Citations

Abstract Cardiovascular disease is the leading cause of morbidity and mortality globally, imposing a substantial patient burden. Digital biomarkers derived from digital health technologies enable quantifiable, near-continuous, real-world capture of pathophysiological signals and are increasingly used across cardiovascular medicine. Their growth is often supported by big data analytics, artificial intelligence and connected sensors within the Internet of Things. This scientific statement systematically reviews digital biomarkers, their clinical utility across the cardiovascular care continuum, and summarizes major methodological and implementation challenges. We searched PubMed/MEDLINE, Embase and Scopus for phase III–IV cardiovascular trials published between 1 January 2019 and 1 August 2024 that collected digital biomarkers. We identified 541 records and included 32 trials (40 reports) enrolling 32 246 participants (44.71% women; mean age 66.2 ± 11.0 years). Heart failure was the most frequent target condition (16 trials, 50%), followed by cardiac implantable electronic devices trials (12 trials, 38%). Digital biomarkers included cardiac rhythm and heart rate, blood pressure, body weight, impedance-based measures, haemodynamic pressures, and physical activity and sleep patterns captured using diverse sensor technologies. Methodological quality was good (modal quality score 5; range 3–7), although most trials were unblinded. Current studies suggest potential clinical utility in selected cardiovascular conditions. Implementation remains challenging because of measurement variability, privacy and regulatory requirements, affordability and reimbursement barriers and inequities in access. Robust validation and outcome- and cost-effectiveness evidence are still needed. Registration number: CRD42024573879.

Abstract Heart involvement in eosinophilia represents a rare entity with substantial cardiac morbidity and mortality. This disorder is often overlooked and diagnosed in its advanced stages. In a subgroup of patients with eosinophilic heart disease, peripheral blood eosinophilia may not be present, and the biopsy of the affected organ or tissue is essential to establish the correct diagnosis. In this clinical consensus statement, an expert consensus group reviews the epidemiology, aetiology, diagnosis, and management of eosinophilic heart disease. The aim of the document is to increase awareness about the cardiac involvement associated with eosinophilia in order to diagnose the disease in early phases and prevent deleterious heart damage and adverse outcomes of affected individuals. The spectrum of physiological and immunological derangements requires a multidisciplinary approach for optimal management of affected individuals.

Abstract Background Myocardial strain derived from cardiovascular magnetic resonance (CMR) using feature tracking (FT) offers incremental prognostic value beyond traditional imaging markers. However, its broader clinical application is hindered by the lack of population-specific reference ranges. This study establishes age-, sex-, and ethnicity-specific normative data for FT-CMR-derived myocardial strain metrics from the Healthy Hearts Consortium (HHC). Methods The HHC is a large, international, multicohort initiative comprising CMR data from 9,088 rigorously screened healthy adults across six centers. The cohort spans the full adult age range and includes greater ethnic diversity than any comparable dataset. CMR scans were acquired using standardized protocols. Image analysis followed pre-specified standard operating procedures and was performed by two experienced readers using two commercial platforms (CVI42®, Circle Cardiovascular Imaging; suiteHEART®, Neosoft). Metrics extracted included: (i) global longitudinal, circumferential, and radial FT strain for both ventricles and atria; (ii) strain rate; (iii) annular plane systolic excursion (MAPSE, TAPSE); and (iv) rapid strain metrics (long-axis, junctional strain) for the left ventricle and atria. Comprehensive quality control involved visual review and exclusion of biologically implausible or extreme outlier values. Reference ranges were calculated using the 95% prediction interval method, stratified by age deciles (20–80 years), sex, and ethnicity (White, Black, Chinese, South Asian, Mixed/Other). The impact of technical factors—scanner vendor, field strength (1.5T vs 3.0T), and software platform—was also assessed. Results The study included 4,452 men (49%) and 4,636 women (51%), aged 18 to 83 years (mean 61 ± 13). The cohort was ethnically diverse: White (81.7%), South Asian (5.6%), Mixed/Other (5.3%), Black (3.7%), and Chinese (3.7%). Women consistently exhibited higher absolute strain values than men. A clear age-related decline in strain was observed, particularly in left ventricular global circumferential and longitudinal strain. Ethnic differences were most pronounced between White and Black, and White and Chinese individuals for RV and LV strain metrics; in contrast, RA, LA, and rapid strain metrics showed minimal ethnic variation. Conclusion This study delivers the most comprehensive age-, sex-, and ethnicity-specific reference ranges to date for FT-CMR-derived myocardial strain. These normative values support improved clinical interpretation, aiding diagnosis and risk stratification in diverse populations. The findings mark a pivotal advancement towards the routine integration of myocardial strain assessment in cardiovascular imaging practice.

