158 Racial differences in val122Ile associated transthyretin cardiac amyloidosis

Aims The valine-to-isoleucine substitution (Val122Ile) is the most common variant of transthyretin (TTR) amyloidosis in the USA, primarily affecting individuals of African descent and leading to a restrictive cardiomyopathy. This variant has recently been identified in a cluster of White individuals in Italy. In this study we aimed to investigate differences in the clinical phenotype of Val122Ile associated TTR cardiac amyloidosis (ATTR-CA) between Black and White individuals. Methods and results In this retrospective study of 70 patients (mean age 72 years) with Val122Ile associated TTR ATTR-CA, cardiac chamber performance was compared using noninvasive pressure-volume analysis. Compared to White patients (n = 17), Black individuals (n = 53) had lower systolic blood pressures (110 vs. 131 mmHg, P < 0.001), reduced pulse pressures (41 vs. 58 mmHg, P < 0.001), and impaired renal function (eGFR 46 vs. 67 mL/min/1.73 m2, P < 0.001) at presentation. Systolic properties and arterial elastance were similar. Black patients had an end-diastolic pressure-volume relationship shifted upward and leftward relative to White patients, indicating reduced left ventricular chamber capacitance. Pressure-volume area at a left ventricular end-diastolic pressure of 30 mmHg was lower in Black compared to White individuals (8055 mmHg*ml vs. 11 538 mmHg*ml, P = 0.008). Conclusions Despite presenting at a similar age to White patients, Black individuals with Val122Ile associated ATTR-CA have a greater degree of cardiac chamber dysfunction at the time of diagnosis due to impaired ventricular capacitance. Whether these differences are attributable to amyloidosis or other cardiovascular disease requires further study.

A tripartite structure, the complex nuclear receptor element (cNRE), is a cis-regulatory module of viral origin required for atrial chamber preferential gene expression

Optimal cardiac function requires appropriate contractile proteins in each heart chamber. Atria require slow myosins to act as variable reservoirs, while ventricles demand fast myosin for swift pumping functions. Hence, myosin is under chamber-biased cis-regulatory control to achieve this functional distribution. Failure in proper regulation of myosin genes can lead to severe congenital heart dysfunction. The precise regulatory input leading to cardiac chamber-biased expression remains uncharted. To address this, we computationally and molecularly dissected the quail Slow Myosin Heavy Chain III (SMyHC III) promoter that drives specific gene expression to the atria to uncover the regulatory information leading to chamber expression and understand their evolutionary origins. We show that SMyHC III gene states are autonomously orchestrated by a complex nuclear receptor cis-regulatory element (cNRE), a 32-bp sequence with hexanucleotide binding repeats. Using in vivo transgenic assays in zebrafish and mouse models, we demonstrate that preferential atrial expression is achieved by the combinatorial regulatory input composed of atrial activation motifs and ventricular repression motifs. Through comparative genomics, we provide evidence that the cNRE emerged from an endogenous viral element, most likely through infection of an ancestral host germline. Our study reveals an evolutionary pathway to cardiac chamber-specific expression.

Machine learning analysis of chest CT to detect cardiac abnormalities in COVID19

Introduction Standard, un-gated chest CT can be used as the basis of detailed segmentation of the atrial and ventricular cardiac chambers. In conditions such as COVID19 where dedicated cardiac imaging may be hazardous or unavailable atlas-based machine learning tools allow automatic quantification of cardiac morphology and may allow early detection of abnormalities. Purpose To develop an automated screening tool to detect cardiac changes associated with COVID19 on chest/lung CT to allow early treatment and appropriate selection of patients for dedicated cardiac imaging. Methods A previously validated atlas-based cardiac contouring algorithm was modified to work within the setting of variable and severe lung pathology. The modified technique was used to segment the left and right atria and ventricles from non-contrast CT scans. We applied the developed algorithm to the Moscow University COVID19 CT dataset. 1110 scans were available. COVID19 severity was graded 0 to 4. Grade 4 was not used in analysis due to insufficient numbers. Cardiac chamber sizes were compared according to COVID19 severity status. In a limited cohort of repeat studies, the feasibility of polar mapping to demonstrated serial morphological change was tested. Results A statistically significant increase of average cardiac chamber volumes was noted relative to mild Grade 0 COVID19 at every incremental severity grade (Figure 1). Changes in average ventricular volumes were greater (up to 15.2% and 16.9% for left and right ventricles) than changes in atrial volumes (up 12.1% and 7.6% for left and right atria). Automated quantification was successful in the large majority of cases and inter-patient polar mapping of sequential data to detect progressive chamber enlargement appears feasible (Figure 2). Conclusion Machine learning methods permit automatic quantification of cardiac chamber size from standard lung CT scans. Cardiac changes on lung CT examinations may be used to identify cardiac abnormalities at an early stage and could be useful to triage individuals for dedicated cardiac investigations. With further refinement, this method may be useful to detect and track temporal cardiac changes in COVID19, as well as in other pulmonary pathology and conditions in which chest CT is routinely used. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): SPHERE Research consortium Figure 1 Figure 2

Fully automated quantification of cardiac chamber and function assessment in 2-D Echocardiography: clinical feasibility of deep learning-based algorithms

Objectives: We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation.Background: Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated.Methods: We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), LV end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra- observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views).Results: The overall performance of all DL methods was excellent (average Dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90), with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC =0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899).Conclusions: The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.

Choosing the Adaptive Cardiac Phase for Assessing Cardiac Dimensions Using Cardiac Computed Tomography for Heart Disease

Background Choosing a suitable cardiac cycle to measure cardiac chamber dimensions and wall thickness can be a more accurate assessment of cardiovascular disease. Methods Cardiac CT was performed on 137 patients for suspected coronary disease. The parameters of left atrium (LA), left ventricle (LV), right atrium (RA), and right ventricle (RV), as well as the wall thickness of LV were measured in different cardiac phases. The general linear mixed model was used to analyze differences in different phases and the correlation between these parameters and traditional risk factors. ROC analysis was performed to estimate LA enlargement. Results The dimensions of LA, RA, and LV wall thickness achieved the maximum at the phase of 35–45%, and those of LV and RV, at 95–5%. Whereas, the changes of LA-B (antero-posterior diameter), LV-D1 (basal dimension), RA-B (minor dimension) and RV-D2 (mid cavity dimension) were relatively more stable during the cardiac cycle. The maximum LA-B diameter(95%CI 36.92,38.48mm), LV-D1 diameter(95%CI 44.36,45.83mm), RA-B diameter(95%CI 48.75,50.61mm), and RV-D2 diameter(95%CI 30.83,32.84mm) and the maximum interventricular septum thickness( 95%CI 10.79,11.51mm) was acquired. Heart rate (HR) and smoking were potential indicators of LVD2 (mid cavity dimension), while HR and LV myocardial mass were potential indicators of LVD3 (apical-basal dimension). In phase 45%, the cut-off value of LA-B was 37.12mm has high sensitivity of 90.9% for predicting LA enlargement. Conclusion Cardiac chamber dimensions and wall thickness vary with the cardiac phase. Choosing the adaptive cardiac phase for evaluating these parameters obtained by cardiac CT could provide a more accurate clinical measurement. Trial registration retrospectively registered.