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Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers. Using magnetic resonance images of the heart and aorta from 26,893 individuals in the UK Biobank, a phenome-wide association study associates cardiovascular imaging phenotypes with a wide range of demographic, lifestyle and clinical features.

BackgroundStatistical shape atlases have been used in large-cohort studies to investigate relationships between heart shape and risk factors. The generalisability of these relationships between cohorts is unknown. The aims of this study were to compare left ventricular (LV) shapes in patients with differing cardiovascular risk factor profiles from two cohorts and to investigate whether LV shape scores generated with respect to a reference cohort can be directly used to study shape differences in another cohort.MethodsTwo cardiac MRI cohorts were included: 2106 participants (median age: 65 years, 54% women) from the Multi-Ethnic Study of Atherosclerosis (MESA) and 2960 participants (median age: 64 years, 52% women) from the UK Biobank (UKB) study. LV shape atlases were constructed from 3D LV models derived from expert-drawn contours from separate core labs. Atlases were considered generalisable for a risk factor if the area under the receiver operating characteristic curves (AUC) were not significantly different (p>0.05) between internal (within-cohort) and external (cross-cohort) cases.ResultsLV mass and volume indices were differed significantly between cohorts, even in age-matched and sex-matched cases without risk factors, partly reflecting different core lab analysis protocols. For the UKB atlas, internal and external discriminative performance were not significantly different for hypertension (AUC: 0.77 vs 0.76, p=0.37), diabetes (AUC: 0.79 vs 0.77, p=0.48), hypercholesterolaemia (AUC: 0.76 vs 0.79, p=0.38) and smoking (AUC: 0.69 vs 0.67, p=0.18). For the MESA atlas, diabetes (AUC: 0.79 vs 0.74, p=0.09) and hypercholesterolaemia (AUC: 0.75 vs 0.70, p=0.10) were not significantly different. Both atlases showed significant differences for obesity.ConclusionsThe MESA and UKB atlases demonstrated good generalisability for diabetes and hypercholesterolaemia, without requiring corrections for differences in mass and volume. Significant differences in obesity may be due to different relationships between obesity and heart shapes between cohorts.

IntroductionThe diagnosis of myocarditis is challenging and is often imprecise despite the availability of contemporary diagnostics such as cardiovascular magnetic resonance (CMR) imaging. We aimed to detect associations between baseline characteristics and CMR features in patients with acute myocarditis.MethodsCMR reports of all patients enrolled to the biorepository of a larger tertiary referral centre (Barts BioResource) were interrogated with a natural language processing algorithm to identify individuals with elevated myocardial T2 signal, indicating oedema. Cases not in keeping with a clinical definition of acute myocarditis and those with a known diagnosis of a non-inflammatory form of cardiomyopathy were excluded. Age, sex, body mass index (BMI), peak high sensitivity Troponin T, peak C-reactive protein (CRP), presenting symptoms, and baseline left ventricular ejection fraction (LVEF) on transthoracic echocardiogram (TTE) were collected. CMR outcomes included LVEF, and late gadolinium enhancement (LGE), with the extent of LGE quantified by the number of affected segments. Multivariate analyses were completed evaluating baseline variables and their association with CMR LVEF and LGE adjusting for age, sex, and time to CMR. A sensitivity analysis additionally adjusting for BMI and cardiovascular risk factors was performed. All analyses were completed in Python 3.7.ResultsCohort characteristics are summarised in table 1. A total of 127 patients with acute myocarditis were identified of whom 118(93%) had a presentation with chest pain and/or shortness of breath but only 21(18%) of this group had a preceding viral syndrome. Clinical signs of heart failure were detected in 24(21%), 43(34%) were white British, 15(12%) were Asian and 12(9%) were black. Age ranged from 17 to 75 years with a bimodal age distribution for women and 81(64%) males (mean age 36 years) (figure 1). Left ventricular LGE was found in 118(93%) patients with a biventricular LGE pattern in 5(4%) and a non-ischaemic LGE pattern in 115(98%). In the multivariate analyses, the highest tertile of peak troponin was associated with higher LGE (beta=1.90, 95% CI 0.20 to 3.59 segment and P = 2.9 x 10–2) reflecting more segments with LGE and a lower LVEF (beta=-6.96, 95% CI -12.78 to – 1.15% and P = 0.02) (table 2). Adjusting for BMI and cardiovascular risk factors to the model minimally attenuated effect sizes although confidence intervals were much wider due to the limited sample size.ConclusionsIn this cohort of patients with acutely presenting myocardial inflammation, the severity of myocardial injury indicated by peak troponin was independently associated with markers of adverse CMR outcomes manifested by a greater extent of LGE and lower LVEF, highlighting its prognostic role in the recovery from acute myocarditis. The heterogeneity in clinical presentation and character of injury also underscores the need to stratify these patients and to better understand long-term outcomes.Abstract 14 Table 1Myocarditis cohort characteristics Parameters Proportion (%) Sex Male 64 Female 36 Smoking status Never 54 Previous 13 Current 34 Hypertension Yes 22 No 78 Dyslipidaemia Yes 18 No 82 Diabetes Mellitus Yes 12 No 88 Mean (SD) Peak CRP (mg/L) 76 (104) Peak troponin(ng/L) 1264 (1444) TTE LVEF (%) 50 (10) CMR LVEF (%) 58 (11) LV segments with LGE 4 (3) SD – standard deviation, CRP- C-reactive protein, TTE- transthoracic echocardiogram, CMR – cardiovascular magnetic resonance imaging, LV- left ventricle, LGE – late gadolinium enhancement.Abstract 14 Table 2Multivariate analyses -Troponin, LVEF and LGE extent LVEF LGE Covariates Beta (95% CI) P value Beta (95% CI) P value Troponin (2nd tertile vs 1st tertile) -0.75 (-6.60 to 5.12) 0.80 0.18 (-1.53 to 1.89) 0.84 Troponin (3rd tertile vs 1st tertile) -6.96 (-12.78 to -1.15) 0.02 1.90 (0.20 to 3.59) 2.9 x 10-2 Age 3.61 x 10- 2 (-0.21to 0.14) 0.68 2.41 x 10-2 (-2.6 x 10-2 to 7.5 x 10-2) 0.35 Sex -3.09 (-8.80 to 2.62) 0.29 2.38 (0.72 to 4.05) 6 x 10-3 Time to CMR 8.69 x 10-2(-7.6 x10-2 to 0.25) 0.29 3.52 x 10-2(-8.3 x 10-2 to 1.2 x 10-2) 0.14 LVEF- left ventricular ejection fraction. LGE – late gadolinium enhancement. CI – confidence interval. CMR -cardiovascular magnetic resonance imaging. Values given to 2 decimal places.Abstract 14 Figure 1Distribution of age by sex in acute myocarditisConflict of InterestNone

