An automated method for BRISQUE quantification of image quality in echocardiography

Background

Echocardiography (echo) remains the most widely used imaging modality for the assessment, monitoring, and prognostication of the heart. Despite its prevalence, standardisation efforts for echo chamber quantification are ongoing, with challenges owing to subjectivity during acquisition and analysis. Furthermore, the confidence in derived functional indices is often dependent on the quality of the acquired images. However, few studies have investigated the accuracy of echo measurements compared to a reference modality such as cardiac magnetic resonance (CMR) imaging, when stratified by image quality.

Purpose

To develop an objective and automated method to quantify echo image quality, and subsequently to investigate the relationship between image quality and patient demographics, as well as the magnitude of bias in left ventricular (LV) functional indices compared with CMR.

Methods

Transthoracic apical 2D echo (2DE) and 3D echo (3DE) data from 128 participants (72 healthy controls and 56 patients with acquired heart disease) were used to train a BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) algorithm [1]. Briefly, feature extraction was performed by fitting pixel luminances to a generalised Gaussian distribution (GGD), followed by support vector regression to correlate features (i.e., shape, variance, and mean parameters of the GGD) to quality scores (Fig. 1). Independent BRISQUE models were trained on 580 2DE images (consisting of 2-, 3-, and 4-chamber views) and 128 targeted LV 3DE acquisitions at end-diastole, each assigned a subjective perceived quality score between 1 (poor) and 9 (excellent) by a single observer. LV indices including end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and global longitudinal strain (GLS), were assessed according to standard guidelines. Resultant BRISQUE scores were plotted against patient demographics (age, height, weight) and the measurement bias by comparison to CMR (acquired within 1 hour of echo).

Results

Several linear relationships (where P-value of slope <0.05) were observed between demographics, cardiac indices, and BRISQUE scores. Increasing patient weight (and height in 3DE) were found to be associated with poorer image quality. There was no apparent relationship between image quality and age. Of interest, EF exhibited a relationship with image quality in both 2DE and 3DE (Fig. 2), whereby higher quality images tended to overestimate EF, while lower quality images underestimated EF. For 3DE, image quality dependency was also observed for ESV and GLS biases.

Conclusions

BRISQUE can objectively quantify image quality to produce scores which correlate to those of an expert observer, with potential utility for the standardised quantification of echo image quality. Using this method, it may be possible to predict patient characteristics which adversely impact echo quality, as well as the magnitude of measurement biases for certain functional indices.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand; National Heart Foundation (NHF) of New Zealand

Right ventricular quantification using 3D echocardiography: a comparison with CMR

Background

Volumetric and functional right ventricular (RV) indices such as ejection fraction (EF) and global strains are known independent predictors of adverse cardiovascular events. While cardiac magnetic resonance (CMR) imaging remains the reference standard for volume quantification, echocardiography is more accessible and allows for rapid ventricular assessment. Compared to conventional 2D echocardiography, 3D echocardiography (3DE) enables full volume acquisitions and the ability to circumvent geometric assumptions. Given the complexity of RV geometry and sensitivity to image plane positioning, this advantage offers the potential to obtain more accurate diagnostic measurements.

Purpose

Tools for RV analysis in 3DE have been less extensively studied compared to those for the left ventricle (LV). We sought to quantify discrepancies in RV indices derived from 3DE and CMR.

Methods

Transthoracic real-time 3DE and cine CMR imaging were performed in 20 prospectively recruited participants (12 patients with acquired cardiac disease and 8 healthy controls), <1 hour apart. Dynamic 3D biventricular models were constructed semi-automatically from CMR by identifying fiducial landmarks, correcting in-plane breath-hold mis-registrations, and interactively fitting contours to the endocardial and epicardial borders on long- and short-axis slices. For 3DE, right ventricular endocardial models were created by fitting contours on 2D image planes resampled from the 3D volume at end-diastole and end-systole, which were subsequently tracked over one cardiac cycle (Figure 1). RV indices including end-diastolic volume (EDV), end-systolic volume (ESV), EF, global longitudinal strain (GLS), and global circumferential strain (GCS) were calculated from the 3DE- and CMR-derived 3D geometric models and compared. Paired-sample t-tests were performed to identify statistically significant differences (where P<0.05), and intraclass correlation coefficients (ICC) for absolute agreement were computed to assess the reliability for each measurement.

