Munroe, Patricia B

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 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.

Precision medicine, which among other aspects includes an individual's genomic data in diagnosis and management, has become the standard‐of‐care for Mendelian cardiovascular disease (CVD). However, early identification and management of asymptomatic patients with potentially lethal and manageable Mendelian CVD through screening, which is the promise of precision health, remains an unsolved challenge. The reduced costs of genomic sequencing have enabled the creation of biobanks containing in‐depth genetic and health information, which have facilitated the understanding of genetic variation, penetrance, and expressivity, moving us closer to the genotype‐first screening of asymptomatic individuals for Mendelian CVD. This approach could transform health care by diagnostic refinement and facilitating prevention or therapeutic interventions. Yet, potential benefits must be weighed against the potential risks, which include evolving variant pathogenicity assertion or identification of variants with low disease penetrance; costly, stressful, and inappropriate diagnostic evaluations; negative psychological impact; disqualification for employment or of competitive sports; and denial of insurance. Furthermore, the natural history of Mendelian CVD is often unpredictable, making identification of those who will benefit from preventive measures a priority. Currently, there is insufficient evidence that population‐based genetic screening for Mendelian CVD can reduce adverse outcomes at a reasonable cost to an extent that outweighs the harms of true‐positive and false‐positive results. Besides technical, clinical, and financial burdens, ethical and legal aspects pose unprecedented challenges. This review highlights key developments in the field of genotype‐first approaches to Mendelian CVD and summarizes challenges with potential solutions that can pave the way for implementing this approach for clinical care.

Abstract Aims Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification. Methods and results We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P < 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models. Conclusion A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.

Background: There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features. Methods: UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative. Results: Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes. Conclusions: In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.

Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiomyopathy characterised by ventricular arrhythmia and an increased risk of sudden cardiac death (SCD). Numerous genetic determinants and phenotypic manifestations have been discovered in ACM, posing a significant clinical challenge. Further to this, wider evaluation of family members has revealed incomplete penetrance and variable expressivity in ACM, suggesting a complex genotype-phenotype relationship. This review details the genetic basis of ACM with specific genotype-phenotype associations, providing the reader with a nuanced perspective of this condition; whilst also proposing a future roadmap to delivering precision medicine-based management in ACM.

Background There exists a wide interindividual variability in blood pressure ( BP ) response to β 1 ‐blockers. To identify the genetic determinants of this variability, we performed a pharmacogenomic genome‐wide meta‐analysis of genetic variants influencing β 1 ‐blocker BP response. Methods and Results Genome‐wide association analysis for systolic BP and diastolic BP response to β 1 ‐blockers from 5 randomized clinical trials consisting of 1254 patients with hypertension of European ancestry were combined in meta‐analysis and single nucleotide polymorphisms (SNPs) with P <10 −4 were tested for replication in 2 independent randomized clinical trials of β 1 ‐blocker–treated patients of European ancestry (n=1552). Regions harboring the replicated SNP s were validated in a β 1 ‐blocker–treated black cohort from 2 randomized clinical trials (n=315). A missense SNP rs28404156 in BST 1 was associated with systolic BP response to β 1 ‐blockers in the discovery meta‐analysis ( P =9.33×10 −5 , β=−3.21 mm Hg) and replicated at Bonferroni significance ( P =1.85×10 −4 , β=−4.86 mm Hg) in the replication meta‐analysis with combined meta‐analysis approaching genome‐wide significance ( P =2.18×10 −7 ). This SNP in BST 1 is in linkage disequilibrium with several SNP s with putative regulatory functions in nearby genes, including CD 38 , FBXL 5 , and FGFBP 1 , all of which have been implicated in BP regulation. SNP s in this genetic region were also associated with BP response in the black cohort. Conclusions Data from randomized clinical trials of 8 European ancestry and 2 black cohorts support the assumption that BST 1 containing locus on chromosome 4 is associated with β 1 ‐blocker BP response. Given the previous associations of this region with BP , this is a strong candidate region for future functional studies and potential use in precision medicine approaches for BP management and risk prediction.

Selection of antihypertensive treatment according to self-defined ethnicity is recommended by some guidelines but might be better guided by individual genotype rather than ethnicity or race. We compared the extent to which variation in blood pressure response across different ethnicities may be explained by genetic factors: genetically defined ancestry and gene variants at loci known to be associated with blood pressure. We analyzed data from 5 trials in which genotyping had been performed (n=4696) and in which treatment responses to β-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blocker, thiazide or thiazide-like diuretic and calcium channel blocker were available. Genetically defined ancestry for proportion of African ancestry was computed using the 1000 genomes population database as a reference. Differences in response to the thiazide diuretic hydrochlorothiazide, the β-blockers atenolol and metoprolol, the angiotensin-converting enzyme inhibitor lisinopril, and the angiotensin receptor blocker candesartan were more closely associated to genetically defined ancestry than self-defined ethnicity in admixed subjects. A relatively small number of gene variants related to loci associated with drug-signaling pathways ( KCNK3, SULT1C3, AMH, PDE3A, PLCE1, PRKAG2 ) with large effect size (−3.5 to +3.5 mm Hg difference in response per allele) and differing allele frequencies in black versus white individuals explained a large proportion of the difference in response to candesartan and hydrochlorothiazide between these groups. These findings suggest that a genomic precision medicine approach can be used to individualize antihypertensive treatment within and across populations without recourse to surrogates of genetic structure such as self-defined ethnicity.