European heart journal. Imaging methods and practice最新文献

Coronary artery disease (CAD) remains one of the most frequent comorbidities among transcatheter aortic valve implantation (TAVI) candidates. Whether routine assessment of CAD by invasive coronary angiography (CA) and eventual peri-procedural percutaneous coronary intervention (PCI) is generally beneficial in TAVI patients has recently been heavily questioned. CA carries significant risks, such as kidney injury, bleeding, and prolonged hospital stay, and may frequently be unnecessary if significant stenoses of the proximal coronary segments can be ruled out on computed tomography angiography. Moreover, the benefits of pre-emptive coronary revascularization at the time of TAVI are not well defined. Despite these facts and weak guideline recommendations, CA and eventual PCI of stable significant coronary lesions at the time of TAVI remain common practice. However, ongoing randomized trials currently challenge the efficacy of such strategies to enable a more streamlined, individualized, and resource-sparing treatment with TAVI.

Aims: To address the limitations of traditional diagnostic methods for mitral valve prolapse (MVP), specifically fibroelastic deficiency (FED) and Barlow's disease (BD), by introducing an automated diagnostic approach utilizing multi-view echocardiographic sequences and deep learning.

Methods and results: An echocardiographic data set, collected from Zhongshan Hospital, Fudan University, containing apical 2 chambers (A2C), apical 3 chambers (A3C), and apical 4 chambers (A4C) views, was employed to train the deep learning models. We separately trained view-specific and view-agnostic deep neural network models, which were denoted as MVP-VS and MVP view-agonistic (VA), for MVP diagnosis. Diagnostic accuracy, precision, sensitivity, F1-score, and specificity were evaluated for both BD and FED phenotypes. MVP-VS demonstrated an overall diagnostic accuracy of 0.94 for MVP. In the context of BD diagnosis, precision, sensitivity, F1-score, and specificity were 0.83, 1.00, 0.90, and 0.92, respectively. For FED diagnosis, the metrics were 1.00, 0.83, 0.91, and 1.00. MVP-VA exhibited an overall accuracy of 0.95, with BD-specific metrics of 0.85, 1.00, 0.92, and 0.94 and FED-specific metrics of 1.00, 0.83, 0.91, and 1.00. In particular, the MVP-VA model using mixed views for training demonstrated efficient diagnostic performance, eliminating the need for repeated development of MVP-VS models and improving the efficiency of the clinical pipeline by using arbitrary views in the deep learning model.

Conclusion: This study pioneers the integration of artificial intelligence into MVP diagnosis and demonstrates the effectiveness of deep neural networks in overcoming the challenges of traditional diagnostic methods. The efficiency and accuracy of the proposed automated approach suggest its potential for clinical applications in the diagnosis of valvular heart disease.

Aims: A new model of computational fluid dynamics (CFD)-based algorithm for coronary CT angiography (CCTA)-derived fractional flow reserve (FFR) (CT-FFR) analysis by expanding the coronary tree to smaller-diameter lumen (0.8 mm) using Newton-Krylov-Schwarz (NKS) method to solve the three-dimensional time-dependent incompressible Navier-Stokes equations has been developed; however, the diagnostic performance of this new method has not been sufficiently investigated. The aim of this study was to determine the diagnostic performance of a novel CT-FFR technique by expanding the coronary tree in the CFD domain.

Methods and results: Six centres enrolled 338 symptomatic patients with suspected or known coronary artery disease (CAD) who prospectively underwent CCTA and FFR. Stenosis assessment in CCTA and CT-FFR analysis were performed in independent core laboratories. Haemodynamically significant stenosis was defined by a CT-FFR and FFR ≤ 0.80, and anatomically obstructive CAD was defined as a CCTA with stenosis ≥ 50%. Diagnostic performance of CT-FFR was evaluated against invasive FFR using receiver operating characteristic (ROC) curve analysis. The correlation between CT-FFR and invasive FFR was analysed using the Spearman correlation coefficient and Bland-Altman analysis. Intra-observer and inter-observer agreements were evaluated utilizing the intraclass correlation coefficient (ICC). In this study, 338 patients with 422 targeted vessels were investigated, revealing haemodynamically significant stenosis in 31.1% (105/338) of patients and anatomically obstructive stenosis in 54.1% of patients. On a per-vessel basis, the area under the ROC curve for CT-FFR was 0.94 vs. 0.76 for CCTA (P < 0.001). Per-vessel accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 89.8%, 89.3%, 90.0%, 79.0%, and 99.2%, respectively, for CT-FFR and were 68.4%, 82.8%, 62.3%, 48.1%, and 89.6%, respectively, for CCTA stenosis. CT-FFR and FFR were well correlated (r = 0.775, P < 0.001) with a Bland-Altman bias of 0.0011, and limits of agreement from -0.1509 to 0.1531 (P = 0.770). The ICCs with CT-FFR for intro- and inter-observer agreements were 0.919 (95% CI: 0.866-0.952) and 0.909 (95% CI: 0.851-0.945), respectively. The average computation time for CT-FFR analysis was maintained at 11.7 min.