Results

Differences (mean ± SD) in RV indices between 3DE and CMR, with corresponding ICCs are presented in Table 1. Statistically significant differences in RV EDV, ESV, EF, and GLS were observed, with 3DE consistently underestimating volumes and overestimating function when compared to CMR. Although a statistically significant difference in RV GCS was not observed, a low ICC score indicated poor reliability.

Conclusions

Volume underestimation in RV indices between 3DE and CMR were found to be larger than those previously reported for the LV, which is likely due to the increased geometric complexity and surface area to volume ratio for the RV. Moreover, 3DE tends to overestimate RV function in terms of EF and GLS, which may impact treatment pathways if used in a clinical setting. Recognising systematic differences between modalities reinforces the need to further develop 3DE technologies for more accurate RV quantification.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand;National Heart Foundation (NHF) of New Zealand

Leveraging CMR for 3D echocardiography: an annotated multimodality dataset for AI

Funding Acknowledgements: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council of New Zealand (HRC)

National Heart Foundation of New Zealand (NHF)

Segmentation of the left ventricular myocardium and cavity in 3D echocardiography (3DE) is a critical task for the quantification of systolic function in heart disease. Continuing advances in 3DE have considerably improved image quality, prompting increased clinical uptake in recent years, particularly for volumetric measurements. Nevertheless, analysis of 3DE remains a difficult problem due to inherently complex noise characteristics, anisotropic image resolution, and regions of acoustic dropout.

One of the primary challenges associated with the development of automated methods for 3DE analysis is the requirement of a sufficiently large training dataset. Historically, ground truth annotations have been difficult to obtain due to the high degree of inter- and intra-observer variability associated with manual 3DE segmentation, thus, limiting the scope of AI-based solutions. To address the lack of expert consensus, we instead used labels derived from cardiac magnetic resonance (CMR) images of the same subjects. By spatiotemporally registering CMR labels to corresponding 3DE image data on a per subject basis (Figure 1), we collated 520 annotated 3DE images from a mixed cohort of 130 human subjects (2 independent single-beat acquisitions per subject at end-diastole and end-systole) consisting of healthy controls and patients with acquired cardiac disease. Comprising images acquired across a range of patient demographics, this curated dataset exhibits variation in image quality, 3DE acquisition parameters, as well as left ventricular shape and pose within the 3D image volume.

To demonstrate the utility of such a dataset, nn-UNet, a self-configuring deep learning method for semantic segmentation was employed. An 80/20 split of the dataset was used for training and testing, respectively, and data augmentations were applied in the form of scaling, rotation, and reflection. The trained network was capable of reproducing measurements derived from CMR for end-diastolic volume, end-systolic volume, ejection fraction, and mass, while outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility (Table I).

As part of ongoing efforts to improve the accuracy and efficiency of 3DE analysis, we have leveraged the high resolution and signal-to-noise-ratio of CMR (relative to 3DE), to create a novel, publicly available benchmark dataset for developing and evaluating 3DE labelling methods. This approach not only significantly reduces the effects of observer-specific bias and variability in training data arising from conventional manual 3DE analysis methods, but also improves the agreement between cardiac indices derived from 3DE and CMR.

Figure 1. Data annotation workflow Table I. Results

Left ventricular dimensions and mass measurement from 3D echocardiography: are we there yet?

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand and National Heart Foundation (NHF) of New Zealand

Introduction—Echocardiographic measures of left ventricular (LV) structure and size, including LV wall thickness and LV end-diastolic dimension (LVID), provide important information in the assessment of patients with heart disease. For example, LV mass is a predictor of outcome for patients with hypertension and LVID is a predictor of cardiac resynchronisation response in patients with heart failure. Advances in 3D echocardiography (3DE) have enabled full-volume acquisitions, which overcome geometric assumptions present in conventional 2D echocardiography (2DE), providing a more accurate representation of cardiac geometry. Although numerous validation studies have been performed for 3DE-derived LV volumes, comparisons of LV dimension by 3DE against established methods are limited.

Purpose—We sought to compare routine LV dimension measurements between 3DE and 2DE, with validation using cardiac magnetic resonance (CMR) imaging.

