European Heart Journal - Cardiovascular Imaging最新文献

Aims: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank.

Methods and results: A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1 mm isotropic resolution from CMR short- and long-axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate, and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P-values), particularly for sex, age, and body mass index. AUCs for all logistic regressions were higher for deep learning volumes than standard volumes (P < 0.001 for all four chambers at ED and ES).

Conclusion: Neural network reconstructions of whole heart volumes had significantly stronger associations with CVD and risk factors than standard volume estimation methods in an automatic processing pipeline.

Aims: There is increasing evidence that plaque instability in the extracranial carotid artery may lead to an increased stroke risk independently of the degree of stenosis. We aimed to determine diagnostic accuracy of vulnerable and stable plaque using noninvasive imaging modalities when compared to histology in patients with symptomatic and asymptomatic carotid atherosclerosis.

Methods and results: Medline Ovid, Embase, Cochrane Library, and Web of Science were searched for diagnostic accuracy of noninvasive imaging modalities (CT, MRI, US) in the detection of 1) vulnerable/stable plaque, and 2) vulnerable/stable plaque characteristics, compared to histology. The quality of included studies was assessed by QUADAS-2 and univariate and bivariate random-effect meta-analyses were performed. We included 36 vulnerable and 5 stable plaque studies in the meta-analysis, and out of 211 plaque characteristics from remaining studies, we classified 169 as vulnerable and 42 as stable characteristics (28 CT, 120 MRI, 104 US characteristics). We found that MRI had high accuracy [90% (95% CI: 82-95%)] in the detection of vulnerable plaque, similar to CT [86% (95% CI: 76-92%); P > 0.05], whereas US showed less accuracy [80% (95% CI: 75-84%); P = 0.013]. CT showed high diagnostic accuracy in visualizing characteristics of vulnerable or stable plaques (89% and 90%) similar to MRI (86% and 89%; P > 0.05); however, US had lower accuracy (77%, P < 0.001 and 82%, P > 0.05).

Conclusion: CT and MRI have a similar, high performance in detecting vulnerable carotid plaques, whereas US showed significantly less diagnostic accuracy. Moreover, MRI visualized all vulnerable plaque characteristics allowing for a better stroke risk assessment.

Registration: PROSPERO ID CRD42022329690.

Left ventricular assist devices (LVADs) are gaining increasing importance as therapeutic strategy in advanced heart failure (HF), not only as bridge to recovery or to transplant but also as destination therapy. Even though long-term LVADs are considered a precious resource to expand the treatment options and improve clinical outcome of these patients, these are limited by peri-operative and post-operative complications, such as device-related infections, haemocompatibility-related events, device mis-positioning, and right ventricular failure. For this reason, a precise pre-operative, peri-operative, and post-operative evaluation of these patients is crucial for the selection of LVAD candidates and the management LVAD recipients. The use of different imaging modalities offers important information to complete the study of patients with LVADs in each phase of their assessment, with peculiar advantages/disadvantages, ideal application, and reference parameters for each modality. This clinical consensus statement sought to guide the use of multimodality imaging for the evaluation of patients with advanced HF undergoing LVAD implantation.

Aims: Cardiac cycle morphological changes can accelerate plaque growth proximal to myocardial bridging (MB) in the left anterior descending artery (LAD). To assess coronary computed tomography angiography (CCTA)-based vascular radiomics for predicting proximal plaque development in LAD MB.

Methods and results: Patients with repeated CCTA scans showing LAD MB without proximal plaque in index CCTA were included from Jinling Hospital as a development set. They were divided into training and internal testing in an 8:2 ratio. Patients from four other tertiary hospitals were set as external validation set. The endpoint was proximal plaque development of LAD MB in follow-up CCTA. Four vascular radiomics models were built: MB centreline (MB CL), proximal MB CL (pMB CL), MB cross-section (MB CS), and proximal MB CS (pMB CS), whose performances were evaluated using area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). In total, 295 patients were included in the development (n = 192; median age, 54 ± 11 years; 137 men) and external validation sets (n = 103; median age, 57 ± 9 years; 57 men). The pMB CS vascular radiomics model exhibited higher AUCs in training, internal test, and external sets (AUC = 0.78, 0.75, 0.75) than the clinical and anatomical model (all P < 0.05). Integration of the pMB CS vascular radiomics model significantly raised the AUC of the clinical and anatomical model from 0.56 to 0.75 (P = 0.002), along with enhanced NRI [0.76 (0.37-1.14), P < 0.001] and IDI [0.17 (0.07-0.26), P < 0.001] in the external validation set.

Conclusion: The CCTA-based pMB CS vascular radiomics model can predict plaque development in LAD MB.

Aims: Aortic valve calcification (AVC) is prognostic in patients with aortic stenosis (AS). We assessed the AVC prognostic value in non-severe AS patients.

