Ye, Z

Abstract Background Aortic valve calcification (AVC), as measured by gold-standard computed tomography (CT) Agatston score, provides an anatomic assessment of aortic stenosis (AS) severity and is a key predictor of AS progression and need for valve replacement. AVC detection and quantification from transthoracic echocardiography (TTE) could expand AS early diagnosis and risk stratification, currently limited by CT availability and radiation exposure. Methods A multi-view video-based deep learning framework was developed using 1166 TTE aortic valve videos from 187 TTE studies acquired in 110 patients with available AVC score by CT. EchoAVC architecture includes a feature extraction model followed by a quality-aware model that aggregates video-level information to obtain patient-level predictions for AVC detection and quantification (score). The framework was validated internally with 173 TTE studies from 86 patients across seven centres, and externally using 430 TTE studies from 280 patients across four different centres. The associations between EchoAVC estimations and AS severity, progression, and need for aortic valve replacement were examined. Results EchoAVC demonstrated excellent performance in AVC detection (AUROC 0.98, accuracy 94.4%), and quantification (R = 0.64) in the external multi-centre testing set. EchoAVC score was correlated with echocardiographic AS severity descriptors, including aortic valve mean pressure gradient (ρ = 0.749) and peak velocity (ρ = 0.757), and predicted future increase in mean pressure gradient (ρ = 0.382), peak velocity (ρ = 0.433) and calcium score by CT (ρ = 0.650). In 361 patients followed for a median of 3.8 years, 139 underwent aortic valve replacement. Baseline presence and extent of AVC as predicted by EchoAVC showed strong risk-stratification power for aortic valve replacement, remarkably in line with those obtained by CT, and incremental over TTE AS descriptors. EchoAVC was further tested in routine clinical practice images, confirming strong associations with AS severity and progression, including stratification for incident AS in previously unaffected individuals (p<0.001). Conclusions EchoAVC enables accurate and non-invasive detection and quantification of AVC, offering substantial diagnostic and prognostic value for aortic stenosis progression and need for valve replacement. This technique holds promise as a scalable tool for early detection and clinical management of aortic valve stenosis. Clinical Perspective What Is New? * Aortic valve calcification can be detected and quantified on standard, two-dimensional transthoracic echocardiography * EchoAVC score is associated with echocardiographic metrics of aortic stenosis severity * EchoAVC predicts progressive increase in aortic stenosis severity and need for aortic valve replacement What Are the Clinical Implications? * This openly available deep learning framework can be used to estimate aortic valve calcium in patients at risk of or presenting aortic stenosis to predict aortic stenosis progression * EchoAVC may help identify patients likely to have high aortic valve calcium and prioritize them for confirmatory CT assessment, thereby supporting earlier identification of calcific aortic valve disease in clinical pathways.

Abstract Background The relative contribution of calcific versus noncalcific aortic valve tissue volume to aortic stenosis (AS) in bicuspid aortic valve (BAV) is unclear. Purpose We aimed to assess the association of aortic valve tissue volume quantified by contrast-enhanced CT with echocardiographic AS severity, and diagnostic implication of noncalcific tissue volume for severe AS. Methods We retrospectively identified 234 patients with BAV who had echocardiographic AS severity and contrast-enhanced CT evaluation within six months and left ventricular ejection ≥50%, including 150 severe AS (aortic valve area≤1 cm2 or indexed valve area≤0.6cm2/m2 plus mean grdient≥40mmHg or peak velocity≥4 m/s), 84 nonsevere AS (61 with at least mild AS: peak veloicty≥2.0 m/s plus mean gradient ≥10mmHg, and 23 with normal aortic valve function). Contrast-enhanced CT aortic valve calcific volume and noncalcific (fibrosis) volume were quantified using patient-specific Hounsfield unit thresholds (Figure). Noncontrast CT aortic valve calcium score (AVCscore) was quantified by the Agaston method. CT measurements were indexed to echocardiographic aortic valve annulus area and square-root transformed for the analysis. Results Of 234 patients, the mean age was 64±11 years, and 64% were men. There were positive correlations of indexed calcific volume, noncalcific volume, and inverse correlations of percentages of noncalcific volume (noncalcific volume divided by the sum of calcific and noncalcific volume) with peak velocity and mean gradient (|r|=0.30 to 0.68). Among 150 patients with severe AS, 28 had an AVCscore below the severe AVC threshold (1200 AU in women and 2000 AU in men) while exhibited a higher percentage of noncalcific volume compared to 122 patients with severe AVCscore (67±15% vs. 39±15%, p&lt;0.001). After excluding 122 patients with severe AS and severe AVCscore, the indexed noncalcific volume was associated with severe AS (β±S.E: 0.18±0.07, p=0.005) with an area under the curve of 0.71; in contrast, AVCscore was not (p=0.55). Conclusion Both calcific and noncalcific aortic valve tissue volume contribute to AS severity in patients with BAV. Notably, noncalcific tissue volume is a key factor in determining severe AS in patients without severe AVCscore, suggesting its potential diagnostic value.Figure