Methods—Transthoracic echocardiography (2D and 3D) and cine CMR imaging were performed in 62 prospectively recruited participants (47 healthy controls, 9 patients with LVH, 6 patients with aortic regurgitation), <1 h apart. 2DE LV dimension measurements (interventricular septum [IVS], posterior wall thickness [PWT], and LVID) were taken at end-diastole from the parasternal long axis, and mass was calculated using the linear method based on ASE/EACVI guidelines. For 3DE, 3D geometric models of the LV were constructed by interactively fitting surfaces to the endocardium and epicardium using previously validated software, from which corresponding LV dimension measurements and mass were extracted. Measurements were obtained from CMR by a similar 3D geometric modelling process.

Results—Differences (mean ± SD) in LV dimension measurements between the three modalities and intraclass correlation coefficients (ICC) are presented in Table I. When compared with CMR, 3DE exhibited higher agreement in terms of LVID and mass than 2DE, but lower agreement in wall thickness measurements. Statistically significant differences were found between 2DE and 3DE for PWT, LVID, and mass, as well as 2DE and CMR for LVID and mass (where P < 0.01 for a paired sample t­-test, marked with an asterisk). Meanwhile, there were no statistically significant differences between 3DE and CMR for IVS, PWT, LVID, or mass.

Conclusions—Our results demonstrate that 3DE is superior to 2DE in terms of LVID and mass quantification, exhibiting good agreement with CMR. 3DE exhibited moderate and poor agreement for IVS and PWT, respectively, with both 2DE and CMR, likely due to the lower spatial resolution of 3DE. Further advances in 3DE image quality and analysis tools are therefore needed to improve accuracy of wall thickness measurements. Since 2DE imaging plane and probe positioning can result in oblique measurement and underestimation of LVID, the assessment of LVID and mass by 3DE is likely to lead to more accurate diagnostic and prognostic outcomes. Abstract Table 1

Longitudinal strain measurement by 3D modelling from cine CMR: feasibility and comparison to 2D speckle tracking echocardiography

Background

Global longitudinal strain (GLS) has emerged as a sensitive index of left ventricular (LV) systolic function with greater prognostic value than LV ejection fraction (LVEF) in a variety of cardiac disorders. While GLS is routinely derived from 2D speckle tracking echocardiography (STE) and feature tracking in cardiac magnetic resonance (CMR) imaging, calculation of strain via 3D geometric modelling enables analyses of deformation that are independent of 2D image plane constraints.

Purpose

We sought to compare longitudinal strain measurements extracted from geometric 3D analysis of CMR against values obtained from conventional 2D-STE.

Methods

Consecutive 2D-echocardiography (2D-echo) and steady-state free precession multiplanar cine CMR scans were performed in 80 prospectively recruited participants (48 healthy controls with LVEF range 53–74%, 30 patients with non-ischaemic cardiac disease with LVEF range 25–77%, and 2 heart transplant recipients with LVEF 53% and 58%), <1 hour apart. Average endocardial peak GLS from 2D-STE was calculated offline using vendor-independent clinical software from apical triplane (2, 3 and 4-chamber) images for each of the standardised LV walls (anterior, anteroseptal, inferoseptal, inferior, inferolateral, anterolateral). Dynamic 3D geometric models of the LV were reconstructed from 3 long- and 6 short-axis CMR slices over one cardiac cycle. Corresponding longitudinal strain measurements were then evaluated by extracting analogous endocardial arc lengths (apex to base of each LV wall) from the 3D LV model. Finally, an average peak GLS was calculated as the mean of the peak longitudinal strains in each LV wall.

Results

GLS measured by 2D-STE ranged between −6.5% and −27.9% for the study population. A two-way mixed-effects intraclass correlation coefficient (ICC) for absolute agreement of 0.820 (95% CI: [0.720, 0.885]) demonstrated good correlation between average GLS obtained from 2D-STE and CMR. A Bland-Altman analysis revealed a minimal bias (<1%) and 95% limits of agreement (LOA) between −6.3% and 5.5% (Fig. 1), with no apparent proportional bias. Comparatively lower correlation and wider LOA between longitudinal strains from 2D-STE and CMR were observed for each LV wall (Table I).

