Paravalvular leak prediction after transcatheter aortic valve replacement with self-expandable prosthesis based on quantitative aortic calcification analysis

Introduction

The number of transcatheter aortic valve replacement (TAVR) procedures in patients with severe aortic valve stenosis (AS) is increasing every year, and the indications are being constantly extended (1). One of the most common complications after TAVR is paravalvular leak (PVL), which influences acute and long-term clinical outcomes (2,3). The incidence of at least mild PVL after TAVR is estimated at ca. 20 to 60% of patients and occurs more frequently than in the case of surgical aortic valve replacement (AVR) (4,5). During short- and long-term follow-up, previous studies have shown that the occurrence of moderate to severe PVL increases mortality after TAVR (6,7). However, there is no unified method of identifying patients with increased PVL risk. Moreover, there is no clear, objective statement regarding perioperative management or proper valvular prostheses selection, both of which make it possible to reduce the postprocedural occurrence of PVL. The European Imaging Biomarkers ALLiance (EIBALL), which is a subcommittee of the European Society of Radiology (ESR), emphasized the important role of imaging biomarkers as they are able to quantify the extent of morphological characteristics. Also, the EIBALL subcommittee indicates the necessity of building imaging databases and their use in the prediction of clinical outcomes (8).

The purpose of our study was to identify imaging biomarkers to predict PVL occurrence after TAVR. We focused on a detailed analysis of aortic valve calcifications (AVC) in preprocedural multi-slice computed tomography (MSCT) of AS patients qualified for TAVR.

Methods

Fifty patients with severe AS (tricuspid aortic valve, aortic valve area <1.0 cm² and mean pressure gradient >40 mmHg) who had undergone TAVR with a self-expanding valve (Evolut R, Medtronic Inc, USA) were included in the study. The valve size selection was based on the CT-derived aortic annulus size, together with perimeter according to valve sizing charts. Imaging data from preprocedural contrast-enhanced CT and postprocedural transthoracic echocardiography (TTE) were analyzed. Data on baseline characteristics, procedural and long-term outcomes were collected. All clinical and imaging data were collected and analyzed retrospectively. The study was approved by the Ethical Board of the Jagiellonian University Medical College, Krakow, Poland (1072.6120.234.2017). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki with later amendments. Written informed consent was obtained from the patient for publication of this study and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.

All contrast-enhanced CT scans were performed using a Toshiba scanner (Toshiba Aquilion 64 CT scanner, Toshiba, Tokyo, Japan). All acquisitions were performed using an institutional standard protocol and reconstruction settings. The protocol involved a scan run that consisted of the prospective acquisition of contiguous transverse slices (0.5 mm × 64 detector row) in a single breath-hold of 6–10 seconds, triggered at 60–80% of the electrocardiogram R-to-R wave interval.

MSCT scans were analyzed twice: (I) by an experienced radiologist before TAVR, using the traditional method; (II) by two trained corelab analysts blinded to the clinical and echocardiographic data, using dedicated software based on MATLAB version R2017a (MathWorks, Natick, MA, USA) and ImageJ version 1.51 (NIH, USA) with the BoneJ plugin version 1.4.2 (9). The limited intraobserver and interobserver variability for AVC evaluation was confirmed by Intraclass Correlation Coefficient (ICC), which was 0.992 (95% CI, 0.990–0.995) and 0.991 (95% CI, 0.988–0.993), respectively.

Traditional CT parameters measured by radiologist were used to validate the quantitative parameters received by our dedicated software based on MATLAB.

TTE was performed on a GE Healthcare echocardiograph (Vivid E9, GE Healthcare, Chicago, Illinois, USA). A trained physician who was blinded to the clinical and radiological data analyzed the postprocedural TTE images to detect PVL occurrence in 1-, 3-, 6-, and 12-month follow-ups, and its severity was classified according to the Valve Academic Research Consortium-2 (VARC-2) Consensus Recommendation and American Society of Echocardiography guidelines for the evaluation of valvular regurgitation after percutaneous intervention (10,11). We based on the assessment on semi-quantitative methods in color doppler echocardiography such as jet number, location, direction, and eccentricity and circumferential extent.

Clinical follow-up data, including novel medical incidents such as pacemaker implantation, were collected retrospectively at 1-, 3-, 6-, and 12-month observations.

CT-derived data processing

All calculations were performed in the Volume-Of-Interest (VOI), which is defined as a stack of closed planar polygons of CT slices; this is a 3D space which in our case contains the calcified aortic valve (between the highest visible point of the aortic apparatus and the left ventricle outflow tract). For better visualization of calcified aortic leaflets, the initial imaging data underwent computational modelling, recomputation and rotation into a set of cross-sections parallel to the plane of the valve ring and short axis of the ascending aorta. The result of computational modelling is presented in Figure 1.

