Quantifying Aortic Valve Calcification using Coronary Computed Tomography Angiography

Robust automated calcification meshing for personalized cardiovascular biomechanics

Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcium deposits on cardiovascular structures are still often manually reconstructed for physics-driven simulations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated image-to-mesh algorithm that enables robust incorporation of patient-specific calcification onto a given cardiovascular tissue mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to ~1 min of automated computation, and it solves an important problem that cannot be addressed with recent template-based meshing techniques. We validated our final calcified tissue meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of personalized cardiovascular biomechanics.

Quantification of Aortic Valve Calcification in Contrast-Enhanced Computed Tomography

Background: The goal of our study is to evaluate a method to quantify aortic valve calcification (AVC) in contrast-enhanced computed tomography for patients with suspected severe aortic stenosis pre-interventionally. Methods: A total of sixty-five patients with aortic stenosis underwent both a native and a contrast-enhanced computed tomography (CECT) scan of the aortic valve (45 in the training cohort and 20 in the validation cohort) using a standardized protocol. Aortic valve calcification was semi-automatically quantified via the Agatston score method for the native scans and was used as a reference. For contrast-enhanced computed tomography, a calcium threshold of the Hounsfield units of the aorta plus four times the standard deviation was used. Results: For the quantification of aortic valve calcification in contrast-enhanced computed tomography, a conversion formula (691 + 1.83 x AVCCECT) was derived via a linear regression model in the training cohort. The validation in the second cohort showed high agreement for this conversion formula with no significant proportional bias (Bland-Altman, p = 0.055) and with an intraclass correlation coefficient in the validation cohort of 0.915 (confidence interval 95% 0.786-0.966) p < 0.001. Conclusions: Calcium scoring in patients with aortic valve stenosis can be performed using contrast-enhanced computed tomography with high validity. Using a conversion factor led to an excellent agreement, thereby obviating an additional native computed tomography scan. This might contribute to a decrease in radiation exposure.

Aortic Valve Calcium Score by Computed Tomography as an Adjunct to Echocardiographic Assessment-A Review of Clinical Utility and Applications

Aortic valve stenosis (AS) is increasing in prevalence due to the aging population, and severe AS is associated with significant morbidity and mortality. Echocardiography remains the mainstay for the initial detection and diagnosis of AS, as well as for grading of severity. However, there are important subgroups of patients, for example, patients with low-flow low-gradient or paradoxical low-gradient AS, where quantification of severity of AS is challenging by echocardiography and underestimation of severity may delay appropriate management and impart a worse prognosis. Aortic valve calcium score by computed tomography has emerged as a useful clinical diagnostic test that is complimentary to echocardiography, particularly in cases where there may be conflicting data or clinical uncertainty about the degree of AS. In these situations, aortic valve calcium scoring may help re-stratify grading of severity and, therefore, further direct clinical management. This review presents the evolution of aortic valve calcium score by computed tomography, its diagnostic and prognostic value, as well as its utility in clinical care.

Thickness and Volume of Epicardial Adipose Tissue in Relation to Stiffness and Elasticity of Aorta Assessed by Computed Tomography Angiography

PURPOSE

The aim of the study was to assess the importance of the measurements of thickness and volume of epicardial adipose tissue (EAT) in coronary computed tomography angiography (CCTA) as a predictive factor of increased stiffness and impaired elasticity of aorta.

METHODS AND MATERIALS

The study involved a group of 97 patients (63.48 ± 8.50 years). In accordance with the medians of epicardial adipose tissue (EAT) parameters, aortic elasticity and stiffness parameters, patients were divided into subgroups: EAT thickness median 9.40 mm, EAT volume median 61.95 mL, EAT thickness index 5.08 mm/m² and EAT volume index 34.33 mL/m².

RESULTS

The mean coronary artery calcium score was 162.24 (±317.69). The mean aortic stiffness index was 4.18 (±0.81). The assessed mean aortic elasticity parameters were 3.29% (±2.37) and 0.12 cm²/dyn (±0.09) for strain and distensibility, respectively. A positive linear correlation was observed between EAT parameters and aortic stiffness (0.21), volume (0.51), thickness index (0.24), volume index (0.55) and, for aorta elasticity, a negative linear correlation between the following EAT parameters was observed: thickness (-0.32 and -0.30), volume (-0.49 and -0.48), thickness index (-0.34 and -0.31), volume index (-0.51 and -0.49) and aortic elasticity parameters (aorta strain and aorta distensibility, respectively).

