelines for HCM achieve consensus across a broad range of criteria and recommendations concerning diagnosis and management. However, variations in the approach towards risk assessment for SCD exist between the guidelines. There are also more subtle differences concerning diagnostic criteria and the utility of late gadolinium enhancement for risk stratification, which will likely evolve as the evidence-base broadens.

Abstract Dilated cardiomyopathy (DCM) is extensively discussed in numerous expert consensus documents and international guidelines, with differing recommendations. To support clinicians in daily practice and decision-making, we conducted a systematic review of key guidelines and recommendations concerning the diagnosis and clinical management of DCM. Our research encompassed MEDLINE and EMBASE databases for relevant articles published, as well as the websites of relevant scientific societies. We identified two guidelines and one scientific statement that met stringent criteria, thereby qualifying them for detailed systematic analysis. Our review revealed consensus on several key aspects: the definition of DCM, the use of B-type natriuretic peptides and high-sensitivity troponin in laboratory testing, the essential role of multimodality cardiovascular imaging for initial diagnosis, genetic counselling, and the management of advanced disease. Nonetheless, notable areas of variation included the formation of multidisciplinary management teams, the role of cascade genetic testing, pathways for arrhythmic risk stratification, and the criteria for prophylactic defibrillator implantation. Significant evidence gaps persist, particularly regarding the clinical trajectory of genetic, non-genetic and gene-elusive forms of DCM, the use of cardiovascular magnetic resonance in phenotype-negative family members with genotype-positive probands, and the development of potential aetiology-oriented therapies. Addressing these gaps could enhance clinical outcomes and inform future research directions and guideline development.

Background Hyperlipidemia is a major cardiovascular disease (CVD) risk factor, but there are limited data on its mortality trends in CVD over time. We assessed annual hyperlipidemia‐related CVD mortality trends in the United States, including the COVID‐19 pandemic's impact. Methods and Results Mortality data were obtained from the Centers for Disease Control and Prevention repository between 1999 and 2020 among patients ≥15 years old, using International Classification of Diseases, Tenth Revision ( ICD‐10 ) codes for hyperlipidemia (E78.0–E78.5) and CVD (I00–I99). Age‐adjusted mortality rates (AAMRs) per 1 000 000 population were standardized to the 2000 US population. Log‐linear regression models were used to evaluate mortality shifts. Average annual percentage change from 1999 to 2019 was used to project 2020 AAMRs, estimating pandemic‐attributed excess deaths. From 1999 to 2020, 483 155 hyperlipidemia‐related CVD deaths occurred. Despite a general CVD mortality decline, hyperlipidemia‐related CVD AAMRs rose from 36.33 in 1999 to 99.77 in 2019. Ischemic heart diseases (AAMR 49.39) were the leading cause, whereas hypertension had the highest mortality increase (average annual percentage change +10.23%). Mortality rates were higher in men (AAMR 104.87) and non‐Hispanic (AAMR 82.49), and rural populations (AAMR 89.98). Highest mortality was observed in Black populations (AAMR 84.35), those ≥75 years old (AAMR 646.45), and Western US regions (AAMR 96.88). During the first pandemic year, deaths exceeded projections by 10.55%, with notable increases among ages 35 to 75 (14.23%), Hispanic (17.96%), Black (14.82%), and urban (11.68%) groups. Conclusions Hyperlipidemia‐related CVD mortality has risen over the past 2 decades, further heightened by the COVID‐19 pandemic, with higher impact on men, Black Americans, the older population, and rural residents. Further study is needed to understand contributing factors and mitigate disparities.

Objective Drug-induced long QT syndrome (diLQTS) is an adverse reaction from over 150 FDA-approved medications, posing the risk of triggering torsades de pointes and sudden death. While common genetic variants may modestly impact QT interval individually, their collective effect can significantly amplify risk of diLQTS. Consequently, this study aimed to validate a polygenic risk score (PRS) for diLQTS previously proposed by Strauss et al. Methods A retrospective cohort study was conducted utilizing patients from the Michigan Genomics Initiative prescribed 27 high-risk QT-prolonging drugs and an ECG during the prescription. The primary outcome was marked prolongation of the QTc interval (either >60 ms change from baseline or >500 ms absolute value) during treatment with a high-risk QT-prolonging drug. Results The primary outcome occurred in 12.0% of n = 6070 self-reported White, 12.4% of 558 African American, and 8.2% of 110 Asian patients. The PRS significantly associated with diLQTS in White patients [adjusted odds ratio = 1.44 (95% CI: 1.09–1.89); P = 0.009]. However the study lacked sufficient statistical power to confirm the PRS as a risk factor in African Americans [adjusted odds ratio = 2.18 (95% CI: 0.98–5.49); P = 0.073] and Asians [adjusted odds ratio = 3.21 (95% CI: 0.69–16.87); P = 0.139] due to smaller sample sizes in these groups. Conclusion The previously published PRS for diLQTS was validated in a large, real-world cohort, demonstrating its potential as a tool for identifying high-risk patients. Incorporating this PRS into routine clinical practice could enable proactive measures to prevent life-threatening diLQTS.