Abstract Aims Left ventricular hypertrophy (LVH) is a strong predictor of cardiovascular disease. We previously compared supervised machine learning techniques to classify cardiac magnetic resonance (CMR)-derived LVH using electrocardiogram (ECG) and clinical variables in 37 534 UK Biobank participants, obtaining an area under the receiving operating curve (AUROC) of 0.85, but with limited specificity and requiring external validation. In this study, we develop a deep learning (DL) model to improve classification with external evaluation in the Study of Health in Pomerania (SHIP). Methods and results We analysed 12-lead ECGs of 48 835 participants from the UK Biobank imaging study. The dataset was split into a training set (70%), validation set (15%), and test set (15%) for performance evaluation. The model architecture was a fully convolutional network, for which the input was the participants’ median ECG and clinical variables and the predicted indexed left ventricular mass (iLVM) as the output. A subsequent logistic regression model was used to recalibrate iLVM predictions. In UK Biobank, 717 (1.5%) participants had CMR-derived LVH and the AUROC for the DL model was 0.97. The ECG components most predictive of LVH were the QRS complex and ventricular rate. The DL model outperformed our supervised algorithms, previous DL modelling efforts and clinical ECG benchmarks. There was modest generalizability of the DL model to 1423 participants in SHIP (AUROC 0.78), with differences in clinical profile, ECG acquisition, and CMR labelling as important factors. Conclusion Our findings support the feasibility of scalable DL-based screening tools for the prediction of LVH from the ECG, whilst highlighting the need for model development using larger datasets with greater diversity to ensure generalizability.

Importance Sleep disordered breathing (SDB) is a well-established contributor to cardiovascular morbidity, mediated by intermittent hypoxemia, autonomic dysregulation, and endothelial dysfunction. Patients with hypertrophic cardiomyopathy (HCM) may be especially at risk for SDB, but the clinical impact of SDB in this population remains unclear. Objective To define the prevalence and subtypes of SDB in HCM and examine their association with echocardiographic parameters and cardiac biomarker expression. Design, Setting, and Participants A prospective cohort study was conducted between April 18, 2018, and January 15, 2024, at a single tertiary referral center specializing in HCM care. Adults with HCM (left ventricular wall thickness ≥15 mm or pathogenic variants) were recruited from an institutional registry. Patients diagnosed with SDB or current pregnancy were excluded. Patients underwent polysomnography, with comparative assessment of echocardiographic, electrocardiographic, and biomarker indices. Observers were blinded to polysomnographic results. Data analysis was performed from April 11, 2024, to July 25, 2024. Exposures SDB classified via polysomnography using apnea-hypopnea index thresholds, with subtypes including obstructive sleep apnea and central sleep apnea and with event severity, hypoxemia, and sleep architecture disruption quantified. Main Outcomes and Measures Echocardiographic indices, cardiac biomarker expression, functional status, apnea-hypopnea index, and overnight hypoxemia. Results Among 154 patients (median [IQR] age, 60 [48-68] years; 102 [66.2%] male), 91 (59.1%) were diagnosed with SDB. Those with SDB, compared with those without SDB, had higher left ventricular mass index (median [IQR], 128 [107-161] vs 109 [96-134] g/m 2 ; P = .03), E/e′ ratio (median [IQR], 12.5 [10.0-15.0] vs 10.0 [8.3-14.5]; P = .04), and baseline troponin-T level (median [IQR], 0.013 [0.009-0.022] vs 0.011 [0.007-0.015] ng/mL [to convert to micrograms per liter, multiply by 1]; P = .04) and greater overnight troponin-T level increases (change in median [IQR], 0.0021 [−0.0029 to 0.0062] vs 0.0002 [−0.0022 to 0.0026] ng/mL; P = .02). New York Heart Association class II or III symptoms were more common in those with SDB (48 [52.7%] vs 17 [27.0%]; P = .005). Hypertension and diabetes were more prevalent among patients with SDB than without SDB (hypertension: 67 [73.6%] vs 36 [57.1%]; P = .03; diabetes: 14 [15.4%] vs 3 [4.8%]; P = .04), whereas rates of atrial fibrillation and prior myectomy did not differ significantly between groups. Conclusions and Relevance This study suggests that undiagnosed SDB is highly prevalent in patients with HCM and that SDB is associated with adverse myocardial remodeling, greater diastolic dysfunction, and elevated troponin-T levels, indicating subclinical myocardial injury. SDB may contribute to HCM pathophysiology and symptom burden, supporting the rationale for randomized clinical trials to determine the impact of treating SDB on symptoms and clinical outcomes in patients with HCM.