CONCLUSION

The study showed that CCTA illustrates a relationship between the parameters of EAT and an increased stiffness of the aorta, while the most predictive factor of stiffness was the volume index.

A Doppler-exclusive non-invasive computational diagnostic framework for personalized transcatheter aortic valve replacement

Given the associated risks with transcatheter aortic valve replacement (TAVR), it is crucial to determine how the implant will affect the valve dynamics and cardiac function, and if TAVR will improve or worsen the outcome of the patient. Effective treatment strategies, indeed, rely heavily on the complete understanding of the valve dynamics. We developed an innovative Doppler-exclusive non-invasive computational framework that can function as a diagnostic tool to assess valve dynamics in patients with aortic stenosis in both pre- and post-TAVR status. Clinical Doppler pressure was reduced by TAVR (52.2 ± 20.4 vs. 17.3 ± 13.8 [mmHg], p < 0.001), but it was not always accompanied by improvements in valve dynamics and left ventricle (LV) hemodynamics metrics. TAVR had no effect on LV workload in 4 patients, and LV workload post-TAVR significantly rose in 4 other patients. Despite the group level improvements in maximum LV pressure (166.4 ± 32.2 vs 131.4 ± 16.9 [mmHg], p < 0.05), only 5 of the 12 patients (41%) had a decrease in LV pressure. Moreover, TAVR did not always improve valve dynamics. TAVR did not necessarily result in a decrease (in 9 out of 12 patients investigated in this study) in major principal stress on the aortic valve leaflets which is one of the main contributors in valve degeneration and, consequently, failure of heart valves. Diastolic stresses increased significantly post-TAVR (34%, 109% and 81%, p < 0.001) for each left, right and non-coronary leaflets respectively. Moreover, we quantified the stiffness and material properties of aortic valve leaflets which correspond with the reduced calcified region average stiffness among leaflets (66%, 74% and 62%; p < 0.001; N = 12). Valve dynamics post-intervention should be quantified and monitored to ensure the improvement of patient conditions and prevent any further complications. Improper evaluation of biomechanical valve features pre-intervention as well as post-intervention may result in harmful effects post-TAVR in patients including paravalvular leaks, valve degeneration, failure of TAVR and heart failure.

An ultrasound-exclusive non-invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis

Aortic stenosis (AS) is an acute and chronic cardiovascular disease and If left untreated, 50% of these patients will die within two years of developing symptoms. AS is characterized as the stiffening of the aortic valve leaflets which restricts their motion and prevents the proper opening under transvalvular pressure. Assessments of the valve dynamics, if available, would provide valuable information about the patient's state of cardiac deterioration as well as heart recovery and can have incredible impacts on patient care, planning interventions and making critical clinical decisions with life-threatening risks. Despite remarkable advancements in medical imaging, there are no clinical tools available to quantify valve dynamics invasively or noninvasively. In this study, we developed a highly innovative ultrasound-based non-invasive computational framework that can function as a diagnostic tool to assess valve dynamics (e.g. transient 3-D distribution of stress and displacement, 3-D deformed shape of leaflets, geometric orifice area and angular positions of leaflets) for patients with AS at no risk to the patients. Such a diagnostic tool considers the local valve dynamics and the global circulatory system to provide a platform for testing the intervention scenarios and evaluating their effects. We used clinical data of 12 patients with AS not only to validate the proposed framework but also to demonstrate its diagnostic abilities by providing novel analyses and interpretations of clinical data in both pre and post intervention states. We used transthoracic echocardiogram (TTE) data for the developments and transesophageal echocardiography (TEE) data for validation.

ACURATE neo2 versus SAPIEN 3 Ultra for transcatheter aortic valve implantation

BACKGROUND

No comparative data exist with the latest generation self-expanding ACURATE neo2 (Neo2) and the balloon-expandable SAPIEN 3 Ultra (Ultra) transcatheter heart valves (THV).

AIMS

We aimed to compare the outcomes after transcatheter aortic valve implantation (TAVI) using the Neo2 and the Ultra THV.