Conclusions

Fully automated calculation of LV GLS can be obtained from geometric 3D CMR analysis. Average peak GLS from cine CMR exhibits good agreement with 2D-STE, despite showing only moderate agreement at each LV wall. The increased discrepancy in regional longitudinal strain may be attributed to subjective plane positioning in 2D-echo, which can be expected to improve with advances in 3D-STE. The calculation of GLS by 3D geometric modelling may enhance the diagnostic value of routine cine CMR examinations for LV systolic function assessment.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand and National Heart Foundation (NHF) of New Zealand Figure 1. Bland-Altman analysisTable I. Regional correlations

Automated analysis of 3D-echocardiography using spatially registered patient-specific CMR meshes

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Foundation (NHF) of New Zealand Health Research Council (HRC) of New Zealand

Artificial intelligence shows considerable promise for automated analysis and interpretation of medical images, particularly in the domain of cardiovascular imaging. While application to cardiac magnetic resonance (CMR) has demonstrated excellent results, automated analysis of 3D echocardiography (3D-echo) remains challenging, due to the lower signal-to-noise ratio (SNR), signal dropout, and greater interobserver variability in manual annotations. As 3D-echo is becoming increasingly widespread, robust analysis methods will substantially benefit patient evaluation. 

We sought to leverage the high SNR of CMR to provide training data for a convolutional neural network (CNN) capable of analysing 3D-echo. We imaged 73 participants (53 healthy volunteers, 20 patients with non-ischaemic cardiac disease) under both CMR and 3D-echo (<1 hour between scans). 3D models of the left ventricle (LV) were independently constructed from CMR and 3D-echo, and used to spatially align the image volumes using least squares fitting to a cardiac template. The resultant transformation was used to map the CMR mesh to the 3D-echo image. Alignment of mesh and image was verified through volume slicing and visual inspection (Fig. 1) for 120 paired datasets (including 47 rescans) each at end-diastole and end-systole.

100 datasets (80 for training, 20 for validation) were used to train a shallow CNN for mesh extraction from 3D-echo, optimised with a composite loss function consisting of normalised Euclidian distance (for 290 mesh points) and volume. Data augmentation was applied in the form of rotations and tilts (<15 degrees) about the long axis. The network was tested on the remaining 20 datasets (different participants) of varying image quality (Tab. I). For comparison, corresponding LV measurements from conventional manual analysis of 3D-echo and associated interobserver variability (for two observers) were also estimated.

Initial results indicate that the use of embedded CMR meshes as training data for 3D-echo analysis is a promising alternative to manual analysis, with improved accuracy and precision compared with conventional methods. Further optimisations and a larger dataset are expected to improve network performance.

(n = 20) LV EDV (ml) LV ESV (ml) LV EF (%) LV mass (g) Ground truth CMR 150.5 ± 29.5 57.9 ± 12.7 61.5 ± 3.4 128.1 ± 29.8 Algorithm error -13.3 ± 15.7 -1.4 ± 7.6 -2.8 ± 5.5 0.1 ± 20.9 Manual error -30.1 ± 21.0 -15.1 ± 12.4 3.0 ± 5.0 Not available Interobserver error 19.1 ± 14.3 14.4 ± 7.6 -6.4 ± 4.8 Not available Tab. 1. LV mass and volume differences (means ± standard deviations) for 20 test cases. Algorithm: CNN – CMR (as ground truth). Abstract Figure. Fig 1. CMR mesh registered to 3D-echo.

150 ProBNP glycosylation of threonine 71 is increased with obesity in patients with heart failure

Funding Acknowledgements

The National Heart Foundation of New Zealand

Background

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. Plasma levels of B-type natriuretic peptide (BNP) or its amino terminal congener (NT-proBNP) are used for HF diagnosis and risk stratification.  Circulating levels of both BNP and NT-proBNP are reduced by obesity and this phenomenon is one of the key weaknesses of the diagnostic performance of the natriuretic peptides in HF.  Formation of BNP from enzymatic cleavage of proBNP1-108 between residues 76 and 77 by corin and/or furin is influenced by the degree of proBNP glycosylation, therefore we investigated the relationship between proBNP glycosylation, plasma NT-proBNP and body mass index (BMI) in HF patients.

Methods

Three assays were developed to distinguish between total proBNP (glycosylated plus non-glycosylated proBNP), proBNP not glycosylated at threonine 71 (NG-T71) and proBNP not glycosylated in the central region (NG-C). Intra and inter-assay CVs were <15%, limits of detection were <2 pmol/L and samples diluted in parallel.

