A systematic review of contrast-enhanced computed tomography calcium scoring methodologies and impact of aortic valve calcium burden on TAVI clinical outcomes

Different methodologies have been used to assess the role of AV calcification (AVC) on TAVI outcomes. This systematic review aims to describe the burden of AVC, synthesize the different methods of calcium score quantification, and evaluate the impact of AVC on outcomes after TAVI. We included studies of TAVI patients who had reported AV calcium scoring by contrast-enhanced multidetector CT and the Agatston method. The impact of calcification on TAVI outcomes without restrictions on follow-up time or outcome type was evaluated. Results were reported descriptively, and a meta-analysis was conducted when feasible. Sixty-eight articles were included, with sample sizes ranging from 23 to 1425 patients. Contrast-enhanced calcium scoring was reported in 30 studies, calcium volume score in 28 studies, and unique scoring methods in two. All studies with calcium volume scores had variable protocols, but most utilized a modified Agatston method with variable attenuation threshold values of 300-850 HU. Eight studies used the Agatston method, with the overall mean AV calcium score in studies published from 2010 to 2012 of 3342.9 AU [95%CI: 3150.4; 3535.4, I2 ​= ​0%]. The overall mean score was lower and heterogenous in studies published from 2014 to 2020 (2658.9 AU [95% CI: 2517.3; 2800.5, I2 ​= ​79%]. Most studies reported a positive association between calcium burden and increased risk of adverse outcomes, including implantation of permanent pacemaker (7/8 studies), paravalvular leak (13/13 studies), and risk of aortic rupture (2/2 studies). AVC quantification methodology with contrast-enhanced CT is still variable. AVC negatively impacts TAVI outcomes independently of the quantification method.

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

Background

Paravalvular leak (PVL) is one of the most common complications of transcatheter aortic valve replacement (TAVR) and affects short- and long-term outcomes. The aim of this study was to identify the computed tomography (CT) imaging biomarkers that allow PVL after TAVR to be predicted.

Methods

Patients were included who had severe aortic valve stenosis, had undergone TAVR with a self-expanding valve, and had undergone a pre-procedural CT scan. Data on baseline characteristics, procedural and long-term outcomes were collected retrospectively. We used MATLAB software with a self-developed algorithm for CT scan analysis and found parameters that quantified aortic valve calcifications (AVC) in detail.

Results

Fifty patients were included. The identified CT-derived parameters included AVC size, volume, thickness and density, as well as calcium radial distribution. The volume of the largest calcium block, calcium perimeter and calcium size (assessed by Feret's diameter) showed a strong association with PVL occurrence after TAVR (P=0.012, P=0.001 and P=0.045, respectively). The prognostic model showed that a 10 mm² increase in the local AVC amount in each valve section was associated with a 9.8% (95% CI: 2-18%; P=0.019) increase in the risk of PVL occurrence in the corresponding area after TAVR. ROC analysis revealed that the cut-off point of the AVC area was 96.5 mm² in the polar coordinate system presentation. Kaplan-Meier curves showed worse PVL-free survival in patients with more than 96.5 mm² of calcium area (P=0.013; log-rank).

Conclusions

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.

How valvular calcification can affect the outcomes of transcatheter aortic valve implantation

Introduction In transcatheter aortic valve implantation (TAVI), assessment of aortic valve calcification is not as standardized as aortic annulus measurement. Aortic valve calcification is important for stable anchoring of the prosthesis to the aortic annulus. However, excessive aortic valve calcification is related to procedural complications. Areas covered This review covers the methods to assess aortic valve calcification and the implications of aortic valve calcium burden for TAVI outcomes. We performed a systematic review of the literature in Pubmed and secondary sources. Furthermore, future perspectives on how to integrate aortic valve calcification assessment in the management of patients with aortic stenosis is discussed. Expert opinion Thorough assessment of the aortic valve and aortic root components including aortic valve calcification is key in the planning of TAVI. Aortic valve calcification load, location and extension are important contributors to paravalvular regurgitation. Asymmetric calcification burden with greater calcification of the left-coronary cusp related to higher need of permanent pacemaker implantation. Patients with moderate and severe left ventricular outflow tract/subannular calcification are more susceptible to aortic annular rupture. Periprocedural dislodgement of calcium form cusps and commissures is one of the main reasons of coronary artery ostial occlusion during transcatheter aortic valve implantation. Abbreviations Ao, aorta; LA, left atrium; LAA, left atrial appendage; LV, left ventricle; LVOT, left ventricular outflow tract; THV, transcatheter heart valve.

Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule

BACKGROUND

Progression of aortic valve calcifications (AVC) leads to aortic valve stenosis (AS). Importantly, the AVC degree has a great impact on AS progression, treatment selection and outcomes. Methods of AVC assessment do not provide accurate quantitative evaluation and analysis of calcium distribution and deposition in a repetitive manner.

OBJECTIVE

We aim to prepare a reliable tool for detailed AVC pattern analysis with quantitative parameters.

METHODS

We analyzed computed tomography (CT) scans of fifty patients with severe AS using a 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 with a self-developed algorithm.

RESULTS

We listed unique parameters describing AVC and prepared 3D AVC models with color pointed calcium layer thickness in the stenotic aortic valve. These parameters were derived from CT-images in a semi-automated and repeatable manner. They were divided into morphometric, topological and textural parameters and may yield crucial information about the anatomy of the stenotic aortic valve.

CONCLUSION

In our study, we were able to obtain and define quantitative parameters for calcium assessment of the degenerated aortic valves. Whether the defined parameters are able to predict potential long-term outcomes after treatment, requires further investigation.

A novel approach to quantification of aortic valve calcifications in patients undergoing transcatheter aortic valve implantation

BACKGROUND

Precise calcium evaluation in the aortic complex may be complicated. We aimed to assess the usefulness of a novel semi-automatic algorithm for multi slice computed tomography-derived (MSCT) quantitative estimation of aortic valve calcifications (AVC) in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation (TAVI).

METHODS

Ten patients with severe AS who underwent TAVI with Edwards Sapien S3 26 mm prosthesis and had a pre-procedural MSCT scan were included. Data on baseline characteristics, procedural and long-term outcomes were collected prospectively. Pre-procedural MSCT data were used for AVC evaluation with 3D modeling (calcium volume, thickness, area, density, and distribution) in a dedicated program.

RESULTS

Mean calcium thickness was 4.6 (3.6-5.8) mm. Median calcium are 333.6 (274.7-386.7) mm2. We found a significant correlation between larger maximal calcium layer thickness and PVL occurrence after TAVI (P=0.039). The radial representation of the calcium distribution allowed to divide aortic valve into 3 zones and to compare each zone to parallel zone on TTE images. In zones with PVL ≥2 mean AVC was higher than in zones with PVL <2 (7354.6±4020.4 pixels vs. 4325.1±1790.6 pixels; P=0.018). Based on ROC analysis, the optimal cut-off value of AVC to predict PVL ≥2 was >6506 pixels with 57.1% sensitivity and 90.5% specificity (AUC 0.762 [95% CI: 0.564 to 0.901], P=0.029).

CONCLUSIONS

Multiplane AVC quantitative evaluation provided details on total calcium amount, pattern and distribution in aortic valve. Established AVC parameters allowed better visualization of an operating area and prediction of PVL after TAVI.

Aortic Valve Calcium Volume Predicts Paravalvular Regurgitation and the Need for Balloon Post-Dilatation After Transcatheter Aortic Valve Implantation

OBJECTIVE

This study sought to evaluate the impact of aortic valve (AV) and left ventricle outflow tract (LVOT) calcium on paravalvular regurgitation (PVR) and need for balloon post-dilatation (BPD) during transcatheter aortic valve implantation (TAVI).

METHODS

The overall study population comprised 152 patients. Calcium mass and volume of AV and LVOT were estimated from contrast-enhanced multislice computed tomography imaging, using 3 thresholds for calcium detection [650, 850, and 1,050 Hounsfield units (HU)].

RESULTS

A self-expandable prosthesis was implanted in 67.8% of patients and a balloon-expandable prosthesis in the remaining. Eleven patients required BPD and 82 patients presented post-procedural PVR, which was mild in 44.1% and moderate in 9.9%. The greatest discriminatory value for PVR ≥ mild was seen for calcium volume using 850 HU threshold, with an area under the curve of 0.72 (95%CI 0.64-0.80, P < 0.001) for AV and of 0.63 (95%CI 0.54-0.72, P = 0.008) for LVOT. For 850 HU threshold, the calcium volume cut-off with the highest sum of sensitivity and specificity for PVR was 157 mm(3) for AV and 0.6 mm(3) for LVOT. In multivariate logistic regression analysis, the presence of AV calcium ≥157 mm(3) (OR 3.83, 95%CI 1.81-8.10, P < 0.001) and ≥267 mm(3) (OR 11.3, 95%CI 1.2-103.1, P = 0.03) were the only independent predictors of PVR and BPD, respectively.

CONCLUSIONS

AV calcium volume was an independent predictor of PVR and BPD in patients submitted to TAVI. Our results support a systematic assessment of AV calcium volume to identify patients at increased risk of post-procedural PVR.