Hesse, Kerrick

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.

Background: Mitral valve prolapse (MVP) is a common valvular abnormality. Observational studies demonstrate an association between MVP and increased risk of sudden cardiac death from ventricular arrhythmias, coined arrhythmogenic MVP. A genetic component has been postulated but remains under-investigated. Hypothesis: Predicted deleterious variants in Mendelian cardiomyopathy and arrhythmia genes are more frequent in arrhythmogenic MVP (aMVP) compared to non-arrhythmogenic MVP. Aims: To comprehensively phenotype and genotype a single-center aMVP cohort and compare to non-aMVP from two large population-based cohorts and investigate their clinical outcomes. Methods: The aMVP cohort included 46 prospectively enrolled patients from a regional referral center for inherited cardiovascular disease and had undergone multi-modality imaging and clinical gene panel testing. The non-aMVP comparator cohorts included 453 participants from the UK Biobank (UKBB), a prospective cohort of 500,000 UK residents, and 1,418 participants from the National Institutes of Health (NIH) All of Us (AoU), a prospective cohort of 450,000 USA participants. Gene variants were annotated in ANNOVAR. Participants with and without pathogenic/likely pathogenic (P/LP) variants reported on ClinVar with ≥2-star level of evidence were considered genotype positive (G+) and negative (G-) respectively. Results: The demographics for aMVP (age 61.2 ± 12.8 years, 56.5% female) and non-aMVP (UKBB: age 59.8 ± 7.3 years, 50.7% female and AoU: age 65.3 ± 13.4 years, 76.2% female) were comparable. G+ participants were more prevalent in the aMVP cohort 10/46 (21.7%) versus non-aMVP in the UKBB 8/453 (1.8%) and in the AoU 21/1418 (1.5%); p<0.001. The most frequently implicated gene in aMVP was TTN . Of 46 in aMVP cohort, 33 (71%) underwent CMR. G+ participants had a higher frequency of late gadolinium enhancement [6/6 (100%) vs. 14/27 (51.8%), p<0.001] and wall motion abnormalities [3/6 (50%) vs. 1/27 (3.7%), p<0.001] compared to G- participants. The aMVP cohort had significantly more implantable cardioverter defibrillator (ICD) implants compared to non-aMVP cohort [5/46 (10.9%) vs. 1/453 in UKBB (0.2%), p<0.001]. G+ aMVP cohort showed a trend for developing heart failure and ICD implants compared to G- aMVP . Conclusion: P/LP variants in cardiomyopathy and arrhythmia genes are more prevalent in aMVP participants compared to non-aMVP participants. This could be a hitherto unrecognized effect modifier predicting aMVP .

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.