Results

Applying these assays and an NT-proBNP assay to plasma samples from 106 healthy volunteers and 238 patients with HF determined that concentrations (median(IQR)) of proBNP, NG-T71 and NT-proBNP were greater in HF patients compared to controls (24.9 (3.6-55), 9.4 (1.5-21) and 212 (104-409) pmol/L vs 3.0 (1.5-19), 3.0 (1.5-14.5) and 4.7 (2-8) pmol/L respectively, all p < 0.012).  NG-C was undetectable in most samples. ProBNP levels in HF patients with BMI above and below 30 kg/m2 were not different (21.9 (2.6-70) pmol/L and 25.7 (3.9-53) pmol/L respectively, p = 0.85), whereas HF patients with BMI > 30 had lower NT-proBNP and NG-T71 levels (121 (64-248) and 3 (1.5-16) pmol/L verse 271 (178-486) and 13.5 (1.5-24.2) pmol/L respectively, p < 0.003) and higher proBNP:NT-proBNP and proBNP:NG-T71 ratios  (p = 0.001 and p = 0.02 respectively) than those with BMI < 30.

Discussion and Conclusion

Using three new assays specific for different glycosylated forms of proBNP we have shown that the processing of proBNP is dysregulated in heart failure compared to controls due to increased glycosylation at threonine 71 of proBNP.  Obese patients with HF have even greater dysregulation, demonstrated by decreased concentrations of proBNP that is not glycosylated at T71 (NG-T71), and concomitant decreases in NT-proBNP.  Thus, we have shown for the first time that increased BMI is associated with increased proBNP glycosylation at T71 in patients with heart failure.  Glycosylation-induced impairment of proBNP processing explains, at least in part, the reduction in plasma concentrations of B-type cardiac natriuretic peptides observed in obesity. Using these assays to evaluate the proBNP profile of larger patient cohorts will further develop understanding of the relationships between BNP production, BMI and heart failure pathogenesis, which would be expected to lead to increased diagnostic performance.

P5719Plasma proteomics identify plaque-related proteins that predict long-term recurrent coronary events in patients with acute coronary syndrome

Background

Coronary plaque burden and composition drive recurrent ischaemic events in coronary artery disease.

Purpose

We first investigated the association between plasma proteins and coronary plaque characteristics in a cohort of asymptomatic individuals with low-intermediate Framingham Risk Score. Plaque-related proteins were further evaluated in a second cohort of patients with acute coronary syndrome (ACS) to determine their prognostic value for predicting future myocardial infarction (MI).

Methods

We profiled 1305 plasma proteins using an aptamer-based array (SOMAscan) in asymptomatic individuals who had undergone 384-slice coronary computed tomography angiography. Plaques were categorized by composition as calcified or non-calcified. First, we identified proteins that were different (based on multiple testing adjusted p-values: q-value <0.05) between 250 ACS patients who suffered a recurrent MI event on follow-up compared with another 250 ACS patients who remained event-free using Mann-Whitney U test. Next, protein candidates that also correlated (Pearson's p<0.05) with specific categories of plaque composition were evaluated using a cox proportional hazards model to determine the risk of recurrent MI, adjusting for potential confounders in the second cohort.

Results

A total of 65 and 120 plasma proteins were significantly associated with calcified and non-calcified plaques respectively in the asymptomatic cohort (N=79). Of these 185 proteins, 23 proteins were differentially expressed (DE) between ACS patients with and without recurrent MI events (median follow-up 1811 days). The top three up-and down-regulated proteins in the recurrent MI group were macrophage-capping protein, trefoil factor 3 and cystatin-SN (median FC 1.22, 1.17 and 1.17; q-value 4.34x10–6, 2.18x10–4, 3.17x10–3 respectively) and fibroblast growth factor 20, lymphotoxin a2/b1 and vascular endothelial growth factor receptor 2 (median FC 0.92, 0.94 and −0.090; q-value 1.31x10–3, 9.45x10–3 and 3.90x10–3) respectively. The quartiles of these protein concentrations were also associated with risk of recurrent MI, (log-rank test p-value range from 2.71x10–7 to 0.04). Of the DE proteins, the adjusted hazards ratio (HR) of cystatin-SN in the highest quartile (Q4) was 1.44 times that of the first quartile (Q1) (adjusted HR: 1.44, 95% CI: 0.93–2.2) and higher plasma concentration of cystatin-SN was associated with increasing risk of recurrent MI events (Trend test p=0.004). On the other hand, the highest quartile of fibroblast growth factor 20 was associated with 44% reduction in risks of recurrent MI adjusted HR: 0.56, 95% CI of HR: 0.35–0.87), with significant trend test (p=0.0096).