Background Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Methods Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). Results By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. Conclusions We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.

Cardiovascular magnetic resonance (CMR) is the gold standard method for the assessment of cardiac structure and function. Reference ranges permit differentiation between normal and pathological states. To date, this study is the largest to provide CMR specific reference ranges for left ventricular, right ventricular, left atrial and right atrial structure and function derived from truly healthy Caucasian adults aged 45–74. Five thousand sixty-five UK Biobank participants underwent CMR using steady-state free precession imaging at 1.5 Tesla. Manual analysis was performed for all four cardiac chambers. Participants with non-Caucasian ethnicity, known cardiovascular disease and other conditions known to affect cardiac chamber size and function were excluded. Remaining participants formed the healthy reference cohort; reference ranges were calculated and were stratified by gender and age (45–54, 55–64, 65–74). After applying exclusion criteria, 804 (16.2%) participants were available for analysis. Left ventricular (LV) volumes were larger in males compared to females for absolute and indexed values. With advancing age, LV volumes were mostly smaller in both sexes. LV ejection fraction was significantly greater in females compared to males (mean ± standard deviation [SD] of 61 ± 5% vs 58 ± 5%) and remained static with age for both genders. In older age groups, LV mass was lower in men, but remained virtually unchanged in women. LV mass was significantly higher in males compared to females (mean ± SD of 53 ± 9 g/m2 vs 42 ± 7 g/m2). Right ventricular (RV) volumes were significantly larger in males compared to females for absolute and indexed values and were smaller with advancing age. RV ejection fraction was higher with increasing age in females only. Left atrial (LA) maximal volume and stroke volume were significantly larger in males compared to females for absolute values but not for indexed values. LA ejection fraction was similar for both sexes. Right atrial (RA) maximal volume was significantly larger in males for both absolute and indexed values, while RA ejection fraction was significantly higher in females. We describe age- and sex-specific reference ranges for the left ventricle, right ventricle and atria in the largest validated normal Caucasian population.