Figure 1 The coronal view (A) and perpendicular plane (B) for aortic valve. The images were rotated to obtain planes perpendicular to the aortic valve. The yellow dotted squares show the boundaries for VOI and ROI analysis. ROI, region of interest; VOI, volume of interest.

The threshold cut-off value for detection of calcifications was set individually for each case, but it was typically set in the range 391–689 Hounsfield units (HU). This threshold was adopted due to the contrast medium attenuation in the aortic root. Every calcified mass except for the AVC was excluded from the analysis.

The selected VOI was then used to adjust the corresponding planar projection [region-of-interest (ROI)] of the aortic valve using the maximum intensity of the calcium conglomerates contained in the VOI. However, the large variability of calcium distribution causes a quantification problem in a single-slice image (before processing). To solve this problem and to provide better visualization of local AVC distribution, radial AVC presentation was implemented. The ROI presentation was inscribed in a circular plane with a central point that was previously mathematically assigned as the center point of the aortic valve. The AVC amount was individually calculated from the mass center in 1-degree slices until the entire circle had been analyzed. This approach allowed the exact local AVC distribution to be presented, both in the direct CT image and in the form of a chart. An exemplary image is presented in Figure 2, which shows the radial image intensity (y-axis) in reference to degree coordination (x-axis).

Figure 2 Radial presentation of the aortic valve. Visualization of the polar coordinate system makes it possible to present the degree of calcification on a single aortic leaflet (A,B,C).

CT-derived parameters

Technical aspects of CT data acquisition and AVC quantification have been described in detail previously (12,13). The AVCs were presented and analyzed in a twofold manner:

• AVC was presented as a whole mass in the stenotic valve. In this category, the analyzed parameters were assigned to two groups: morphometric (AVC size, volume and thickness in ROI and VOI presentation) and textural (AVC radiodensity).
• AVC was presented in a radial presentation. This category presents AVC orientation and distribution (topological parameters); the local AVC amount was described in each of the three sections of the valve. The aortic valve ROI projection was presented on the circular plane and was subdivided into three zones, each containing 120 1-degree slices (Figure 3A). The AVC amount was calculated radially from the stenotic valve mass center for each 1-degree slice, and the total in each of the three sections was then calculated. The AVC amount is presented in square millimeters (mm²).

Figure 3 Radial calcium presentation: (A) ROI presentation divided into three zones (120 degrees each) and radial summation of AVC amount in each zone (explanation in the text); (B) TTE image with PVL location. AVC, aortic valve calcification; PVL, paravalvular leak; ROI, region of interest; TTE, transthoracic echocardiography.

The presentation of the AVC amount in each of the three valve ROI sections allowed us to compare the local AVC amount and the location of the PVL site after TAVR (if it occurred) (Figure 3B). Additionally, 3D AVC models with a color scale depending on the degree of calcium thickness were obtained (Figure 4).

Figure 4 Exemplary AVC 3D models with a color scale representing calcium thickness. The brightest color is the thickest AVC part. (A) Separated AVC 3D models; (B) 3D models of AVC and its location in the aortic valve. 3D, 3 dimensional; AVC, aortic valve calcification.

A single case analysis using our method of AVC quantification took about 20 minutes.

The cover index was calculated from the following formula:

$$CI=\frac{P_d-A_d}{P_d}\times 100$$

where CI = cover index (-), P_d = prosthesis diameter (mm), A_d = annulus diameter (mm),

Ellipticity and eccentricity indexes were calculated in three ways: based on traditional CT measurements; based on separate quantitative AVC calculations with ROI diameter and Feret’s diameter. The ellipticity index was defined as the annulus maximum/minimum diameter. The eccentricity index was defined as 1 minus the annulus minimal diameter/maximal diameter.

Statistical analysis

Categorical variables were expressed as numbers (percentages). Continuous variables were expressed as mean ± standard deviation or median (interquartile range). The risk of PVL due to the AVC amount was assessed using the Cox method. The best multivariable model was chosen using stepwise combined regression with the Akaike information criterion as the target, with calcium locked in the model and adjusted for gender, body mass index and medical history. Risk ratios were presented with 95% confidence intervals. In the radial presentation analysis, the Cox regression model was constructed using one valve section as an observation and additional clustering of sections was considered. Bootstrap validation with 1,000 simulations was performed on the multivariable model with C-statistics as a measure of goodness of fit. ROC analysis was performed to calculate the cut-off point with the most discriminative power, then Kaplan-Meier curves with 95% CI were plotted for cases below and above the cut-off point, which were then compared using the log-rank test. To assess the agreement between traditional MSCT measurement and AVC quantitative assessment, interobserver repeatability calculations were performed using Bland-Altman analysis. All tests were 2-tailed, and a P value <0.05 was considered statistically significant. All statistical analyses were performed using JMP 14.3 (SAS Institute Inc., Cary, NC, USA, 2019) and R 3.5.3 (R Foundation for Statistical Computing, Vienna, Austria, 2019).