Conclusions

Large-scale plasma proteomics identified novel plaque-related proteins predictive of recurrent coronary events in patients with ACS. Further studies may help unravel the biological underpinnings of these circulating proteins and their potential as novel prognostic biomarkers.

Acknowledgement/Funding

This work was supported by grant NMRC/CSA-INV/0001/2016 from the National Medical Research Council, Singapore.

P4360Personalised shape models of the left ventricle from 3D echocardiography: an initial comparison to cardiac magnetic resonance imaging

Background

The heart constantly adapts to maintain cardiac output. In the longer term, this process (remodeling) can manifest as changes in ventricular volume, sphericity, and/or wall thickness, amongst several other morphological indices. Previous studies have shown the significance of remodeling in evaluations of survival, and as a determinant of the clinical course of heart failure. Yet surprisingly, diagnostic measures, typically of left ventricular (LV) mass and ejection fraction, neglect much of the shape information that is available through imaging. A recent UK Biobank study revealed that morphometric atlases show more compelling associations with cardiovascular risk factors, than do LV mass and volumes. While it has been possible to construct shape models from cardiac magnetic resonance imaging (MRI), such a framework is still under development for echocardiography (echo).

Purpose

Despite MRI being long regarded as the gold standard, it is greatly limited by high costs, long scan times and incompatibility with ferromagnetic cardiac devices. In contrast, echo has presented as a convenient alternative, whilst also offering good temporal resolution. The advancements of 3D echo now provide adequate spatial resolution and thus elicit the possibility of conducting more complex analyses on this modality. With the ability to extract LV geometry directly from 3D echo acquisitions, we sought to create dynamic, 3D patient-specific models–and subsequently compare these results to those derived from MRI.

Methods

As part of an ongoing study, 8 volunteers with no known cardiovascular problems (nor family history thereof), were recruited for non-invasive imaging. Cine MRI and 3D echo of the LV were acquired within a 2 hour session. A Siemens Avanto Fit 1.5 T MRI scanner and Siemens ACUSON SC2000 Ultrasound System with a 4Z1c Transducer were used. 3D models of the LV were generated independently from echo (EchobuildR 2.7 prototype software, Siemens Ultrasound) and MRI acquisitions (Cardiac Image Modeller v8.1), and registered to fiducial landmarks (apex, base plane, right ventricular inserts) and myocardial contours.

Results

Euclidian distances between 1682 corresponding points sampled from the surface of echo/MRI models were calculated, and used as a discrepancy measure (Figure). Across the 8 cases, we found an average root mean square deviation (RMSD) of 5.71 mm at end-systole and 6.03 mm at end-diastole. The maximum RMSD for a single model was 9.47 mm (case 8, ES).

Conclusion

We demonstrate that it is possible to create shape models from 3D echo examinations for comparison with MRI. As more cases are collected, we will devise methods to objectively quantify the mismatch that may arise between models derived from the two modalities. The establishment of such a framework would not only provide previously unavailable measures of shape and function, but in turn leverage the significantly wider clinical reach of echocardiography.

Screening for Atrial Fibrillation

Approximately 10% of ischemic strokes are associated with atrial fibrillation (AF) first diagnosed at the time of stroke. Detecting asymptomatic AF would provide an opportunity to prevent these strokes by instituting appropriate anticoagulation. The AF-SCREEN international collaboration was formed in September 2015 to promote discussion and research about AF screening as a strategy to reduce stroke and death and to provide advocacy for implementation of country-specific AF screening programs. During 2016, 60 expert members of AF-SCREEN, including physicians, nurses, allied health professionals, health economists, and patient advocates, were invited to prepare sections of a draft document. In August 2016, 51 members met in Rome to discuss the draft document and consider the key points arising from it using a Delphi process. These key points emphasize that screen-detected AF found at a single timepoint or by intermittent ECG recordings over 2 weeks is not a benign condition and, with additional stroke factors, carries sufficient risk of stroke to justify consideration of anticoagulation. With regard to the methods of mass screening, handheld ECG devices have the advantage of providing a verifiable ECG trace that guidelines require for AF diagnosis and would therefore be preferred as screening tools. Certain patient groups, such as those with recent embolic stroke of uncertain source (ESUS), require more intensive monitoring for AF. Settings for screening include various venues in both the community and the clinic, but they must be linked to a pathway for appropriate diagnosis and management for screening to be effective. It is recognized that health resources vary widely between countries and health systems, so the setting for AF screening should be both country- and health system-specific. Based on current knowledge, this white paper provides a strong case for AF screening now while recognizing that large randomized outcomes studies would be helpful to strengthen the evidence base.