BackgroundThe impact of early-stage chronic kidney disease (CKD) on cardiovascular outcomes, particularly when albuminuria is present, remains unclear. This study examined the associations between early CKD (stages 1 and 2) with and without albuminuria and the incidence of major adverse cardiovascular events (MACEs), heart failure (HF) and all-cause mortality.MethodsA cohort of 456 015 participants from the UK Biobank was categorised by CKD stage using serum creatinine to calculate estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (≥3 mg/mmol) to define albuminuria. Multivariable Cox proportional hazard models were applied to evaluate the associations between CKD stages and cardiovascular outcomes. Additionally, left ventricular mass (LVM), an intermediate cardiovascular risk marker, was assessed in a subset of participants using cardiovascular MRI.ResultsCompared with normal kidney function, the risk of adverse outcomes increased progressively with advancing CKD stages, except for stage 2 CKD without albuminuria. Stage 2 CKD with albuminuria was associated with higher risks of MACE (HR 1.32, 95% CI 1.25 to 1.38), HF (HR 1.79, 95% CI 1.67 to 1.92) and all-cause mortality (HR 1.51, 95% CI 1.44 to 1.58), comparable to stage 3A CKD without albuminuria. The presence of albuminuria significantly interacted with the relationships between CKD stages and outcomes. No significant differences in indexed LVM were observed between early-stage CKD with albuminuria and normal renal function.ConclusionsIn early-stage CKD, albuminuria is independently associated with increased risks of MACE, HF and mortality. These findings support the use of albuminuria over eGFR decline alone for cardiovascular risk stratification in early CKD.

Arterial stiffness index (ASI) is a non-invasive measure of arterial stiffness using infra-red finger sensors (photoplethysmography). It is a well-suited measure for large populations as it is relatively inexpensive to perform, and data can be acquired within seconds. These features raise interest in using ASI as a tool to estimate cardiovascular disease risk as prior work demonstrates increased arterial stiffness is associated with elevated systolic blood pressure, and ASI is predictive of cardiovascular disease and mortality. We conducted genome-wide association studies (GWASs) for ASI in 127,121 UK Biobank participants of European-ancestry. Our primary analyses identified variants at four loci reaching genome-wide significance ( P  < 5 × 10 −8 ): TEX41 (rs1006923; P  = 5.3 × 10 −12 ), FOXO1 (rs7331212; P  = 2.2 × 10 −11 ), C1orf21 (rs1930290, P  = 1.1 × 10 −8 ) and MRVI1 (rs10840457, P  = 3.4 × 10 −8 ). Gene-based testing revealed three significant genes, the most significant gene was COL4A2 ( P  = 1.41 × 10 −8 ) encoding type IV collagen. Other candidate genes at associated loci were also involved in smooth muscle tone regulation. Our findings provide new information for understanding the development of arterial stiffness.

