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Arterial involvement is the cardinal feature of large-vessel vasculitis (LVV) and prevention of disease progression is the principal therapeutic goal. However, development of tools for its evaluation represents a major unmet need. To address this, a widely-applicable imaging tool for LVV, analysing arterial involvement in 17 arterial territories, has been developed and validated. Individual stenosis and dilation scores were generated and combined in a composite score. The methodology was validated cross-sectionally and longitudinally in 131 patients, 96 Takayasu arteritis (TA), 35 large-vessel giant-cell arteritis (LV-GCA). In total, 4420 arterial segments from 260 imaging studies were evaluated. The new scores allowed quantitative grading of LVV arterial involvement with high consistency, revealing inter-patient differences. TA had higher stenosis and composite scores and lower dilation scores than LV-GCA. Baseline stenotic and composite scores reflected arterial damage rather than disease-activity. Longitudinal changes in all three scores correlated with disease activity and mirrored arterial disease evolution, reflecting both progressive injury and lesion improvement. Increases ≥1 in any score were specific for arterial disease progression. The scores objectively quantify arterial involvement in LVV, providing precise definition of disease phenotype and evolution. We propose that they represent novel vascular outcome measures essential for future clinical trials.

Background Cardiovascular disease (CVD) and non-small cell lung cancer (NSCLC) are the global leading causes of overall and cancer-related deaths, respectively. NSCLC patients have a higher CVD risk than the general population which is frequently underdiagnosed. Coronary artery calcification (CAC), a marker of CVD, is commonly detected on routinely acquired CT from NSCLC work-up but often not reported. We present an automated CAC assessment tool validated for NSCLC patients using a deep learning-based framework to provide a non-invasive CVD screening opportunity without incurring extra workload or radiation exposure. Methods We trained nnU-Net models on ungated, unenhanced chest CTs ( n  = 97) from Stanford AIMI dataset, and tested them on three mutually independent datasets: (1) ungated unenhanced CTs from AIMI ( n  = 95), (2) attenuation correction CTs from PET-CT scans of NSCLC patients at our institution (ICHNT, n  = 87; age 67.8 ± 10.1 years; M:F 174:113), and (3) CAC-negative scans from TCIA ( n  = 50); and used the best performing model to produce CAC segmentations, post-processed with TotalSegmentator, to stratify patients into CVD risk groups, informing the need for dedicated cardiac clinic assessment. Results For a CAC threshold of 100, the model achieved accuracy: 83.6%, sensitivity: 91.9%, specificity: 70.8%, positive predictive value (PPV): 82.9%, negative predictive value (NPV): 85.1%, F1-score: 0.87, kappa coefficient: 0.65 and Area Under the Receiver Operating Characteristic Curve (AUC) score of 0.899. For a threshold of 400, accuracy: 84.5%, sensitivity: 90.9%, specificity: 79.5%, PPV: 77.6%, NPV: 91.8%, F1-score: 0.84, and kappa coefficient: 0.69 as well as an AUC of 0.926. Conclusion Our optimised deep learning model can benefit NSCLC patients by providing CVD risk information from their routine CT scans which may not acted upon otherwise, thus enabling a practical opportunistic screening solution for these patients.

Gender differences exist in outcomes after percutaneous coronary intervention and coronary artery bypass graft surgery but have yet to be fully explored after transcatheter aortic valve implantation. We aimed to investigate gender differences after transcatheter aortic valve implantation in the UK National Institute for Cardiovascular Outcomes Research registry. A retrospective analysis was performed of Medtronic CoreValve and Edwards SAPIEN implantation in 1,627 patients (756 women) from January 2007 to December 2010. Men had more risk factors: poor left ventricular systolic function (11.9% vs 5.5%, p <0.001), 3-vessel disease (19.4% vs 9.2%, p <0.001), previous myocardial infarction (29.5% vs 13.0%, p <0.001), peripheral vascular disease (32.4% vs 23.3%, p <0.001), and higher logistic EuroSCORE (21.8 ± 14.2% vs 21.0 ± 13.4%, p = 0.046). Thirty-day mortality was 6.3% (confidence interval 4.3% to 7.9%) in women and 7.4% (5.6% to 9.2%) in men and at 1 year, 21.9% (18.7% to 25.1%) and 22.4% (19.4% to 25.4%), respectively. There was no mortality difference: p = 0.331 by log-rank test; hazard ratio for women 0.91 (0.75 to 1.10). Procedural success (96.6% in women vs 96.4% in men, p = 0.889) and 30-day cerebrovascular event rates (3.8% vs 3.7%, p = 0.962) did not differ. Women had more major vascular complications (7.5% vs 4.2%, p = 0.004) and less moderate or severe postprocedural aortic regurgitation (7.5% vs 12.5%, p = 0.001). In conclusion, despite a higher risk profile in men, there was no gender-related mortality difference; however, women had more major vascular complications and less postprocedural moderate or severe aortic regurgitation.