Genetic markers of repolarization and arrhythmic events after acute coronary syndromes

BACKGROUND

There is a genetic contribution to the risk of ventricular arrhythmias in survivors of acute coronary syndromes (ACS). We wished to explore the role of 33 candidate single nucleotide polymorphisms (SNPs) in prolonged repolarization and sudden death in patients surviving ACS.

METHODS

A total of 2,139 patients (1680 white ethnicity) surviving an admission for ACS were enrolled in the prospective Coronary Disease Cohort Study. Extensive clinical, echocardiographic, and neurohormonal data were collected for 12 months, and clinical events were recorded for a median of 5 years. Each SNP was assessed for association with sudden cardiac death (SCD)/cardiac arrest (CA) and prolonged repolarization at 3 time-points: index admission, 1 month, and 12 months postdischarge.

RESULTS

One hundred six SCD/CA events occurred during follow-up (6.3%). Three SNPs from 3 genes (rs17779747 [KCNJ2], rs876188 [C14orf64], rs3864180 [GPC5]) were significantly associated with SCD/CA in multivariable models (after correction for multiple testing); the minor allele of rs17779747 with a decreased risk (hazard ratio [HR] 0.68 per copy of the minor allele, 95% CI 0.50-0.92, P = .012), and rs876188 and rs386418 with an increased risk (HR 1.52 [95% CI 1.10-2.09, P = .011] and HR 1.34 [95% CI 1.04-1.82, P = .023], respectively). At 12 months postdischarge, rs10494366 and rs12143842 (NOS1AP) were significant predictors of prolonged repolarization (HR 1.32 [95% CI 1.04-1.67, P = .022] and HR 1.30 [95% CI 1.01-1.66, P = .038], respectively), but not at earlier time-points.

CONCLUSION

Three SNPs were associated with SCD/CA. Repolarization time was associated with variation in the NOS1AP gene. This study demonstrates a possible role for SNPs in risk stratification for arrhythmic events after ACS.

Hyperuricaemia and gout in New Zealand rural and urban Māori and non-Māori communities

BACKGROUND

There are few current data on the prevalence of hyperuricaemia and gout in New Zealand, particularly among the indigenous Māori population.

AIMS

To determine the prevalence of gout and hyperuricaemia in rural and urban Māori and non-Māori community samples and describe the treatment and comorbidities of participants with gout.

METHODS

Participants aged 20-64 years were recruited by random selection from the electoral roll. Māori samples were selected from among those identified as being of Māori descent on the roll and who self-identified as being of Māori ethnicity at interview. Personal medical history, blood pressure, anthropometrics, fasting lipids, glucose, HbA1c and urate were recorded.

RESULTS

There were 751 participants. Mean serum urate (SU) was 0.30 mmol/L (0.06-0.69 mmol/L). Māori had a significantly higher prevalence of hyperuricaemia (SU > 0.40 mmol/L) compared with non-Māori (17.0% vs 7.5%, P = 0.0003). A total of 57 participants had a history of gout, with a higher prevalence in Māori compared with non-Māori (10.3% vs 2.3%, P < 0.0001). Of the participants, 18/57 (31.6%) with gout were receiving urate-lowering therapy, but in 38.9%, SU was >0.36 mmol/L. Participants with gout were more likely to have metabolic syndrome, diabetes, cardiac disease or hypertension.

CONCLUSIONS

Gout and hyperuricaemia were more prevalent in Māori, and participants with gout were more likely to have comorbidities. There was not a higher overall adjusted cardiovascular disease risk in Māori participants with gout. Despite the high prevalence of gout, management remains suboptimal.

Genetic variation in the renin-angiotensin-aldosterone system is associated with cardiovascular risk factors and early mortality in established coronary heart disease