Methods and results: We conducted a retrospective study of 395 patients with non-severe AS, LVEF ≥ 50%. The Agatston method was used for CT AVC assessment. The log-rank test determined the best AVC cut-offs for survival under medical surveillance: 1185  arbitrary unit (AU) in men and 850 AU in women, lower than the established cut-offs for severe AS (2064 AU in men and 1274 AU in women). Patients were divided into 3 AVC groups based on these cut-offs: low (<1185 AU in men and <850 AU in women), sub-severe (1185-2064 AU in men and 850-1274 AU in women), and severe (>2064 AU in men and >1274 AU in women). Of 395 patients (mean age 73 ± 12 years, 60.5% men, aortic valve area 1.23 ± 0.30 cm2, mean pressure gradient 28 ± 8 mmHg), 218 underwent aortic valve intervention (AVI) and 158 deaths occurred during follow-up, 82 before AVI. Median survival time under medical surveillance was 2.1 (0.7-4.9) years. Compared with the low AVC group, both sub-severe and severe AVC groups had higher risk for all-cause death under medical surveillance after comprehensive adjustment including echocardiographic AS severity and coronary artery calcium score (all P ≤ 0.006); while mortality risk was similar between sub-severe and severe AVC groups (all P ≥ 0.2). This mortality risk pattern persisted in the overall survival analysis after adjustment for AVI. AVI was protective of all-cause death in the sub-severe and severe AVC (all P ≤ 0.01), but not in the low AVC groups.

Conclusion: Sub-severe AVC is a robust risk stratification parameter in patients with non-severe AS and may inform AVI timing.

Aims: To evaluate different cardiovascular magnetic resonance (CMR) parameters for the differentiation of light chain amyloidosis (AL) and transthyretin-related amyloidosis (ATTR).

Methods and results: In total, 75 patients, 53 with cardiac amyloidosis {20 patients with AL [66 ± 12 years, 14 males (70%)] and 33 patients with ATTR [78 ± 5 years, 28 males (88%)]} were retrospectively analysed regarding CMR parameters such as T1 and T2 mapping, extracellular volume (ECV), late gadolinium enhancement (LGE) distribution patterns, and myocardial strain, and compared to a control cohort with other causes of left ventricular hypertrophy {LVH; 22 patients [53 ± 16 years, 17 males (85%)]}. One-way ANOVA and receiver operating characteristic analysis were used for statistical analysis. ECV was the single best parameter to differentiate between cardiac amyloidosis and controls [area under the curve (AUC): 0.97, 95% confidence intervals (CI): 0.89-0.99, P < 0.0001, cut-off: >30%]. T2 mapping was the best single parameter to differentiate between AL and ATTR amyloidosis (AL: 63 ± 4 ms, ATTR: 58 ± 2 ms, P < 0.001, AUC: 0.86, 95% CI: 0.74-0.94, cut-off: >61 ms). Subendocardial LGE was predominantly observed in AL patients (10/20 [50%] vs. 5/33 [15%]; P = 0.002). Transmural LGE was predominantly observed in ATTR patients (23/33 [70%] vs. 2/20 [10%]; P < 0.001). The diagnostic performance of T2 mapping to differentiate between AL and ATTR amyloidosis was further increased with the inclusion of LGE patterns [AUC: 0.96, 95% CI: (0.86-0.99); P = 0.05].

Conclusion: ECV differentiates cardiac amyloidosis from other causes of LVH. T2 mapping combined with LGE differentiates AL from ATTR amyloidosis with high accuracy on a patient level.

Aims: Longitudinal dyssynchrony correction and 'strain' improvement by comparable cardiac resynchronization therapy (CRT) techniques is unreported. Our purpose was to compare echocardiographic dyssynchrony correction and 'strain' improvement by conduction system pacing (CSP) vs. biventricular pacing (BiVP) as a marker of contractility improvement during 1-year follow-up.

Methods and results: A treatment-received analysis was performed in patients included in the LEVEL-AT trial (NCT04054895), randomized to CSP or BiVP, and evaluated at baseline (ON and OFF programming) and at 6 and 12 months (n = 69, 32% women). Analysis included intraventricular (septal flash), interventricular (difference between left and right ventricular outflow times), and atrioventricular (diastolic filling time) dyssynchrony and 'strain' parameters [septal rebound, global longitudinal 'strain' (GLS), LBBB pattern, and mechanical dispersion). Baseline left ventricular ejection fraction (LVEF) was 27.5 ± 7%, and LV end-systolic volume (LVESV) was 138 ± 77 mL, without differences between groups. Longitudinal analysis showed LVEF and LVESV improvement (P < 0.001), without between-group differences. At 12-month follow-up, adjusted mean LVEF was 46% with CSP (95% CI 42.2 and 49.3%) vs. 43% with BiVP (95% CI 39.6 and 45.8%), (P = 0.31), and LVESV was 80 mL (95% CI 55.3 and 104.5 mL) vs. 100 mL (95% CI 78.7 and 121.6 mL), respectively (P = 0.66). Longitudinal analysis showed a significant improvement of all dyssynchrony parameters and GLS over time (P < 0.001), without differences between groups. Baseline GLS significantly correlated with LVEF and LVESV at 12-month follow-up.

Conclusion: CSP and BiVP provided similar dyssynchrony and 'strain' correction over time. Baseline global longitudinal 'strain' predicted ventricular remodelling at 12-month follow-up.