ObjectivesTo identify the optimal threshold values of CT-AVCS (CT-Aortic valve calcium score) and CT-AVA (CT-aortic valve area) for diagnosing severe AS (aortic stenosis) in patients referred for TAVI (Transcatheter Aortic valve Implantation CT) against echocardiographic mean gradient (E-MG).Methods1150 consecutive patients from three centres who underwent TAVI-CT systolic phase ECG-gating were identified. Patients with previous aortic valve surgery or TAVI, aortic, or mitral regurgitation were excluded. Patients with LVEF (left ventricular ejection fraction) ≥ 50% and normal indexed stroke volume (28–48 ml/m2) were selected. CT-AVA was obtained by direct planimetry. ROC curves were used to identify optimal thresholds of AVCS and CT-AVA for predicting stenosis severity against E-MG ≥40 mmHg using a combination of sensitivity and specificity.ResultsA total of 428 patients (54% women) with a mean age of 81 years (±7.5) fulfilled the inclusion criteria (Table 1). Severe AS was present in 239 (56%) by E-MG ≥40 mmHg. In men, the optimal threshold for AVCS and CT-AVA were ≥3046 (AUC 0.78) and ≤0.97 cm2 (AUV 0.69) respectively (Table 2). In women, the thresholds were ≥2034 (AUC 0.79) and ≤0.90 cm2 (AUC, 0.71) respectively. The combined AUC of both parameters was 0.79.ConclusionThis study defines the optimal thresholds for AVCS and CT-AVA for severe stenosis in TAVI patient cohort. The AVCS thresholds are much higher than previously reported.

ObjectivesTo explore the prevalence and discrepancy in the diagnosis of severe AS (aortic stenosis) by echocardiography in patients referred for TAVI-CT (Transcatheter Aortic valve Implantation CT) and accuracy of CT-AVCS (CT-Aortic valve calcium score) and CT-AVA (CT-aortic valve area) compared to echocardiographic mean gradient (MG) in patients with normal function and flow.Methods1150 patients from three centres who underwent TAVI-CT systolic phase ECG-gating were identified. Patients with previous aortic valve surgery, aortic, or mitral regurgitation were excluded. Patients with left ventricular ejection fraction ≥ 50% and normal indexed stroke volume (28–48 ml/m2) were selected. CT-AVA was obtained by direct planimetry. Accuracy of echo and CT parameters for predicting stenosis severity against MG ≥40 mmHg was determined.ResultsA total of 428 patients (54% women) with a mean age of 81 years (±7.5) fulfilled the inclusion criteria (Table 1). Severe AS by EVA (Echo valve area) ≤1 cm2, peak velocity ≥4 m2, and MG ≥40 mmHg, was present in 95%, 56%, and 56% patients respectively. Severe AS defined by AVCS ≥3000 in men, AVCS ≥2000 in women, and CT-AVA ≤1 cm2 was present in 53%, 59%, and 59% with AUC of AVCS and CT-AVA being (AUC, 0.72–0.74) and (0.69) respectively.ConclusionMost patients referred for TAVI have severe AS defined by EVA with high discrepancy compared to MG. CT-AVCS and CT-AVA are flow-independent parameters with least discrepancy with MG.