Abstract Background Little is known about clinical outcomes in patients with bicuspid aortic valve (BAV) and ascending aortic diameters ≥50 mm where the elective surgical-indication zone begins. Purpose To assess incident aortic dissection (AoD), post-surgical survival and guideline implementation. Methods Multi-center retrospective study of BAV patients (≥18 years) with maximal root or tubular-ascending aortic diameters ≥50mm detected by transthoracic echocardiography at baseline. Ascertainment of aortic surgery, AoD and death was carried-out at their respective institutions. The primary outcome was AoD: “confirmed” AoD by surgery or death certificate, or “possible” AoD defined as sudden cardiac death of unknown cause by death certificate. Secondary outcomes were aortic surgery, post-surgical survival and guideline implementation. Results We included 506 consecutive BAV patients, mean age 61±14 years, 83% men, mean maximal aortic diameter 52±2 mm at baseline, ascending aorta was the most common segment ≥50mm (85%). During a median follow-up of 7.7 years, 356 (71%) underwent elective surgery (89% of class 1 patients). Early-surgery (≤6 months from baseline) occurred in 195 patients and 311 patients remained “under-surveillance”. Surgery under-surveillance (&gt;6 months from baseline) occurred in 161/311 (52%) patients at 2.6 [IQR: 1.5–4.7] years of follow-up. Surgical mortality was 1.4%. Ten-year post-surgical survival was &gt;90% and similar between early-surgery and surgery under-surveillance (p=0.8). Of 8 AoD events, 3 confirmed and 5 possible (all occurring in unoperated patients under-surveillance), 5 events occurred with maximal aortic baseline diameter ≥55 mm and/or &gt;1 year without clinical follow-up; therefore, under guideline non-compliance. Incidence of confirmed plus possible aortic dissection was 0.37% per year with size 50–54mm, 1.13% per year between 55–59mm, and 10.41% per year with size ≥60mm. Aortic stenosis was associated with AoD (p=0.04) and all-cause death in unoperated patients (p=0.001). Conclusions Our results suggest that BAV patients can be safely followed as per guidelines after aortic diameters enter the elective surgical zone (≥50mm), with excellent post-surgical survival regardless of whether surgery is early- or under-surveillance. Appropriate guideline implementation for surgical thresholds and interval clinical follow-up could have prevented most AoD events. Valvular disease guideline should also be followed to prevent AoD and improve survival in these patients. Funding Acknowledgement Type of funding sources: None.

Abstract Objectives Right ventricular (RV) failure occurring after left ventricular assist device (LVAD) implantation is associated with increased mortality. Purpose We sought to determine whether RV strain by echocardiography predicts outcomes following LVAD. Methods Consecutive patients who underwent continuous flow LVAD placement at our clinic between February 2007 and October 2018 were included. Patients with complex congenital heart disease and arrhythmogenic right ventricular cardiomyopathy were excluded. Baseline characteristics, pre-operative hemodynamic catheterization (1 week) transthoracic echocardiography (1 month) measurements were obtained from the medical record. Speckle tracking RV free wall longitudinal 2D strain was measured using TomTec Imaging System. Univariate and multivariable analysis performed to identify predictors of mortality following LVAD. Results The study group was comprised of 323 patients (mean age 60.8±11.5, 79.9% male) of which 256 had adequate image quality for RV strain. RV strain was impaired in most patients (mean −11.7±3.47). RV strain of −8.1% was identified as predictive of poor outcomes. RV strain did not correlate with other measures of RV dysfunction including pulmonary artery pulsatility index (PAPi) and RVFAC. After adjusting for PAPi, RA pressure, and clinical cumulative risk scores (Matthews, Kormos and Lietz-Miller) impaired RV free wall longitudinal strain (OR: 1.14; 95% CI: 1.01–1.29, p=0.025), tricuspid regurgitation (by vena contracta width (TRvc) (OR: 1.19; 95% CI: 1.05–1.36, p=:0.0021) and smaller LVEDD (OR: 0.95; 95% CI: 0.92–0.99, p:0.023) predicted 30-day all-cause mortality and RV failure. Decreased RV free wall strain also predicted peri-operative mortality (22.58% vs. 4.89%, p=0.0003) and prolonged inotropic support (247.0.±277.4 vs. 122.8±139.3, p=0.024). Conclusions RV free wall strain is a non-invasive independent predictor of 30-day adverse outcomes (RV failure or all-cause mortality) Routine measurement of RV free strain may identify those patients at highest risk for early and long term mortality following LVAD. Funding Acknowledgement Type of funding source: None