Aung, Nay

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.

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.

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 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.

ImportanceThe population prevalence of cardiac transthyretin amyloidosis (ATTR) caused by pathogenic variation in the TTR gene (vATTR) is unknown.ObjectiveTo estimate the population prevalence of disease-causing TTR variants and evaluate associated phenotypes and outcomes.Design, Setting, and ParticipantsThis population-based cohort study analyzed UK Biobank (UKB) participants with whole-exome sequencing, electrocardiogram, and cardiovascular magnetic resonance data. Participants were enrolled from 2006 to 2010, with a median follow-up of 12 (IQR, 11-13) years (cutoff date for the analysis, March 12, 2024). Sixty-two candidate TTR variants were extracted based on rarity (minor allele frequency ≤0.0001) and/or previously described associations with amyloidosis if more frequent.ExposureCarrier status for TTR variants.Main Outcomes and MeasuresAssociations of TTR carrier status with vATTR prevalence and cardiovascular imaging and electrocardiogram traits were explored using descriptive statistics. Associations between TTR carrier status and atrial fibrillation, conduction disease, heart failure, and all-cause mortality were evaluated using adjusted Cox proportional hazards models. Genotypic and diagnostic concordance was examined using International Statistical Classification of Diseases, Tenth Revision codes from the hospital record.ResultsThe overall cohort included 469 789 UKB participants (mean [SD] age, 56.5 [8.1] years; 54.2% female and 45.8% male). A likely pathogenic/pathogenic (LP/P) TTR variant was detected in 473 (0.1%) participants, with Val142Ile being the most prevalent (367 [77.6%]); 91 individuals (0.02%) were carriers of a variant of unknown significance . The overall prevalence of LP/P variants was 0.02% (105 of 444 243) in participants with European ancestry and 4.3% (321 of 7533) in participants with African ancestry. The LP/P variants were associated with higher left ventricular mass indexed to body surface area (β = 4.66; 95% CI, 1.87-7.44), and Val142Ile was associated with a longer PR interval (β = 18.34; 95% CI, 5.41-31.27). The LP/P carrier status was associated with a higher risk of heart failure (hazard ratio [HR], 2.68; 95% CI, 1.75-4.12) and conduction disease (HR, 1.88; 95% CI, 1.25-2.83). Higher all-cause mortality risk was observed for non-Val142Ile LP/P variants (HR, 1.98; 95% CI, 1.06-3.67). Thirteen participants (2.8%) with LP/P variants had diagnostic codes compatible with cardiac or neurologic amyloidosis. Variants of unknown significance were not associated with outcomes.Conclusions and RelevanceThis study found that approximately 1 in 1000 UKB participants were LP/P TTR variant carriers, exceeding previously reported prevalence. The findings emphasize the need for clinical vigilance in identifying individuals at risk of developing vATTR and associated poor outcomes.

Abstract The management of acute myocarditis (AM) is addressed in multiple clinical guidelines. We systematically reviewed current guidelines developed by national and international medical organizations on the management of AM to aid clinical practice. Publications in MEDLINE, EMBASE and Cochrane were identified between 1 January 2013 and 12 April 2024. Additionally, the websites of relevant organizations and the Guidelines International Network, Guideline Central, and NHS knowledge and library hub were reviewed. Two reviewers independently screened titles and abstracts, two reviewers assessed the rigour of guideline development, and one reviewer extracted the recommendations. Two of the three guidelines identified showed good rigour of development. Those rigorously developed agreed on the definition of AM, sampling serum troponin as part of the workflow for AM, testing for B-type natriuretic peptides in heart failure, key diagnostic imaging in the form of cardiovascular magnetic resonance, coronary angiography to exclude significant coronary disease, indications for endomyocardial biopsy (EMB), and indications for immunosuppression and advanced treatment options. Discrepancies exist in sampling creatine kinase-myocardial bound as a marker of myocardial injury, indications for EMB, and indications for immunosuppression and treatment of uncomplicated AM. Evidence is lacking for the use of 18F-Fluorodeoxyglucose Positron Emission Tomography for myocardial imaging, exercise restriction, follow-up measures, and genetic testing, and there are few high-quality randomized trials to support treatment recommendations. Recommendations for management of AM in the guidelines have largely been developed from expert opinion rather than trial data.

Abstract Multiple guidelines exist for the diagnosis and management of heart failure with preserved ejection fraction (HFpEF). We systematically reviewed current guidelines and recommendations, developed by national and international medical organizations, on the management of HFpEF in adults to aid clinical decision-making. We searched MEDLINE and EMBASE on 28 February 2024 for publications over the last 10 years as well as websites of organizations relevant to guideline development. Of the 10 guidelines and recommendations retrieved, 7 showed considerable rigour of development and were subsequently retained for analysis. There was consensus on the definition of HFpEF and the diagnostic role of serum natriuretic peptides and resting transthoracic echocardiography. Discrepancies were identified in the thresholds of serum natriuretic peptides and transthoracic echocardiography parameters used to diagnose HFpEF. There was agreement on the general pharmacological and supportive management of acute and chronic HFpEF. However, differences exist in strategies to identify and address specific phenotypes. Contemporary guidelines for HFpEF management agree on measures to avoid its development and the consideration of cardiac transplantation in advanced diseases. There were discrepancies in recommended frequency of surveillance for patients with HFpEF and sparse recommendations on screening for HFpEF in the general population, use of diagnostic scoring systems, and the role of newly emerging therapies.

Abstract Cardiac disease affects the heart non-uniformly. Examples include focal septal or apical hypertrophy with reduced strain in hypertrophic cardiomyopathy, replacement fibrosis with akinesia in an infarct-related coronary artery territory, and a pattern of scarring in dilated cardiomyopathy. The detail and versatility of cardiovascular magnetic resonance (CMR) imaging mean it contains a wealth of information imperceptible to the naked eye and not captured by standard global measures. CMR-derived heterogeneity biomarkers could facilitate early diagnosis, better risk stratification, and a more comprehensive prediction of treatment response. Small cohort and case–control studies demonstrate the feasibility of proof-of-concept structural and functional heterogeneity measures. Detailed radiomic analyses of different CMR sequences using open-source software delineate unique voxel patterns as hallmarks of histopathological changes. Meanwhile, measures of dispersion applied to emerging CMR strain sequences describe variable longitudinal, circumferential, and radial function across the myocardium. Two of the most promising heterogeneity measures are the mean absolute deviation of regional standard deviations on native T1 and T2 and the standard deviation of time to maximum regional radial wall motion, termed the tissue synchronization index in a 16-segment left ventricle model. Real-world limitations include the non-standardization of CMR imaging protocols across different centres and the testing of large numbers of radiomic features in small, inadequately powered patient samples. We, therefore, propose a three-step roadmap to benchmark novel heterogeneity biomarkers, including defining normal reference ranges, statistical modelling against diagnosis and outcomes in large epidemiological studies, and finally, comprehensive internal and external validations.