Results

Baseline and follow up data

Table 1 shows the baseline characteristics and baseline echocardiographic and CT data (measured using the traditional method) of the study population.

Table 1 Baseline characteristics, baseline echocardiographic and computed tomography data
Full table

During TAVR procedure, three patients required balloon postdilatation (6%), which was performed in case of PVL 2 or more or in case of prosthesis underexpansion as assessed in TTE immediately after valve deployment. No periprocedural life-threatening complications (cardiac arrest, cardiac tamponade, severe arrhythmias) were observed. Five patients required pacemaker implantation within a year of their TAVR procedures (10%). In 23 patients (46%), PVL occurred during long-term follow-up: 13 patients had PVL grade 1 (26%), 8 patients had PVL grade 2 (16%), and 2 patients had PVL grade 3 (4%).

AVC quantitative parameters

Measured using our method, the quantitative AVC parameters of the study population are shown in Table 2.

Table 2 Aortic valve calcification quantitative parameters
Full table

The univariate model shows significant association between the volume of the largest calcium block (VOI presentation), calcium perimeter and Feret’s diameter (14) (ROI presentation), and PVL occurrence after TAVR. There was no significant correlation between the remaining ROI and VOI AVC parameters and long-term perivalvular complications after TAVR (pacemaker implantation and PVL occurrence) (Table 3).

Table 3 Univariate analysis of the impact of aortic valve calcifications characteristics on paravalvular leak occurrence after transcatheter aortic valve replacement
Full table

Radial presentation-PVL prognostic model and cross-validation of the results

The multivariable model shows that AVC is an independent PVL predictor (Table 4). A 10 mm² increase in local AVC amount in each section was associated with a 9.8% (95% CI: 2%–18%; P=0.019) increase in the risk of PVL after TAVR in the corresponding area.

Table 4 Multivariable model for local paravalvular leak occurrence in radial presentation
Full table

Bootstrap validation with 1,000 simulations was performed on the multivariable model with C-statistics as a measure of goodness of fit. C-statistics was 0.7, which indicates a good model (Bootstrap validation revealed C-statistic of 0.6).

ROC analysis revealed a cut-off point of 96.5 mm² of the AVC amount assessed in radial slices. Kaplan-Meier curves showed the lowest PVL-free survival in patients with more than 96.5 mm² of calcium (P=0.013; log-rank) (Figure 5). Figure 6 shows 3D models and AVC quantification in the radial presentation of three exemplary cases. In case 1, the AVC amount in each field did not exceed the threshold. In this case, PVL formation was not observed at the 12-month follow-up. In cases 2 and 3, the AVC amount exceeded the threshold in field C in case 2, and in fields B and C in case 3. In these cases, PVL was observed at the 6-month TTE follow-up in locations corresponding to these fields in CT.

Figure 5 Kaplan-Meier curves for PVL occurrence during long-term follow-up, stratified by AVC (radial presentation). Blue: AVC amount <96.5 mm²; red: AVC amount ≥96.5 mm²; dashed lines represent 95% CI. AVC, aortic valve calcification; PVL, paravalvular leak.

Figure 6 3D models and AVC quantification in three cases (explanation in the text). 3D, 3-dimensional; AVC, aortic valve calcification.

Additionally, we assessed the agreement between traditional CT measurement and AVC quantitative assessment of ellipticity and eccentricity indexes based on ROI diameter and Feret’s diameter (Table 5 and Figure 7). Figure 8 shows Bland-Altman plots for comparison of traditional CT measurements and AVC quantitative assessment. For ellipticity index assessment, the mean relative differences were −2.99% (−6.93–0.96%) and −0.50% (−4.28–3.28%), respectively. For the eccentricity index, the mean relative differences were −16.11% (−31.08% to −1.14%) and −4.64% (−18.43% to 9.14%), respectively.

Table 5 Mean ellipticity and eccentricity indexes measured in traditional computed tomography assessment and aortic valve calcifications quantitative assessment with the use of MATLAB
Full table

Figure 7 The AVC measurements using the traditional method (A) and the Feret’s method (B). In the standard approach (A), the maximum and minimum ROI diameter are determined based on the individual judgment of the analyst, as the diameters of the most fitted circle. In the Feret’s method (B), results are selected from maximal and minimal length of the rectangle fitted in different angles. This method ensures the repeatability and the high accuracy. AVC, aortic valve calcifications; ROI, region of interest.

Figure 8 Bland-Altman plots for ellipticity index (A,B) and eccentricity index (C,D). AVC ellipticity and eccentricity indexes calculated on the basis of ROI maximal and minimal diameter; CT ellipticity and eccentricity indexes calculated on the basis of traditional CT measurements; Feret’s diameter – ellipticity/eccentricity indexes calculated on the basis of ROI and Feret’s minimal and maximal diameter. AVC, aortic valve calcification; CT, computed tomography; ROI, region of interest.