Progressive heart failure due to remodeling is a major cause of morbidity and mortality following myocardial infarction. Conventional clinical imaging measures global volume changes, and currently there is no means of assessing regional myocardial dilatation in relation to ischemic burden. Here we use 3D co-registration of Cardiovascular Magnetic Resonance (CMR) images to assess the long-term effects of ischemia-reperfusion injury on left ventricular structure after acute ST-elevation myocardial infarction (STEMI). Forty six patients (age range 33–77 years) underwent CMR imaging within 7 days following primary percutaneous coronary intervention (PPCI) for acute STEMI with follow-up at one year. Functional cine imaging and Late Gadolinium Enhancement (LGE) were segmented and co-registered. Local left ventricular wall dilatation was assessed by using intensity-based similarities to track the structural changes in the heart between baseline and follow-up. Results are expressed as means, standard errors and 95% confidence interval (CI) of the difference. Local left ventricular remodeling within infarcted myocardium was greater than in non-infarcted myocardium (1.6% ± 1.0 vs 0.3% ± 0.9, 95% CI: -2.4% – -0.2%, P = 0.02). One-way ANOVA revealed that transmural infarct thickness had a significant effect on the degree of local remodeling at one year (P < 0.0001) with greatest wall dilatation observed when infarct transmurality exceeded 50%. Infarct remodeling was more severe when microvascular obstruction (MVO) was present (3.8% ± 1.3 vs −1.6% ± 1.4, 95% CI: -9.1% – -1.5%, P = 0.007) and when end-diastolic volume had increased by >20% (4.8% ± 1.4 vs −0.15% ± 1.2, 95% CI: -8.9% – -0.9%, P = 0.017). The severity of ischemic injury has a significant effect on local ventricular wall remodeling with only modest dilatation observed within non-ischemic myocardium. Limitation of chronic remodeling may therefore depend on therapies directed at modulating ischemia-reperfusion injury. CMR co-registration has potential for assessing dynamic changes in ventricular structure in relation to therapeutic interventions.

BackgroundSome patients are found to have myocardial scarring after infection with coronavirus-2019 disease (COVID-19) as evidenced by the presence of late gadolinium enhancement on cardiac MRI (CMR). In many types of heart disease, the presence of late gadolinium enhancement (even without symptoms) is associated with a poorer prognosis. However, it is not known whether the presence of scar after COVID-19 is associated with outcome.PurposeThis study explores the association between late gadolinium enhancement (LGE) in recovered patients with COVID-19 and of longer-term clinical outcomes.MethodsIn this single-centre, retrospective, observational cohort study, troponin levels, CMR data, and follow-up outcomes of 169 patients with COVID-19 were collected. The primary outcome was all-cause mortality. The secondary outcome was a composite of myocardial infarction, stroke, and admission for heart failure. The log-rank test was used to compare survival of those with and without late gadolinium enhancement.ResultsA total of 169 patients (mean age 61 ± 16 years, 54% male) were included. Scans were typically performed in patients with a raised troponin or ongoing cardiac symptoms post COVID-19 infection. The median (IQR) time between COVID-19 diagnosis and CMR was 14 (7-23) weeks. Median follow-up was 28 (IQR 23 to 37) months. Troponin was performed in 92% (n=155) of patients. Of those, 78% (n=121) had a positive result.Late gadolinium enhancement was present in 54 patients (32%). There were no observed differences in comorbidities such as hypertension, known ischaemic heart disease or heart failure, diabetes, obesity, or frailty between in patients with LGE compared to those without LGE. Kaplan-Meier survival analysis showed no significant difference in all cause mortality for patients with COVID-19 with LGE compared to those without LGE (Log-rank P = 0.36). Similarly, the composite secondary endpoint was not significantly different (Log-rank P = 0.48).ConclusionIn this single centre study, patients found to have myocardial LGE after COVID-19 did not have an increased risk of mortality or cardiovascular events compared to those without. The overall event rates remain low even after 2-3 years of follow-up. The cause of this is unclear but underlines the importance of larger multicentre studies to follow-up COVID-19 survivors.Abstract 182 Figure 1Cardiovascular events in COVID-19 Survivors by LGE StatusAbstract 182 Figure 2All-cause mortality in COVID-19 Survivors by LGE StatusConflict of InterestNone

Pulmonary arterial dilatation associated with pulmonary hypertension may result in significant compression of local structures. Left main coronary artery and left recurrent laryngeal nerve compression have been described. Tracheobronchial compression from pulmonary arterial dilatation is rare in adults, and there are no reports in the literature of its occurrence in idiopathic pulmonary arterial hypertension. Compression in infants with congenital heart disease has been well described. We report 2 cases of tracheobronchial compression: first, an adult patient with idiopathic pulmonary arterial hypertension who presents with symptomatic left main bronchus compression, and second, an adult patient with Eisenmenger ventricular septal defect and right-sided aortic arch, with progressive intermedius and right middle lobe bronchi compression in association with enlarged pulmonary arteries.