METHODS

A total of 1,356 patients at 4 centres were treated either with the Neo2 (n=608) or the Ultra (n=748). The primary endpoint was device success according to the latest Valve Academic Research Consortium definitions. The association of the THV used and the primary endpoint was assessed using inverse probability treatment weighting (IPTW) and 1:1 propensity score matching (PSM), which identified 472 matched pairs.  Results: After PSM, there were no relevant differences between the groups. While rates of moderate to severe paravalvular leakage (PVL) were overall low (0.6% vs 1.1%; p=0.725), elevated transvalvular gradients (≥20 mmHg) were less frequent with the Neo2 (2.4% vs 7.7%; p<0.001), which translated into a significantly higher rate of device success with the Neo2 compared with the Ultra (91.9% vs 85.0%; p<0.001). Consistently, the Neo2 was associated with higher rates of device success in the IPTW analysis (odds ratio [OR] 1.961, 95% confidence interval [CI]: 1.269-3.031; p=0.002). Rates of mild PVL were significantly lower with the Ultra compared with the Neo2 (20.0% vs 32.8%; p<0.001). Clinical events at 30 days were comparable between the 2 groups.

CONCLUSIONS

Short-term outcomes after TAVI using the Neo2 or Ultra THV were excellent and, overall, comparable. However, transvalvular gradients were lower with the Neo2, which translated into higher rates of device success. Rates of mild PVL were significantly lower with the Ultra THV.

A Novel Two-Dimensional Echocardiography Method to Objectively Quantify Aortic Valve Calcium and Predict Aortic Stenosis Severity

Aortic valve calcium (AVC) is a strong predictor of aortic stenosis (AS) severity and is typically calculated by multidetector computed tomography (MDCT). We propose a novel method using pixel density quantification software to objectively quantify AVC by two-dimensional (2D) transthoracic echocardiography (TTE) and distinguish severe from non-severe AS. A total of 90 patients (mean age 76 ± 10 years, 75% male, mean AV gradient 32 ± 11 mmHg, peak AV velocity 3.6 ± 0.6 m/s, AV area (AVA) 1.0 ± 0.3 cm², dimensionless index (DI) 0.27 ± 0.08) with suspected severe aortic stenosis undergoing 2D echocardiography were retrospectively evaluated. Parasternal short axis aortic valve views were used to calculate a gain-independent ratio between the average pixel density of the entire aortic valve in short axis at end diastole and the average pixel density of the aortic annulus in short axis (2D-AVC ratio). The 2D-AVC ratio was compared to echocardiographic hemodynamic parameters associated with AS, MDCT AVC quantification, and expert reader interpretation of AS severity based on echocardiographic AVC interpretation. The 2D-AVC ratio exhibited strong correlations with mean AV gradient (r = 0.72, p < 0.001), peak AV velocity (r = 0.74, p < 0.001), AVC quantified by MDCT (r = 0.71, p <0.001) and excellent accuracy in distinguishing severe from non-severe AS (area under the curve = 0.93). Conversely, expert reader interpretation of AS severity based on echocardiographic AVC was not significantly related to AV mean gradient (t = 0.23, p = 0.64), AVA (t = 2.94, p = 0.11), peak velocity (t = 0.59, p = 0.46), or DI (t = 0.02, p = 0.89). In conclusion, these data suggest that the 2D-AVC ratio may be a complementary method for AS severity adjudication that is readily quantifiable at time of TTE.

Head-to-Head Comparison of Different Software Solutions for AVC Quantification Using Contrast-Enhanced MDCT

Aortic valve calcification (AVC) in aortic stenosis patients has diagnostic and prognostic implications. Little is known about the interchangeability of AVC obtained from different multidetector computed tomography (MDCT) software solutions. Contrast-enhanced MDCT data sets of 50 randomly selected aortic stenosis patients were analysed using three different software vendors (3Mensio, CVI42, Syngo.Via). A subset of 10 patients were analysed twice for the estimation of intra-observer variability. Intra- and inter-observer variability were determined using the ICC reliability method, Bland-Altman analysis and coefficients of variation. No differences were revealed between the software solutions in the AVC calculations (3Mensio 941 ± 623, Syngo.Via 948 mm³ ± 655, CVI42 941 ± 637; p = 0.455). The best inter-vendor agreement was found between the CVI42 and the Syngo.Via (ICC 0.997 (CI 0.995-0.998)), followed by the 3Mensio and the CVI42 (ICC 0.996 (CI 0.922-0.998)), and the 3Mensio and the Syngo.Via (ICC 0.992 (CI 0.986-0.995)). There was excellent intra- (3Mensio: ICC 0.999 (0.995-1.000); CVI42: ICC 1.000 (0.999-1.000); Syngo.Via: ICC 0.998 (0.993-1.000)) and inter-observer variability (3Mensio: ICC 1.000 (0.999-1.000); CVI42: ICC 1.000 (1.000-1.000); Syngo.Via: ICC 0.996 (0.985-0.999)) for all software types. Contrast-enhanced MDCT-derived AVC scores are interchangeable between and reproducible within different commercially available software solutions. This is important since sufficient reproducibility, interchangeability and valid results represent prerequisites for accurate TAVR planning and its widespread clinical use.

