Automated simultaneous quantification of LA and LV volumes and LVEF is feasible and requires minimal 3D software analysis training. The automated measurements are not only comparable to manual measurements but also to CMR. This technique is highly reproducible and timesaving, and it therefore promises to facilitate the integration of 3D TTE-based left-heart chamber quantification into clinical practice.
3D TTE showed a clear association between device lead position and TR. To minimize TR induced by device-leads, 3D TTE guidance should be considered for placement in a commissural position.
Background
Implantable device leads can cause tricuspid regurgitation (TR) when they interfere with leaflet motion. The aim of this study was to determine whether lead-leaflet interference is associated with TR severity, independent of other causative factors of functional TR.
Methods
A total of 100 patients who underwent transthoracic two-dimensional and three-dimensional (3D) echocardiography of the tricuspid valve before and after lead placement were studied. Lead position was classified on 3D echocardiography as leaflet-interfering or noninterfering. TR severity was estimated by vena contracta (VC) width. Logistic regression analysis was used to identify factors associated with postdevice TR, including predevice VC width, right ventricular end-diastolic and end-systolic areas, fractional area change, right atrial size, tricuspid annular diameter, TR gradient, device lead age, and presence or absence of lead interference. Odds ratios were used to describe the association with moderate (VC width ≥ 0.5 cm) or severe (VC width ≥ 0.7 cm) TR, separately, using bivariate and stepwise multivariate logistic regression analysis.
Results
Forty-five of 100 patients showed device lead tricuspid valve leaflet interference. The septal leaflet was the most commonly affected (23 patients). On bivariate analysis, preimplantation VC width, right atrial size, tricuspid annular diameter, and lead-leaflet interference were significantly associated with postdevice TR. On multivariate analysis, preimplantation VC width and the presence of an interfering lead were independently associated with postdevice TR. Furthermore, the presence of an interfering lead was the only factor associated with TR worsening, increasing the likelihood of developing moderate or severe TR by 15- and 11-fold, respectively.
Conclusion
Lead-leaflet interference as seen on 3D echocardiography is associated with TR after device lead placement, suggesting that 3D echocardiography should be used to assess for lead interference in patients with significant TR.
Curvature analysis using 3D echocardiography allows quantitative evaluation of RV remodelling, which could be used to track differential changes in regional RV shape, as a way to assess disease progression or regression.
Background
Although 3D echocardiography (3DE) allows accurate and reproducible quantification of cardiac chambers, it has not been integrated into clinical practice because it relies on manual input, which interferes with workflow. A recently developed automated adaptive analytics algorithm for simultaneous quantification of left ventricular and atrial (LV, LA) volumes was found to be accurate and reproducible in patients with good images. We sought to prospectively test its feasibility and accuracy in consecutive patients in relationship with image quality and reader experience.
Methods
Three hundred consecutive patients underwent 3DE. Image quality was graded as poor, adequate, or good. Images were analyzed by an expert echocardiographer to obtain LV volumes and ejection fraction (EF) and LA volume using the automated analysis (HeartModel, Philips, Andover, MA) with and without editing the endocardial boundaries and using conventional manual tracing (QLAB, Philips, Andover, MA) blinded to the automated measurements as a reference. In a subgroup of 100 patients, automated analysis was repeated by two readers without 3DE experience.
Results
Automated analysis failed in 31/300 patients (10%). Patients with poor image quality (n = 72, 24%) showed suboptimal agreement with the reference technique, especially for LVEF. Importantly, patients with adequate (n = 89, 30%) and good (n = 108, 36%) images showed small biases and excellent correlations without border corrections, which were further improved with editing. In contrast, border corrections by inexperienced readers did not improve the agreement with reference values.
Conclusions
Automated 3DE analysis allows accurate quantification of left-heart size and function in 66% of consecutive patients, while in the remaining patients, its performance is limited/unreliable due to image quality. Border corrections require 3DE experience to improve the accuracy of the automated measurements. In patients with sufficient image quality, this automated approach has the potential to overcome the workflow limitations of the 3D analysis in clinical practice.
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