Abstract Aims Left ventricular hypertrophy (LVH) regionality carries diagnostic and prognostic importance. Mean absolute deviation of maximum segmental wall thickness (MadWT) is a novel left ventricular wall thickness (LVWT) heterogeneity biomarker from cardiovascular magnetic resonance imaging (CMR). Objectives To compare MadWT to indexed LV mass (LVMi), maximum (MaxWT) and mean (MeanWT) wall thickness to predict incident cardiovascular disease (CVD) and differentiate physiological from pathological LVH in highly physically active individuals. Methods and results Deep learning-assisted analysis of 44 930 UK Biobank CMR scans produced WT indices. Cox regression modelled major adverse cardiovascular events (MACE), heart failure (HF), arrhythmia, and all-cause death against LVWT indices. In the top 1% most physically active biomarker differences between propensity score matched hypertensive and non-hypertensive groups were compared. Over median (Q1, Q3) follow-up of 5.7 (4.9, 7.1) years, MadWT, MaxWT, MeanWT, and LVMi were associated with greater risk of MACE, HF, arrhythmia (P < 0.05), but not all-cause death (P > 0.05). After adjusting for CMR biomarkers, including LVMi, MadWT remained independently prognostic of the greatest number of endpoints, including MACE, HF, and arrhythmia [HR 1.13 (1.04–1.23); HR 1.15 (1.01–1.32); and HR 1.26 (1.18–1.35) respectively]. In the top 1% most physically active by three metrics, MadWT was the only significantly different biomarker between hypertensive and non-hypertensive participants (P < 0.05). Conclusion MadWT is important prognostically beyond LV mass and may be useful when differentiating physiological from hypertensive LVH. Although findings require confirmation in athletic and diseased cohorts, MadWT is readily translatable to deep learning-assisted clinical CMR reporting, especially in early unexplained LVH.

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.

Abstract Autonomic neural control of the cardiovascular system is formed of complex and dynamic processes able to adjust rapidly to mitigate perturbations in hemodynamics and maintain homeostasis. Alterations in autonomic control feature in the development or progression of a multitude of diseases with wide‐ranging physiological implications given the neural system's responsibility for controlling inotropy, chronotropy, lusitropy, and dromotropy. Imbalances in sympathetic and parasympathetic neural control are also implicated in the development of arrhythmia in several cardiovascular conditions sparking interest in autonomic modulation as a form of treatment. A number of measures of autonomic function have shown prognostic significance in health and in pathological states and have undergone varying degrees of refinement, yet adoption into clinical practice remains extremely limited. The focus of this contemporary narrative review is to summarize the anatomy, physiology, and pathophysiology of the cardiovascular autonomic nervous system and describe the merits and shortfalls of testing modalities available. © 2023 American Physiological Society. Compr Physiol 13:4493‐4511, 2023.

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.