Estimation of Aortic Valve Calcium Score Based on Angiographic Phase Versus Reduction of Ionizing Radiation Dose in Computed Tomography

The aim of the study was to evaluate the estimation efficacy of aortic valve calcium score (AVCS) based on the multislice computed tomography (MSCT) angiographic phase. The evaluation of the reduced amount of ionizing radiation dose was performed because of this estimation. The study included 51 consecutive patients who qualified for transcatheter aortic valve implantation (TAVI) (78.59 ± 5.72 years). All subjects underwent MSCT: in the native phase dedicated to AVCS as well as angiographic phases aimed to morphologically assess the aortic ostium and arterial accesses for TAVI. Based on the native phase, an AVCS assessment was performed for axial reconstructions at 3.0 mm and 2.0 mm slice thickness (AVCS_native3.0 and AVCS_native2.0). Based on the angiographic phase AVCS was estimated for axial reconstruction at 0.6 mm slice thickness with increased values of lesion density in aortic valve cusps/aortic valve annulus, which is considered a calcification, from a typical value of 130 HU to 500 HU and 600 HU (AVCS_{CTA0.6 500 HU} and AVCS_{CTA0.6 600 HU}). Mathematical formulations were developed, allowing for AVCS native calculation based on AVCS values estimated based on the angiographic phase: AVCS_native3.0 = 813.920 + 1.510 AVCS_{CTA0.6 500 HU}; AVCS_native3.0 = 1235.863 + 1.817 AVCS_{CTA0.6 600 HU}; AVCS_native2.0 = 797.471 + 1.393 AVCS_{CTA0.6 500 HU}; AVCS_native2.0 = 1228.310 + 1.650 AVCS_{CTA0.6 600 HU}. The amount of a potential reduction in dose length product (DLP) in the case of AVCS estimation was 4.45 ± 1.54%. In summary, relying solely on the angiographic phase of MSCT examination before TAVI, it is possible to conclusively estimate AVCS. This estimation results in a marked reduction in radiation dose in MSCT.

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.

New insights on potential permanent pacemaker predictors in TAVR using the largest self-expandable device

Background

Post-procedural conduction disorders following transcatheter aortic valve replacement (TAVR) still remain frequent, especially using the largest self-expandable device (Medtronic Corevalve Evolut R^TM, 34 mm, STHV-34). We, therefore, assessed previously described, predictive factors of permanent pacemaker (PPM) implantation in the context of the STHV-34, including calcification distribution, implantation depth and membranous septum length (MSL).

Methods

We performed a dual centre analysis of 130 of 182 consecutive patients treated with STHV-34, further stratified into subjects without post-procedural PPM (-PPM n=100, 76.9%) and those requiring post-procedural PPM (+PPM n=30, 23.1%). These events were further analyzed by univariate and multivariate analysis according to several underlying conditions.

Results

Multivariate analysis only depicted previous right bundle branch block [RBBB; OR: 11.52 (2.63-50.44), P=0.001] and eccentricity index of the left ventricular outflow tract (LVOT-EI) >0.3 [OR: 3.07 (1.22-7.77), P=0.018] as highly predictive for PPM-need, being also confirmed by c-statistics [area under the curve (AUC) =0.68; 95% confidence interval (CI): 0.57-0.80; P=0.0025]. There was only moderate correlation of implantation depth over the MSL in terms of PPM prediction (r=0.23; P<0.0001).

Conclusions

This study offers new insights into potential PPM predictors using the STHV-34: previous RBBB and a pronounced LVOT-EI were independent predictors of PPM, while most of the previously reported determinants failed to predict PPM-need including MSL and implantation depth.

