Relationship Between the Ratio of Acceleration Time/Ejection Time and Mortality in Patients With High-Gradient Severe Aortic Stenosis

Is the Corrected Carotid Flow Time a Clinically Acceptable Surrogate Hemodynamic Parameter for the Left Ventricular Ejection Time?

OBJECTIVE

The corrected left ventricular ejection time (cLVET) comprises the phase from aortic valve opening to aortic valve closure corrected for heart rate. As a surrogate measure for cLVET, the corrected carotid flow time (ccFT) has been proposed in previous research. The aim of this study was to assess the clinical agreement between cLVET and ccFT in a dynamic clinical setting.

METHODS

Twenty-five patients with severe aortic valve stenosis (AS) were selected for transcatheter aortic valve replacement (TAVR). The cLVET and ccFT were derived from the left ventricular outflow tract (LVOT) and the common carotid artery (CCA), respectively, using pulsed wave Doppler ultrasound. Bazett's (B) and Wodey's (W) equations were used to calculate cLVET and ccFT. Measurements were performed directly before (T1) and after (T2) TAVR. Correlation, Bland-Altman and concordance analyses were performed.

RESULTS

Corrected LVET decreased from T1 to T2 (p < 0.001), with relative reductions of 11% (B) and 9% (W). Corrected carotid flow time decreased (p < 0.001), with relative reductions of 12% (B) and 10% (W). The correlation between cLVET and ccFT was strong for B (ρ = 0.74, p < 0.001) and W (ρ = 0.81, p < 0.001). The bias was -39 ms (B) and -37 ms (W), and the upper and lower levels of agreement were 19 and -98 ms (B) and 5 and -78 ms (W), respectively. Trending ability between cLVET and ccFT was good (concordance 96%) for both B and W.

CONCLUSION

In TAVR patients, the clinical agreement between cLVET and ccFT was acceptable, indicating that ccFT could serve as a surrogate measure for cLVET.

Sex-Specific Differences in Aortic Valve Calcification Between Bicuspid and Tricuspid Severe Aortic Stenosis

Sex-specific thresholds of aortic valve calcification (AVC) correlate with aortic stenosis (AS) and may complement echocardiography to determine AS severity. Importantly, current guideline-recommended thresholds of AVC scores derived by multislice computed tomography do not distinguish between bicuspid and tricuspid aortic valves. The objective of this study was to evaluate the sex-specific differences in the amount of AVC in patients with severe AS and tricuspid (TAV) versus bicuspid (BAV) aortic valve morphologies, retrospectively evaluated by 2 tertiary care institutions. The inclusion criteria comprised patients with severe AS and a left ventricular ejection fraction ≥50% and suitable imaging examinations. The study included 1,450 patients (723 men; 49.9%) with severe AS, including 1,335 patients with TAV (92.1%) and 115 with BAV (17.9%). The calculated Agatston score was higher in BAV patients (men: BAV 4,358 [2,644 to 6,005] AU vs TAV 2,643 [1,727 to 3,794] AU, p <0.01; women: BAV 2,174 [1,330 to 4,378] AU vs TAV 1,703 [964 to 2,534] AU, p <0.01), also when indexed for valve dimensions and body surface area (men: BAV 2,227 [321 to 3,105] AU/m² vs TAV 1,333 [872 to 1,913] AU/m², p <0.01; women: BAV 1,326 [782 to 2,148] AU/m² vs TAV 930 [546 to 1,456] AU/m², p <0.01). Differences between the BAV- and TAV-derived Agatston score was more prominent in concordant severe AS. In conclusion, sex-specific Agatston scores in severe AS were approximately 1/3 higher in patients with BAV than in patients with TAV for both women and men. Optimal AVC thresholds should be adjusted for BAV, also respecting considerable prognostic implications.