IntroductionCardiac magnetic resonance imaging (CMR) evaluates aortic stenosis (AS) and concurrent myocardial abnormalities. Initial diagnosis of aortic valve (AV) disease can occur during CMR scanning, but universal multiparametric AS assessment is inefficient. An automated strategy for identifying AS patients from the 3-chamber view (part of all protocols) is therefore valuable. We observed that in patients with AS, there was reduced blood signal intensity in the ascending aorta compared to the cardiac chambers. We investigated the diagnostic potential of quantifying this signal reduction as a radiomic marker for AS severity. We introduce an AI-based solution to enhance this metric’s precision and reproducibility.Materials and MethodsThis was a multi-centre, retrospective cohort study with 249 patients. Signal intensity was quantified within a 1cm2 region of interest (ROI) in the ascending aorta (Ao), left ventricle (LV), and left atrium (LA) (figure 1). Normalised Ao:LV ratio was calculated and compared against echocardiographic gold standards of AS severity: e.g., dimensionless index (DI) and AV maximum velocity, utilising Pearson correlation coefficients. Automated analysis was conducted using a point-tracking algorithm.Abstract 18 Figure 1Example of a 3-chamber cine in a patient with aortic stenosis. A region of interest (ROI) in the aorta, left ventricle and left atrium is manually measured in the end-systolic cardiac phase. The ratio of Ao:LV and Ao:LA bSSFP blood signal in this patient 97/163 = 0.59; 97/226 = 0.43 respectivelyResultsThe cohort (n = 249, median age 67 [IQR 58–77], 63% male) was stratified into no AS (n=87), mild AS (n=52), moderate AS (n=53) or severe AS (n=57) based on gold-standard echocardiography. The Ao:LV signal ratio strongly correlated with echocardiography parameters (figure 2A-B). A ratio of <0.87 exhibited 87% sensitivity and 83% specificity in identifying aortic stenosis of any severity (figure 2D).Abstract 18 Figure 2A-B: Scatterplots with Pearson correlation coefficients comparing Ao:LV and echo measures of peak velocity and the dimensionless index; C: Dot plots of Ao:LV ratios according to aortic stenosis severity with P values for Wilcoxon rank sum test; and D: ROC curve showing the diagnostic accuracy for the Ao:LV ratio in predicting aortic stenosis of any severityAbstract 18 Figure 3Example of four cases with varying degrees of aortic stenosis in the 3-chamber view with their corresponding Ao:LV ROI annotationsAbstract 18 Figure 4Proof-of-concept artificial intelligence-based automatic computation of Ao:LV ratio for every frame in the 3-chamber cine. The lowest value, corresponding to the end-systolic phase, will be taken to predict aortic stenosis severityDiscussionThe Ao:LV ratio is a novel, automatable radiomic marker with good correlation to echocardiographic parameters for AS severity assessment. Derived from routine 3CH bSSFP cines, it provides clinical information and could optimise clinical workflow by eliminating the need for unnecessary imaging sequences. Its multi-vendor compatibility allows generalizability across various scanning technologies. The algorithmic framework is designed for machine learning adaptation, offering the potential for real-time analysis.ConclusionThe Ao:LV ratio is a robust, simple tool for the initial diagnosis and severity assessment of AS available in a standard clinical protocol. This study demonstrates feasibility of automated Ao:LV computation which could enhance efficiency by targeting the appropriate use of patient-specific AV sequences.AcknowledgementsKV is funded by the UK research and Innovation [UKRI Centre for Doctoral Training in AI for Healthcare grant number EP/S023283/1]. JH is funded by the British Heart Foundation [FS/ICRF/22/26039]. GDC is supported by the National Institute of Health Research (NIHR) Imperial Biomedical Research Centre (BRC).

Meeting abstractsEMPTY Summary Our series of 5 cases of histologically-proven Giant cell myocarditis with concurrent CMR shows a pattern of late gadolinium enhancement which tends to be widespread involving all layers of the myocardium. Figure 1 shows (a) the widespread mid-wall LGE in the left ventricle on CMR, (b) lateral wall fibrosis on the explant specimen (H&E, x20), (c) Giant cell myocarditis in pre-transplant diagnostic endomyocardial biopsy (H&E, x200) of Patient No: 4 who had CMR and who subsequently underwent cardiac transplantation. shows (a) the widespread mid-wall LGE in the left ventricle on CMR, (b) lateral wall fibrosis on the explant specimen (H&E, x20), (c) Giant cell myocarditis in pre-transplant diagnostic endomyocardial biopsy (H&E, x200) of Patient No: 4 who had CMR and who subsequently underwent cardiac transplantation. [figure omitted; refer to PDF]