Ethnicity differences in geometric remodelling and myocardial composition in hypertension unveiled by cardiovascular magnetic resonance.

AIMS: Hypertensive patients of African ancestry (Afr-a) have higher incidences of heart failure and worse clinical outcomes than hypertensive patients of European ancestry (Eu-a), yet the underlying mechanisms remain misunderstood. This study investigated right (RV) and left (LV) ventricular remodelling alongside myocardial tissue derangements between Afr-a and Eu-a hypertensives. METHODS AND RESULTS: 63 Afr-a and 47 Eu-a hypertensives underwent multi-parametric cardiovascular magnetic resonance. Biventricular volumes, mass, function, mass/end-diastolic volume (M/V) ratios, T2 and pre-/post-contrast T1 relaxation times, synthetic extracellular volume, and myocardial fibrosis (MF) were measured. 3D shape modelling was implemented to delineate ventricular geometry. LV and RV mass (indexed to body-surface-area) and M/V ratio were significantly greater in Afr-a than Eu-a hypertensives (67.1 ± 21.7 vs. 58.3 ± 16.7 g/m2, 12.6 ± 3.48 vs. 10.7 ± 2.71 g/m2, 0.79 ± 0.21 vs. 0.70 ± 0.14 g/mL, and 0.16 ± 0.04 vs. 0.13 ± 0.03 g/mL, respectively; P < 0.03). Afr-a patients showed greater basal interventricular septum thickness than Eu-a patients, influencing LV hypertrophy and RV cavity changes. This biventricular remodelling was associated with prolonged T2 relaxation time (47.0 ± 2.2 vs. 45.7 ± 2.2 ms, P = 0.005) and higher prevalence (23% vs. 4%, P = 0.001) and extent of MF [2.3 (0.6-14.3) vs. 1.6 (0.9-2.5) % LV mass, P = 0.008] in Afr-a patients. Multivariable linear regression showed that modifiable cardiovascular risk factors and greater end-diastolic volume, but not ethnicity, were independently associated with greater LV mass. CONCLUSION: Afr-a hypertensives had distinctive biventricular remodelling, including increased RV mass, septal thickening and myocardial tissue abnormalities compared with Eu-a hypertensives. From this study, modifiable cardiovascular risk factors and ventricular geometry, but not ethnicity, were independently associated with greater LV myocardial mass.

An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.

Aims: Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Methods and results: Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n = 414) and five external datasets (n = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4 mL (left ventricle volume), <9.2 mL (right ventricle volume), <13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. Conclusion: We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases.

Healthcare resource use associated with the diagnosis of transthyretin amyloidosis cardiomyopathy.

OBJECTIVES: Our primary aim was to evaluate the healthcare resource use associated with the diagnosis of transthyretin amyloidosis cardiomyopathy. Second, we aim to assess the effect of the number of diagnostic tests and clinical contact points on the total time and costs between symptom onset and diagnosis defining a quantitative hypothetical optimized diagnostic pathway. SETTING: Clinical and cost data were collected from patients presenting between 2010 and 2018 in a tertiary referral institution in South London involving two participating hospitals. PARTICIPANTS: Thirty-eight adult patients with a definite diagnosis of transthyretin amyloidosis cardiomyopathy were included, mostly male (n = 28, 74%) and of African-Caribbean descent (n = 23, 64%). We excluded patients without a confirmed transthyretin amyloidosis cardiomyopathy or those on inotersen, patisiran, or diflunisal at point of referral. PRIMARY AND SECONDARY OUTCOME MEASURES: The average time between first presentation and final diagnosis, and the cost per patient per month. By comparing to a more optimal clinical pathway towards diagnosis, we considered what could be the theoretical gain in terms of time to diagnosis and financial savings. RESULTS: The average time between first presentation and final diagnosis was 2.74 years. The average cost per patient per month was higher with progressive heart failure symptoms. A hypothetical optimal pathway reduces time to diagnosis of 1.65 to 1.74 years per patient. The potential financial savings are estimated within the range of £3000 to £4800 per patient. CONCLUSIONS: Patients diagnosed with transthyretin amyloidosis cardiomyopathy have substantial healthcare resource utilization and costs starting from symptom onset. Higher costs were observed with progression in symptoms and appear linked to a delayed diagnosis. The number of additional diagnostic tests and clinical contact points may contribute to this and could represent a path to explore further for important health and cost savings, with more efficient pathways for these patients to be managed.

A case of mistaken arrhythmogenic identity during pregnancy.

Atypical LVOT ectopy can present with an RVOT morphology on ECG and differentiation to reveal this focus is in favor of benign idiopathic ventricular ectopy over an arrhythmogenic cardiomyopathy.

The alcohol-induced cardiomyopathy: A cardiovascular magnetic resonance characterization.

BACKGROUND: Alcoholic cardiomyopathy(ACM) is part of the non-ischaemic dilated cardiomyopathy(NI-DCM) spectrum. Little is known about cardiovascular magnetic resonance(CMR) features in ACM patients. The aim of this study is to describe CMR findings and their prognostic impact in ACM patients. METHODS: Consecutive ACM patients evaluated in five referral CMR centres from January 2005 to December 2018 were enrolled. CMR findings and their prognostic value were compared to idiopathic NI-DCM(iNI-DCM) patients. The main outcome was a composite of death/heart transplantation/life-threatening arrhythmias. RESULTS: Overall 114 patients (52 with ACM and 62 with iNI-DCM) were included. ACM patients were more often males compared to iNI-DCM (90% vs 64%, respectively, p ≤ 0.001) and were characterized by a more pronounced biventricular adverse remodelling than iNI-DCM, i.e. lower LVEF (31 ± 12% vs 38 ± 11% respectively, p = 0.001) and larger left ventricular end-diastolic volume (116 ± 40 ml/m2 vs 67 ± 20 ml/m2 respectively, p < 0.001). Similarly to iNI-DCM, late gadolinium enhancement (LGE), mainly midwall, was present in more than 40% of ACM patients but, conversely, it was not associated with adverse outcome(p = 0.15). LGE localization was prevalently septal (87%) in ACM vs lateral in iNI-DCM(p < 0.05). Over a median follow-up of 42 months [Interquartile Range 24-68], adverse outcomes were similar in both groups(p = 0.67). CONCLUSIONS: ACM represents a specific phenotype of NI-DCM, with severe morpho-functional features at the onset, but similar long-term outcomes compared to iNI-DCM. Despite the presence and pattern of distribution of LGE was comparable, ACM and iNI-DCM showed a different LGE localization, mostly septal in ACM and lateral in iNI-DCM, with different prognostic impact.

The Role of AI in Characterizing the DCM Phenotype.

Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.