The Impact of Left Ventricular Performance and Afterload on the Evaluation of Aortic Valve Stenosis: A 1D Mathematical Modeling Approach

The transaortic valvular pressure gradient (TPG) plays a central role in decision-making for patients suffering from severe aortic stenosis. However, the flow-dependence nature of the TPG makes the diagnosis of aortic stenosis challenging since the markers of cardiac performance and afterload present high physiological interdependence and thus, isolated effects cannot be measured directly in vivo. We used a validated 1D mathematical model of the cardiovascular system, coupled with a model of aortic stenosis, to assess and quantify the independent effect of the main left ventricular performance parameters (end-systolic (Ees) and end-diastolic (Eed) elastance) and principal afterload indices (total vascular resistance (TVR) and total arterial compliance (TAC)) on the TPG for different levels of aortic stenosis. In patients with critical aortic stenosis (aortic valve area (AVA) ≤ 0.6 cm2), a 10% increase of Eed from the baseline value was associated with the most important effect on the TPG (-5.6 ± 0.5 mmHg, p < 0.001), followed by a similar increase of Ees (3.4 ± 0.1 mmHg, p < 0.001), in TAC (1.3 ±0.2 mmHg, p < 0.001) and TVR (-0.7 ± 0.04 mmHg, p < 0.001). The interdependence of the TPG left ventricular performance and afterload indices become stronger with increased aortic stenosis severity. Disregarding their effects may lead to an underestimation of stenosis severity and a potential delay in therapeutic intervention. Therefore, a comprehensive evaluation of left ventricular function and afterload should be performed, especially in cases of diagnostic challenge, since it may offer the pathophysiological mechanism that explains the mismatch between aortic severity and the TPG.

Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis

Aims

Hypothesizing that aortic outflow velocity profiles contain more valuable information about aortic valve obstruction and left ventricular contractility than can be captured by the human eye, features of the complex geometry of Doppler tracings from patients with severe aortic stenosis (AS) were extracted by a convolutional neural network (CNN).

Methods and results

After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Among 366 eligible patients [age: 79.8 ± 6.77 years; 146 (39.9%) women] with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR), good quality Doppler tracings from 101 patients were analysed. The convolutional part of the pre-trained VGG-16 model in conjunction with principal component analysis and k-means clustering distinguished two shapes of aortic outflow velocity profiles. Kaplan-Meier analysis revealed that mortality in patients from Cluster 2 (n = 40, 39.6%) was significantly increased [hazard ratio (HR) for 2-year mortality: 3; 95% confidence interval (CI): 1-8.9]. Apart from reduced cardiac output and mean aortic valve gradient, patients from Cluster 2 were also characterized by signs of pulmonary hypertension, impaired right ventricular function, and right atrial enlargement. After training an extreme gradient boosting algorithm on these 101 patients, validation on the remaining 265 patients confirmed that patients assigned to Cluster 2 show increased mortality (HR for 2-year mortality: 2.6; 95% CI: 1.4-5.1, P-value: 0.004).

Conclusion

Transfer learning enables sophisticated pattern recognition even in clinical data sets of limited size. Importantly, it is the left ventricular compensation capacity in the face of increased afterload, and not so much the actual obstruction of the aortic valve, that determines fate after TAVR.

Value of Left Ventricular Indexed Ejection Time to Characterize the Severity of Aortic Stenosis

Aims: The assessment of aortic stenosis (AS) severity is still challenging, especially in abnormal hemodynamic conditions. Left ventricular ejection time (LVET) has been historically related to AS severity, but it also depends on heart rate (HR) and systolic function. Our aim was to verify if correcting LVET (LVET index, LVETI) by its determinants is helpful for the assessment of AS severity, irrespective of hemodynamic conditions. Methods and results: We retrospectively studied 152 patients with AS and 378 patients with heart failure and no-AS. At multivariate analysis, LVET (assessed with pulsed-wave Doppler) showed a strong correlation with stroke volume index (SVI) (Beta 0.354; p < 0.001), HR (−0.385; p < 0.001), AS grade (Beta 0.301; p < 0.001) and, less significantly, ejection fraction (LVEF) (Beta 0.108; p = 0.001). AS grade was confirmed to be a major determinant of LVET, irrespective of forward flow (assessed by SVI and transvalvular flow rate) and LVEF (above and below 50%). A regression equation was derived to index LVET (LVETI) to HR and SVI. By using this formula, LVETI detected severe AS more accurately (AUC 0.812, p < 0.001) than LVET alone (AUC 0.755, p for difference = 0.005). Similar results were observed in patients with abnormal flow status. As an exploratory finding, we observed that the highest tertile of LVETI was associated with a higher rate of aortic valve interventions during follow-up. Conclusions: LVETI correlates with AS severity better than uncorrected LVET, independently from hemodynamic conditions, and may help to discriminate severe AS. This finding needs confirmation in larger prospective multicenter studies.