Clinical impact of mitral calcium volume in patients undergoing transcatheter aortic valve implantation

BACKGROUND

Mitral annular calcification (MAC) has been associated with mitral valve (MV) disease and cardiovascular events in patients undergoing transcatheter aortic valve implantation (TAVI). We aimed to investigate the incidence and impact of mitral calcium volume (MCV) quantified by multidetector computed tomography (MDCT) on MV function and clinical outcomes after TAVI.

METHODS

Consecutive patients with exploitable echocardiography and MDCT performed during TAVI screening were enrolled in this retrospective analysis. Mitral calcium was assessed visually and measured using a semi-automatic tool developed for the aortic valve in an off-label fashion.

RESULTS

MCV >0 mm³ was found in 65% of the 875 included patients. Patients with calcification were older (82 ± 6 versus 81 ± 7; P = 0.002) and had high prevalence of renal dysfunction (69% versus 61%; P = 0.017) and mitral stenosis (25% versus 4%, P < 0.001). MCV correlated well with visual MAC severity (r = 0.94; P < 0.001), but showed a greater predictive value for mitral stenosis (AUC = 0.804 vs. 0.780, P = 0.012) , while it was not a predictor of mitral regurgitation (AUC = 0.514). Correlations were found between MCV and echocardiographic parameters including MV area, mean transmitral gradient, and pressure half-time (P < 0.001 for all). MCV did not impact on cardiovascular mortality or new permanent pacemaker implantation after TAVI.

CONCLUSIONS

Calcification of the mitral apparatus is common in TAVI candidates and results in mitral stenosis in 25% of the patients. Increasing MCV predicts mitral stenosis, but had no impact on clinical outcomes following TAVI.

CLINICAL TRIAL REGISTRATION

NCT01368250.

Aortic valve calcification is subject to aortic stenosis severity and the underlying flow pattern

Sex- and flow-related aortic valve calcification (AVC) studies are still limited in number, and data on the exact calcium quantity and distribution are scarce. Therefore, we aimed to (1) re-define the best threshold of AVC load to distinguish severe from moderate aortic stenosis (AS) in common AS entities and to (2) evaluate differences in the aortic annulus and left ventricular outflow tract (LVOT) calcium load. Nine hundred and thirty-eight patients with contrast-enhanced cardiac MSCT and moderate-to-severe aortic stenosis (AS) were retrospectively enrolled. Patients with severe AS ≤ 1.0 cm² (n = 841) were further separated into three AS entities: high gradient (HGAS, n = 370, 44.0%), paradoxical low gradient (pLGAS, n = 333, 39.6%), and classical low gradient (LGAS, n = 138, 16.4%). AVC, leaflet, and LVOT calcification were quantified. Aortic valve calcification scores were highest in severe HGAS, and lower in severe pLGAS and classical LGAS. In all severity and AS entities, the non-coronary cusp (NCC) was the most calcified one. LVOT calcification was consistently comparable between gender and AS entities. Accuracy of logistic regression was the highest in HGAS (male vs. female: AVC > 2156 Agatston units (AU), c-index 0.76; vs. AVC > 1292 AU, c-index 0.85; or AVC density > 406 AU/cm², c-index 0.82; vs. > 259 AU/cm², c-index 0.86; each p < 0.0001*) to diagnose severe AS. AVC could only be used in men to differentiate between severe LGAS and moderate AS. Data from this retrospective analysis indicate that the NCC is subject to pre-dominant degeneration throughout gender, AS severity, and several AS entities. AVC was consistently comparable in severe pLGAS and classical LGAS, but only AVC in severe LGAS could sufficiently distinguish from moderate AS in men. LVOT calcification failed to be a reliable indicator of accelerating AS.

Association of Time Between Left Ventricular and Aortic Systolic Pressure Peaks With Severity of Aortic Stenosis and Calcification of Aortic Valve

Importance

Diagnosis of low-gradient severe aortic stenosis (AS) is challenging. We hypothesized that the time between left ventricular (LV) and aortic systolic pressure peaks (TLV-Ao) is associated with aortic stenosis (AS) severity and may have additive value in diagnosing severe AS, especially in patients with low-gradient AS.

