ABSTRACT
The bicuspid aortic valve (BAV) is the most common cardiovascular congenital abnormality and is frequently associated with proximal aortopathy. We analyzed the tissues of patients with bicuspid and tricuspid aortic valve (TAV) regarding the protein expression of the receptor for advanced glycation products (RAGE) and its ligands, the advanced glycation end products (AGE), as well as the S100 calcium-binding protein A6 (S100A6). Since S100A6 overexpression attenuates cardiomyocyte apoptosis, we investigated the diverse pathways of apoptosis and autophagic cell death in the human ascending aortic specimen of 57 and 49 patients with BAV and TAV morphology, respectively, to identify differences and explanations for the higher risk of patients with BAV for severe cardiovascular diseases. We found significantly increased levels of RAGE, AGE and S100A6 in the aortic tissue of bicuspid patients which may promote apoptosis via the upregulation of caspase-3 activity. Although increased caspase-3 activity was not detected in BAV patients, increased protein expression of the 48 kDa fragment of vimentin was detected. mTOR as a downstream protein of Akt was significantly higher in patients with BAV, whereas Bcl-2 was increased in patients with TAV, assuming a better protection against apoptosis. The autophagy-related proteins p62 and ERK1/2 were increased in patients with BAV, assuming that cells in bicuspid tissue are more likely to undergo apoptotic cell death leading to changes in the wall and finally to aortopathies. We provide first-hand evidence of increased apoptotic cell death in the aortic tissue of BAV patients which may thus provide an explanation for the increased risk of structural aortic wall deficiency possibly underlying aortic aneurysm formation or acute dissection.
Subject(s)
Bicuspid Aortic Valve Disease , Heart Valve Diseases , Humans , Heart Valve Diseases/metabolism , Caspase 3/metabolism , Aortic Valve/metabolism , Apoptosis/physiologyABSTRACT
Objective: To compare machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) in patients before transcatheter aortic valve replacement (TAVR) by observers with differing training and to assess influencing factors. Background: Coronary computed tomography angiography (cCTA) can effectively exclude CAD, e.g. prior to TAVR, but remains limited by its specificity. CT-FFR may mitigate this limitation also in patients prior to TAVR. While a high reliability of CT-FFR is presumed, little is known about the reproducibility of ML-based CT-FFR. Methods: Consecutive patients with obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers. Categorization into hemodynamically significant CAD was compared against invasive coronary angiography. The influence of image quality and coronary artery calcium score (CAC) was examined. Results: CT-FFR was successfully performed on 214/272 examinations by both observers. The median difference of CT-FFR between both observers was -0.05(-0.12-0.02) (p < 0.001). Differences showed an inverse correlation to the absolute CT-FFR values. Categorization into CAD was different in 37/214 examinations, resulting in net recategorization of Δ13 (13/214) examinations and a difference in accuracy of Δ6.1%. On patient level, correlation of absolute and categorized values was substantial (0.567 and 0.570, p < 0.001). Categorization into CAD showed no correlation to image quality or CAC (p > 0.13). Conclusion: Differences between CT-FFR values increased in values below the cut-off, having little clinical impact. Categorization into CAD differed in several patients, but ultimately only had a moderate influence on diagnostic accuracy. This was independent of image quality or CAC.
ABSTRACT
Background: Coronary artery disease (CAD) is a frequent comorbidity in patients undergoing transcatheter aortic valve implantation (TAVI). If significant CAD can be excluded on coronary CT-angiography (cCTA), invasive coronary angiography (ICA) may be avoided. However, a high plaque burden may make the exclusion of CAD challenging, particularly for less experienced readers. The objective was to analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to correctly categorize cCTA studies without obstructive CAD acquired during pre-TAVI evaluation and to correlate recategorization to image quality and coronary artery calcium score (CAC). Methods: In total, 116 patients without significant stenosis (≥50% diameter) on cCTA as part of pre-TAVI CT were included. Patients were examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the torso. Patients were re-evaluated with ML-based CT-FFR (threshold = 0.80). The standard of reference was ICA. Image quality was assessed quantitatively and qualitatively. Results: ML-based CT-FFR was successfully performed in 94.0% (109/116) of patients, including 436 vessels. With CT-FFR, 76/109 patients and 126/436 vessels were falsely categorized as having significant CAD. With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. Reclassification occurred predominantly in distal segments. Virtually no correlation was found between image quality or CAC. Conclusions: Unselectively applied, CT-FFR may vastly increase the number of false positive ratings of CAD compared to morphological scoring. Recategorization was virtually independently from image quality or CAC and occurred predominantly in distal segments. It is unclear whether or not the reduced CT-FFR represent true pressure ratios and potentially signifies pathophysiology in patients with severe aortic stenosis.