This study examined renin-angiotensin-aldosterone (RAAS) system gene variants for associations with cardiovascular risk factors and outcomes in coronary heart disease. Coronary disease patients (n=1186) were genotyped for 21 single-nucleotide polymorphisms (SNPs) within angiotensinogen (AGT), angiotensin-converting enzyme (ACE), angiotensin-II type-1 receptor (AGTR1) and aldosterone synthase (CYP11B2). Associations with all-cause mortality and cardiovascular readmissions were assessed over a median of 3.0 years. The AGT M235T 'T' allele was associated with a younger age of clinical coronary disease onset (P=0.006), and the AGT rs2478545 minor allele was associated with lower circulating natriuretic peptides (P=0.0001-P=0.001) and E/E(1) (P=0.018). Minor alleles of AGT SNPs rs1926723 and rs11122576 were associated with more frequent history of renal disease (P0.04) and type-2 diabetes (P0.02), higher body mass index (P0.02) and greater mortality (P0.007). AGT rs11568054 minor allele carriers had more frequent history of renal disease (P=0.04) and higher plasma creatinine (P=0.033). AGT rs6687360 minor allele carriers exhibited worse survival (P=0.02). ACE rs4267385 was associated with older clinical coronary disease onset (P=0.008) and hypertension (P=0.013) onset, increased plasma creatinine (P=0.01), yet greater mortality (P=0.044). Less history of hypertension was observed with the AGTR1 rs12685977 minor allele (P=0.039). Genetic variation within the RAAS was associated with cardiovascular risk factors and accordingly poorer survival.

How is the risk of undernutrition associated with cardiovascular disease among individuals of advanced age?

BACKGROUND

The oldest old (85+) are the fastest growing population segment in New Zealand. Cardiovascular disease (CVD) is the main cause of death and is associated with various risk factors including risk of undernutrition.

OBJECTIVES

To determine if there is an association between CVD and nutrition risk in advanced age.

SETTING

Three North Island locations (rural and urban areas) in New Zealand.

PARTICIPANTS

108 participants aged 85 years (75-79 for Maori).

MEASUREMENTS

Comprehensive health assessments were undertaken. Clinically manifest CVD was pre-defined and ascertained from interviews and hospitalisation records. Nutrition risk was assessed using a validated questionnaire-Seniors in the Community: Risk evaluation for eating and nutrition, Version II (SCREEN II).

RESULTS

72 participants (67%) had CVD (49% men); 52% of participants had a SCREEN II score < 50. Those with CVD had lower HDL level [median(IQR)] [1.4(0.7) vs. 1.6(0.6)] (p=0.041), and higher waist circumference [97.5(19.1) vs. 89.3(20.6)] (p=0.043) compared to those without CVD. Those with CVD were at no greater nutrition risk than those without CVD (SCREEN II score: [49(7) vs. 51(10)] (p=0.365). Using logistic regression controlling for confounders, SCREEN II scores trended towards an inverse association with CVD (p=0.10).

CONCLUSION

Two thirds of the study participants had CVD and half were at risk of undernutrition. Nutrition risk was mildly associated with CVD. This study provides further evidence that those in advanced age are at risk of undernutrition. Further research is needed to establish how the causes and consequences of CVD are related to nutrition risk.

Role of echocardiographic left ventricular mass and carotid intima-media thickness in the cardiovascular risk assessment of asymptomatic patients with type 2 diabetes mellitus

BACKGROUND

Standard cardiovascular (CV) risk assessment may underestimate risk in people with type 2 diabetes mellitus (T2DM). Cardiac and vascular imaging to detect subclinical disease may augment risk prediction. This study investigated the association between CV risk, left ventricular hypertrophy (LVH) and carotid intima-media thickness (CIMT) in patients with T2DM free of CV symptoms.

METHODS

People with T2DM without known CV disease were recruited from general practice. The 5-year risk of CV events was calculated using an adjusted Framingham equation and the prevalence of LVH and abnormal CIMT across bands of CV risk assessed. In those at intermediate risk, the number needed to scan (NNS) to reclassify one person to high risk was calculated across the group and compared in those above and below 55 years. The association between LV mass and CIMT was also assessed.

RESULTS

Mean age 57 years (SD11), 51% female. Median 5-year CV risk 14.3% (interquartile range 10.3, 19.5), 51% had LVH (American Society of Echocardiography criteria) and 31% an abnormal CIMT (age and sex criteria). In the 52% at intermediate risk, 37% had LVH and 36% an abnormal CIMT. The NNS was 1.7 using both imaging techniques, 2.7 using cardiac imaging alone or 2.8 using vascular imaging alone. Almost twice as many people >55 years had an abnormal CIMT than those <55 years.

CONCLUSIONS

Cardiac and vascular imaging to detect subclinical disease can be used to augment prediction of CV risk in people with T2DM at intermediate risk. The value of reclassifying risk is as yet unproven and requires outcome data from intervention studies.