BackgroundRight atrial pressure (RAP) corresponds to fluid status and preload and is also important in prognostication for patients with heart failure and pulmonary hypertension. RAP can be measured invasively or non-invasively, but cannot currently be estimated by cardiac MRI (CMR). This study used paired CMR and invasive right heart catheter (RHC) measurements to develop a model to predict RAP from CMR.MethodsThe ASPIRE registry consists of patients referred for assessment of dyspnoea to Sheffield Teaching Hospitals between 2012 and 2020. Inclusion criteria were age >18 years, signs and symptoms of heart failure and adequate CMR image quality. Patients diagnosed with pulmonary arterial hypertension were excluded. RHC and CMR were performed in the same 24 hour period.CMR was performed with a 1.5 T GE HDx scanner. Chamber dimensions, strain, ejection fraction and stroke volume were acquired from 2- and 4- chamber cine-images using operator reviewed AI contours in MASS research software (Fig 1).Associations between invasive mean RAP (mRAP) and CMR metrics were assessed with Pearson’s product-moment correlation coefficient. Stepwise multiple linear regression was used to develop models to predict mRAP. These were compared with receiver-operator curve analysis and DeLong’s test.ResultsThe cohort was made up of 672 patients, divided based upon invasive mRAP ≤ 8 mmHg (44%) and mRAP > 8 mmHg (56%) (table 1). Those with higher mRAP tended to be older, more likely to be male and have a higher diastolic blood pressure but there was no difference in rates of different types of heart failure (table 1).Metrics with the strongest correlation to invasive mRAP were right atrial (RA) dimensions, strain and ejection fraction with moderate correlation to right ventricular (RV) measurements (table 2). Four multivariable models were developed. Model 1 contained RA dimensions. RA strain was added to this to create Model 2. Model 3 contained RA and RV dimensions whilst model 4 incorporated RA end systolic volume (RAESV), the single strongest CMR predictor, and adjusted it for body mass index and sex.All four models had similar predictive capability (Fig 2 Panel A). Model 1 identified RAESV as the only variable required for mRAP prediction (coefficient = 0.06, p < 0.001) and was therefore preferred for simplicity. Using a threshold of mRAP >8 mmHg ROC analysis demonstrated an area under the curve of 0.78 (95% Confidence interval 0.75 to 0.81) (Fig 2 Panel B).ConclusionsmRAP can be estimated with moderate confidence from CMR RAESV. Further studies are required to validate this model externally, including larger cohorts with reduced ejection fraction, and determine its clinical significance. Alternative clinical and MRI data could be explored to further enhance the models predictive capability. Application of this technique could enhance the role of CMR in non-invasive haemodynamic assessment and a wide range of cardiovascular pathophysiology.Abstract 179 Table 1Study demographics of the cohort (n=672). HFpEF = Heart Failure with preserved ejection fraction, HFmrEF = Heart Failure with moderately reduced ejection fraction, HFrEF = Heart Failure with reduced ejection fraction mRAP ≤ 8 mmHg mRAP > 8 mmHg P-value N (%) 295 (44%) 377 (56%) Age (years) 64±14 68±12 <0.01 Male sex (%) 109 (37%) 174 (46%) 0.02 HFpEF (%) 150 (51%) 195 (52%) 0.82 HFmrEF (%) 7 (2.4%) 18 (5%) 0.10 HFrEF (%) 5 (1.7%) 10 (3%) 0.40 Heart rate (bpm) 72±15 70±16 0.05 Systolic blood pressure (mmHg) 142±25 143±27 0.52 Diastolic blood pressure (mmHg) 76±11 79±13 0.01 Abstract 179 Table 2. Pearson correlation of several CMR indices with invasive mean right atrial pressure (mmHg) RA = Right Atrial, RV = Right Ventricular Correlation coefficient P-value RA End Diastolic Volume (ml) 0.553 <0.01 RA End Systolic Volume (ml) 0.579 <0.01 RA Ejection Fraction (%) -0.545 <0.01 RA Stroke Volume (ml) 0.085 0.03 RA peak strain (%) 0.511 <0.01 RV End Diastolic Volume (ml) 0.442 <0.01 RV End Systolic Volume (ml) 0.416 <0.01 RV Stroke Volume (ml) 0.257 <0.01 RV Ejection Fraction (%) -0.223 <0.01 Abstract 179 Figure 1Two case examples. Panel a | Normal invasive right atrial pressure (7 mmHg). Panel b | A patient with a dilated right atrium and significantly compromised right atrial function (volume curves are flattened) with raised invasive mRAP (10 mmHg)Abstract 179 Figure 2Multivariable models. Panel A | Four physiological models to estimate invasive mRAP. Model 1 only incorporates RA end systolic volume, Model 2 incorporates RA End systolic volume and peak strain, whereas Model 3 incorporates RA end systolic volume and RV volumes. Model 4 includes RA end systolic volume, body mass index and sex. There is no significant difference in diagnostic power using any model over Model 1. Panel B | Model 1 ROC with 95% confidence interval presented in light blue with an mRAP threshold of 8 mmHgConflict of InterestNone

Coronavirus disease 2019 (COVID-19) pandemic has had a great impact on every aspect of society. All countries launched preventive measures such as quarantine, lockdown, and physical distancing to control the disease spread. These restrictions might effect on daily life and mental health. This study aimed to assess the prevalence and associated factors of depressive symptoms in patients with COVID-19 at the Treatment Center. A cross-sectional telephone survey was carried out at Hmawbi COVID-19 Treatment Center, Myanmar from December 2020 to January 2021. A total of 142 patients with COVID-19 who met the criteria were invited to participate in the study. A pre-tested Center for Epidemiologic Studies Depression Scale (CES-D) was used as a tool for depressive symptoms assessment. Data were analyzed by using binary logistic regression to identify associated factors of depressive symptoms. Adjusted odds ratio (AOR) with a 95% confidence interval (CI) was computed to determine the level of significance with a p < 0.05. The prevalence of depressive symptoms in patients with COVID-19 was 38.7%, with the means (± standard deviation, SD) subscale of somatic symptom, negative effect, and anhedonia were 4.64 (±2.53), 2.51 (± 2.12), and 5.01 (± 3.26), respectively. The patients with 40 years and older (AOR: 2.99, 95% CI: 1.36-6.59), < 4 of household size (AOR: 3.45, 95% CI: 1.46-8.15), [less than or equal to] 400,000 kyats of monthly family income (AOR: 2.38, 95% CI: 1.02-5.54) and infection to family members (AOR: 4.18, 95% CI: 1.74-10.07) were significant associated factors of depressive symptoms. The high prevalence of depressive symptoms, approximately 40%, was found in patients with COVID-19 in the Treatment Center. Establishments of psychosocial supports, providing psychoeducation, enhancing the social contact with family and friends, and using credible source of information related COVID-19 would be integral parts of mental health services in COVID-19 pandemic situation.

Background The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. Methods To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. Results We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.