Objective

To investigate the diagnostic utility of measuring catheter-based TLV-Ao in patients with severe AS.

Design, Setting, and Participants

We studied 123 patients with severe AS at the Cleveland Clinic Foundation, a tertiary referral center, who underwent transcatheter aortic valve replacement (TAVR) via femoral access and had pre-TAVR cardiac computed tomography assessment and hemodynamic measurements recorded during a TAVR procedure. All patients received hemodynamic evaluation, echocardiographic assessment, and quantification of aortic valve calcification (AVC) by multidetector computed tomography. Hemodynamic data were collected via left heart catheterization done just before TAVR, and TLV-Ao was calculated offline. Data were analyzed between October 5, 2015, and July 20, 2016.

Main Outcomes and Measures

The association between TLV-Ao and AVC or other conventional imaging parameters was analyzed.

Results

Of the included patients, the mean (SD) age was 81 (9) years, and 65 (54%) were men (54%). Among 123 patients, 48 patients (39%) had low-gradient AS (<40 mm Hg) and mean (SD) TLV-Ao was 69 (39) milliseconds. In multivariable logistic regression analyses, higher TLV-Ao (odds ratio [OR], 1.02; 95% CI, 1.01-1.04; P = .002) and higher peak aortic valve (AV) velocity (OR, 1.01; 95% CI, 1.00-1.02; P = .008) were independently associated with severe AVC (AVC >1000 AU). Adding TLV-Ao to the peak AV velocity and AV area showed significant incremental value to be associated with AVC, with a net reclassification improvement of 0.61 (95% CI, 0.23-0.99; P = .002) and integrated discriminatory improvement of 0.09 (95% CI, 0.03-0.16; P = .003). In a subgroup of patients with low-grade AS, higher TLV-Ao was the only parameter associated with severe AVC (OR, 1.02; 95% CI, 1.001-1.04; P = .03).

Conclusions and Relevance

Prolonged TLV-Ao was associated with severe AVC. This catheter-based hemodynamic index may be an additional surrogate to differentiate low-gradient true severe AS. Larger, prospective studies investigating the role of TLV-Ao as a marker of clinical outcomes in patients undergoing TAVR are required.

Why and How to Measure Aortic Valve Calcification in Patients With Aortic Stenosis

The first-line evaluation of aortic stenosis severity is Doppler echocardiography. However, in up to 40% of patients, resting echocardiographic assessment of aortic stenosis severity is discordant, leading to clinical uncertainty. Interest has therefore grown in aortic valve calcium scoring by multidetector computed tomography (CT-AVC) as an alternative load independent assessment of aortic stenosis severity. This paper will briefly review the pathophysiology of aortic stenosis and the crucial role that calcification plays in driving progressive obstruction of the valve. Subsequently, it will describe published reports that have investigated CT-AVC, validating this parameter against histology, and establishing its diagnostic accuracy versus echocardiography as well as its powerful independent prognostic capability. Finally, this review seeks to provide a practical guide about how best to acquire and interpret CT-AVC with a close focus on potential pitfalls and how these might be best avoided as this technique becomes more widely adopted in to clinical practice.

Impact of aortic valve calcification severity on device success after transcatheter aortic valve replacement

Aortic valvular calcium score (AVCS) can identify severe aortic stenosis (AS) and provide powerful prognostic information. In severe and symptomatic AS, patients can be referred for a transcatheter aortic valve replacement (TAVR). The aim of this study was to determine whether AVCS, measured on the preoperative contrast enhanced multislice computed tomography (MSCT), is associated with device success (DS), major adverse cardiac events (MACEs) and paravalvular leak (PVL) after TAVR. Three hundred and fifty-two consecutive patients who underwent TAVR with a preoperative standardised contrast enhanced MSCT were included in the study. Valvular calcification detection was defined by adding + 100 Hounsfield Unit (HU) to mean HU determined by a region of interest placed in the contrast enhanced ascending aorta. AVCS was then indexed to the aortic annulus surface (AVCSi). Endpoints were DS and 30-day MACE according to Valve Academic Research Consortium-2 consensus document, and moderate to severe PVL. DS was obtained for 305 patients. In multivariate analysis, AVCSi was negatively and independently associated with DS: OR = 0.99, 95% CI 0.99-0.99, p = 0.03. In the subgroup analysis, this association was particularly relevant with self-expanding prostheses [n = 151 (43%), p = 0.018] and in the cases of asymmetric calcium valvular distribution [n = 283 (80%), p 0.002]. There was no association between MACE and AVCS (p = 0.953) and AVCSi (p = 0.757). PVL was positively associated with AVCS (p < 0.001) and AVCSi (p < 0.001). In conclusion, in TAVR, AVCS, measured on preoperative contrast enhanced MSCT, is significantly associated with DS and PVL, but not with 30-day MACE. Its routine use could be relevant to appreciate success chances of TAVR.