Discussion

Preoperative CT assessment of patients with severe AS who have qualified for TAVR is obligatory and enables accurate procedure planning and correct device selection. CT assessment makes it possible to reduce the number of postoperative complications such as the occurrence of PVL (15-20). Severe AVC is one of the factors that makes PVL more likely after TAVR, which is why it is obligatory to assess AVC degree in CT (21-23).

Pre-TAVR AVC assessment in CT is based on a semi-quantitative method (Agatston score) or subjective visual assessment of CT data. For now, there is no prognostic model and no consensus on a specific imaging biomarker that would allow for unambiguous, objective identification of patients with a high risk of PVL. The authors of only a few previous studies have attempted to establish an objective, quantitative methodology of AVC description (24,25). Single reports have indicated the role of AVC distribution in local PVL formation (26,27). On the other hand, these reports show a lack of a harmonized, objective AVC assessment method.

We present a unique method of objective AVC assessment and its usefulness in patients who have qualified for TAVR. By building a database of quantitative AVC parameters, we specify parameters that allow estimation of high PVL formation after TAVR (largest calcium block volume, calcium perimeter, Feret’s diameter). Additionally, radial presentation makes it possible to identify the exact location of higher probability of PVL occurrence in relation to the valve ring.

Initial assessment shows that our method has very good agreement with traditional CT measurements. Additionally, a single case analysis is quick, thus it is possible to implement our method in real-time assessment. The predictive model for PVL occurrence based on imaging and clinical data is cross validated.

In the future, our work may affect diagnostic and therapeutic pathways in patients qualified for TAVR. Firstly, based on mathematical data, we will be able to list a group of patients with an increased likelihood of PVL formation. This group of patients will require increased observation both in the perioperative period and in long-term follow-ups. The first difference in the rate of PVL formation between patients with an AVC amount below and above 96.5 mm² was noticed after just 1 month of follow-up. This could be very important information for physicians to do the first TTE within one month of TAVR for patients with higher AVC. Secondly, localization of the problematic area of the valve may have an impact on valve prosthesis selection. Additionally, the combination of radial presentation data with the more frequently described personalized simulations of the TAVR procedure (28-31) may affect both device selection and design.

Considering the increase in the popularity of TAVR, the relatively high PVL occurrence after TAVR, and the increase in mortality in patients with moderate to severe PVL, careful assessment of PVL predictors is required. In the future, the use of quantitative AVC parameters as PVL imaging biomarkers in TAVR patients may help to identify high PVL risk patients and may have an impact on device design and selection. These may impact the planning of the therapeutic and control path before and after TAVR in high PVL risk patients. To achieve these goals, the creation and extension of an online database containing quantitative AVC parameters is needed.

Study limitations

The main limitation of our study is the relatively small sample size and the lack of postprocedural PVL evaluation in transesophageal echocardiography, which is a reference method. Another limitation is the lack of the exact date of PVL formation as the patients were followed up on prescheduled dates on which only the PVL was revealed.

Most of the literature data regarding aortic valve calcium score refer to measurements based on non-contrast CT scans, which should be the reference method for AVC assessment. The quality of contrast enhanced CTs results from the interaction of multiple variables related to patient, scanner and contrast medium. Unlike non-contrast scans, the threshold for calcium detection on contrast-enhanced CT scans has not been standardized in the published reports. Both fixed-intensity values as well as dynamic thresholds related to luminal attenuation in the ascending aorta have been suggested (32). In our study, the threshold cut-off value was set individually (391–689 HU) for each case and was adopted due to contrast medium attenuation in the aortic root. Aortic valvular calcium score assessment based on contrast-enhanced CT is not a recommended method, but we chose to perform our measurements on these data because of their prevalence in TAVR patients. Contrast-enhanced CT is performed during preoperative assessment of every TAVR patient, and the use of this data for AVC evaluation carries no risk of additional radiation for these patients. Additionally, some studies have shown that AVC assessment in contrast-enhanced CT has good agreement with non-contrast MSCT (33).

Conclusions

Detailed, quantitative AVC assessment for PVL prediction may play an important role in screening before TAVR. In future, the use of quantitative AVC assessment as an imaging biomarker in TAVR candidates and the creation and extension of an online database containing quantitative AVC parameters may help to identify high PVL risk patients and may affect the therapeutic and diagnostic pathways of AS patients.

Acknowledgments

Funding: None.

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-669). The authors have no conflicts of interest to declare.

Ethical Statement: The study was approved by the Ethical Board of the Jagiellonian University Medical College, Krakow, Poland (1072.6120.234.2017). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki with later amendments. Written informed consent was obtained from the patient for publication of this study and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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