ABSTRACT
BACKGROUND: Transcatheter mitral valve replacement (TMVR) is a treatment option for patients with therapy refractory high-grade mitral valve regurgitation and a high perioperative risk.During TMVR, the mitral annulus cannot be visualized directly. Therefore, comprehensive pre-interventional planning and a precise visualization of the patient's specific mitral valve anatomy, outflow tract anatomy and projected anchoring of the device are necessary.Aim of this review-article is, to assess the role of pre-procedural computed tomography (CT) for TMVR-planning METHODS: Screening and evaluation of relevant guidelines (European Society of Cardiology [ESC], American Heart Association [AHA/ACC]), meta-analyses and original research using the search terms "TVMR" or "TMVI" and "CT". In addition to this, the authors included insight from their own clinical experience. RESULTS: CT allows for accurate measurement of the mitral annulus with high special and adequate temporal resolution in all cardiac phases. Therefore, CT represents a valuable method for accurate prosthesis-sizing.In addition to that, CT can provide information about the valvular- and outflow-tract-anatomy, mitral valve calcifications, configuration of the papillary muscles and of the left ventricle. Additionally, the interventional access-route may concomitantly be visualized. CONCLUSION: CT plays, in addition to echocardiographic imaging, a central role in pre-interventional assessment prior to TMVR. Especially the precise depiction of the left ventricular outflow tract (LVOT) provides relevant additional information, which is very difficult or not possible to be acquired in their entirety with other imaging modalities. KEY POINTS: · CT plays a central role in pre-interventional imaging for TMVR.. · CT-measurements allow for accurate prosthesis-sizing.. · CT provides valuable information about LVOT-anatomy, mitral calcifications and interventional access-route.. CITATION FORMAT: · Heiser L, Gohmann RF, Noack T etâal. CT Planning prior to Transcatheter Mitral Valve Replacement (TMVR). Fortschr Röntgenstr 2022; 194: 373â-â383.
Subject(s)
Heart Valve Prosthesis Implantation , Heart Valve Prosthesis , Mitral Valve Insufficiency , Ventricular Outflow Obstruction , Cardiac Catheterization/methods , Heart Valve Prosthesis Implantation/methods , Humans , Mitral Valve/diagnostic imaging , Mitral Valve/surgery , Mitral Valve Insufficiency/diagnostic imaging , Mitral Valve Insufficiency/surgery , Tomography, X-Ray Computed , Treatment Outcome , Ventricular Outflow Obstruction/prevention & control , Ventricular Outflow Obstruction/surgeryABSTRACT
OBJECTIVES: The purpose of this study was to analyze the ability of machine-learning (ML)-based computed tomography (CT)-derived fractional flow reserve (CT-FFR) to further improve the diagnostic performance of coronary CT angiography (cCTA) for ruling out significant coronary artery disease (CAD) during pre-transcatheter aortic valve replacement (TAVR) evaluation in patients with a high pre-test probability for CAD. BACKGROUND: CAD is a frequent comorbidity in patients undergoing TAVR. Current guidelines recommend its assessment before TAVR. If significant CAD can be excluded on cCTA, invasive coronary angiography (ICA) may be avoided. Although cCTA is a very sensitive test, it is limited by relatively low specificity and positive predictive value, particularly in high-risk patients. METHODS: Overall, 460 patients (age 79.6 ± 7.4 years) undergoing pre-TAVR CT were included and examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the vascular access route. Images were evaluated for significant CAD. Patients routinely underwent ICA (388/460), which was omitted at the discretion of the local Heart Team if CAD could be effectively ruled out on cCTA (72/460). CT examinations in which CAD could not be ruled out (CAD+) (n = 272) underwent additional ML-based CT-FFR. RESULTS: ML-based CT-FFR was successfully performed in 79.4% (216/272) of all CAD+ patients and correctly reclassified 17 patients as CAD negative. CT-FFR was not feasible in 20.6% because of reduced image quality (37/56) or anatomic variants (19/56). Sensitivity, specificity, positive predictive value, and negative predictive value were 94.9%, 52.0%, 52.2%, and 94.9%, respectively. The additional evaluation with ML-based CT-FFR increased accuracy by Δ+3.4% (CAD+: Δ+6.0%) and raised the total number of examinations negative for CAD to 43.9% (202/460). CONCLUSIONS: ML-based CT-FFR may further improve the diagnostic performance of cCTA by correctly reclassifying a considerable proportion of patients with morphological signs of obstructive CAD on cCTA during pre-TAVR evaluation. Thereby, CT-FFR has the potential to further reduce the need for ICA in this challenging elderly group of patients before TAVR.