Automated segmentation and quantification of aortic calcification at abdominal CT: application of a deep learning-based algorithm to a longitudinal screening cohort

OBJECTIVE

To investigate an automated aortic calcium segmentation and scoring tool at abdominal CT in an adult screening cohort.

METHODS

Using instance segmentation with convolutional neural networks (Mask R-CNN), a fully automated vascular calcification algorithm was applied to a data set of 9914 non-contrast CT scans from 9032 consecutive asymptomatic adults (mean age, 57.5 ± 7.8 years; 4467 M/5447F) undergoing colonography screening. Follow-up scans were performed in a subset of 866 individuals (mean interval, 5.4 years). Automated abdominal aortic calcium volume, mass, and Agatston score were assessed. In addition, comparison was made with a separate validated semi-automated approach in a subset of 812 cases.

RESULTS

Mean values were significantly higher in males for Agatston score (924.2 ± 2066.2 vs. 564.2 ± 1484.2, p < 0.001), aortic calcium mass (222.2 ± 526.0 mg vs. 144.5 ± 405.4 mg, p < 0.001) and volume (699.4 ± 1552.4 ml vs. 426.9 ± 1115.5 HU, p < 0.001). Overall age-specific Agatston scores increased an average of 10%/year for the entire cohort; males had a larger Agatston score increase between the ages of 40 to 60 than females (91.2% vs. 75.1%, p < 0.001) and had significantly higher mean Agatston scores between ages 50 and 80 (p < 0.001). For the 812-scan subset with both automated and semi-automated methods, median difference in Agatston score was 66.4 with an r² agreement value of 0.84. Among the 866-patient cohort with longitudinal follow-up, the average Agatston score change was 524.1 ± 1317.5 (median 130.9), reflecting a mean increase of 25.5% (median 73.6%).

CONCLUSION

This robust, fully automated abdominal aortic calcification scoring tool allows for both individualized and population-based assessment. Such data could be automatically derived at non-contrast abdominal CT, regardless of the study indication, allowing for opportunistic assessment of cardiovascular risk.

Aortic Root Calcification Score as an Independent Factor for Predicting Major Adverse Cardiac Events in Familial Hypercholesterolemia

Aim: The aims of this study were: 1) to determine whether the accumulation of aortic root calcification (ARC) assessed using coronary computed tomography angiography (CCTA) can predict future cardiovascular events, and 2) to estimate the onset and progression of ARC in patients with familial hypercholesterolemia (FH).

Methods: One hundred thirteen consecutive Japanese patients with heterozygous FH (male = 54, mean age = 52.1 ± 15.6 years, mean LDL-C = 299.0 ± 94.6 mg/dL), without known coronary artery disease, who underwent 64-detector row CCTA were retrospectively evaluated. ARC was defined as the presence of calcium at the aortic root. The extent of ARC was expressed in Agatston units as the ARC-score. Major adverse cardiac events (MACE) were defined as either cardiac death, ST elevated myocardial infarction (STEMI), non-ST elevated myocardial infarction (NSTEMI), unstable angina pectoris (UAP), planned percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), or stroke. The periods to MACE were estimated using multivariate logistic regression analysis.

Results: During the follow-up period (median 1635 days), 19 instances of MACE occurred. Multivariate logistic regression analysis revealed that ARC was a significant independent predictor of MACE (OR= 1.48; 95% CI 1.11–1.87, p < 0.001, respectively). The regression equations were Y= 0.09X − 1.59 (R² = 0.34, p < 0.001) in males and Y = 0.08X − 1.60 (R² = 0.13, p < 0. 05) in females.

Conclusions: ARC was significantly associated with future MACE in Japanese patients with heterozygous FH. ARC may start to develop, on average, at 17.4 and 19.7 years of age in males and females, respectively, with heterozygous FH.