Conclusion: This novel CT-FFR model with the inclusion of smaller lumen provides high diagnostic accuracy in detecting haemodynamically significant CAD. Furthermore, the integration of the NKS method ensures that the computation time remains within an acceptable range for potential clinical applications in the future.

Aims: Predictors of true degenerative mitral stenosis (MS) in patients with aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) remain unknown. This study aimed to investigate the predictors and prognostic value of true degenerative MS in this population.

Methods and results: We retrospectively reviewed the records of 760 consecutive patients who underwent TAVI. The mitral valve area (MVA) was assessed using transthoracic echocardiography, and mitral valve calcification was assessed using multi-detector computed tomography. MS was defined as an MVA of ≤2.0 cm², and true MS was defined as moderate or severe MS following TAVI. In our TAVI cohort, we identified 72 (9.5%) patients with degenerative MS. Among these, true MS was observed in 38 (52.7%) patients. Echocardiographic data showed that the true MS group had a significantly lower MVA and higher trans-mitral gradient. The severity of mitral annular calcification was not significantly different between the two groups; however, the true MS group had significantly more posterior mitral leaflet and anterior mitral leaflet (AML) calcification. Multivariable logistic regression analysis showed that AML calcification was the independent predictor of true MS [adjusted odds ratio, 9.23; 95% confidence interval (CI) 2.84-29.9]. True MS was independently associated with poor prognosis (adjusted hazard ratio, 2.76; 95% CI 1.09-6.98).

Conclusion: Approximately half of the patients with concomitant degenerative MS who underwent TAVI had true MS, which was associated with a poor prognosis. Computed tomographic analysis of AML calcification was useful for predicting true MS.

Aims: The clinical utility of regional strain measurements in echocardiography is challenged by suboptimal reproducibility. In this study, we aimed to evaluate the test-retest reproducibility of regional longitudinal strain (RLS) per coronary artery perfusion territory (RLS_Territory) and basal-to-apical level of the left ventricle (RLS_Level), measured by a novel fully automated deep learning (DL) method based on point tracking.

Methods and results: We measured strain in a dual-centre test-retest data set that included 40 controls and 40 patients with suspected non-ST elevation acute coronary syndrome. Two consecutive echocardiograms per subject were recorded by different operators. The reproducibility of RLS_Territory and RLS_Level measured by the DL method and by three experienced observers using semi-automatic software (2D Strain, EchoPAC, GE HealthCare) was evaluated as minimal detectable change (MDC). The DL method had MDC for RLS_Territory and RLS_Level ranging from 3.6 to 4.3%, corresponding to a 33-35% improved reproducibility compared with the inter- and intraobserver scenarios (MDC 5.5-6.4% and 4.9-5.4%). Furthermore, the DL method had a lower variance of test-retest differences for both RLS_Territory and RLS_Level compared with inter- and intraobserver scenarios (all P < 0.001). Bland-Altman analyses demonstrated superior reproducibility by the DL method for the whole range of strain values compared with the best observer scenarios. The feasibility of the DL method was 93% and measurement time was only 1 s per echocardiogram.

Conclusion: The novel DL method provided fully automated measurements of RLS, with improved test-retest reproducibility compared with semi-automatic measurements by experienced observers. RLS measured by the DL method has the potential to advance patient care through a more detailed, more efficient, and less user-dependent clinical assessment of myocardial function.